{"id":7510,"date":"2025-07-18T23:35:35","date_gmt":"2025-07-18T21:35:35","guid":{"rendered":"https:\/\/cubert-hyperspectral.com\/?p=7510"},"modified":"2026-10-04T18:10:43","modified_gmt":"2026-10-04T16:10:43","slug":"ki-unterstuetzte-anomalieerkennung-in-der-industrie","status":"publish","type":"post","link":"https:\/\/cubert-hyperspectral.com\/de\/wissen\/ki-unterstuetzte-anomalieerkennung-in-der-industrie\/","title":{"rendered":"KI-unterst\u00fctzte Anomalieerkennung in der Industrie\u00a0"},"content":{"rendered":"<style>.kadence-column7510_c62fc5-59 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7510_c62fc5-59 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7510_c62fc5-59 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7510_c62fc5-59 > .kt-inside-inner-col{background-color:rgba(0, 0, 0, 0);}.kadence-column7510_c62fc5-59 > .kt-inside-inner-col:before{opacity:0.85;background-color:var(--global-palette7, #EDF2F7);}.kadence-column7510_c62fc5-59{position:relative;}@media all and (max-width: 1024px){.kadence-column7510_c62fc5-59 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7510_c62fc5-59 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7510_c62fc5-59 kb-section-has-overlay\"><div class=\"kt-inside-inner-col\"><style>.kb-row-layout-id7510_2ad02a-91 > .kt-row-column-wrap{align-content:center;}:where(.kb-row-layout-id7510_2ad02a-91 > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:center;}.kb-row-layout-id7510_2ad02a-91 > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-md, 2rem);max-width:var( --global-content-width, 1490px );padding-left:var(--global-content-edge-padding);padding-right:var(--global-content-edge-padding);padding-top:var(--global-kb-spacing-3xl, 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kb-theme-content-width\">\n<style>.kadence-column7510_144034-cf > .kt-inside-inner-col{display:flex;}.kadence-column7510_144034-cf > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7510_144034-cf > .kt-inside-inner-col > .aligncenter{width:100%;}.kt-row-column-wrap > .kadence-column7510_144034-cf{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7510_144034-cf > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7510_144034-cf{z-index:10;position:relative;}.kadence-column7510_144034-cf, .kt-inside-inner-col > .kadence-column7510_144034-cf:not(.specificity){margin-right:0px;}@media all and (max-width: 1024px){.kt-row-column-wrap > .kadence-column7510_144034-cf{align-self:center;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf{align-self:auto;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 1024px){.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kt-row-column-wrap > .kadence-column7510_144034-cf{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7510_144034-cf > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7510_144034-cf\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedbtn.kb-btns7510_a7772e-4a{padding-bottom:var(--global-kb-spacing-md, 2rem);gap:var(--global-kb-gap-xs, 0.5rem );justify-content:flex-start;align-items:center;}.kt-btns7510_a7772e-4a .kt-button{font-weight:normal;font-style:normal;}.kt-btns7510_a7772e-4a .kt-btn-wrap-0{margin-right:5px;}.wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button{color:#555555;border-color:#555555;}.wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button:focus{color:#ffffff;border-color:#444444;}.wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button::before{display:none;}.wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns7510_a7772e-4a .kt-btn-wrap-0 .kt-button:focus{background:#444444;}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns7510_a7772e-4a\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn7510_9c1264-1c.kb-button{width:initial;}<\/style><span class=\"kb-button kt-button button kb-btn7510_9c1264-1c kt-btn-size-standard kt-btn-width-type-auto kb-btn-global-outline kt-btn-has-text-true kt-btn-has-svg-false wp-block-kadence-singlebtn kb-preset--outline\"><span class=\"kt-btn-inner-text\">Science<\/span><\/span>\n\n<style>ul.menu .wp-block-kadence-advancedbtn .kb-btn7510_762a31-c4.kb-button{width:initial;}<\/style><span class=\"kb-button kt-button button kb-btn7510_762a31-c4 kt-btn-size-standard kt-btn-width-type-auto kb-btn-global-outline kt-btn-has-text-true kt-btn-has-svg-false wp-block-kadence-singlebtn kb-preset--outline\"><span class=\"kt-btn-inner-text\">Technology<\/span><\/span><\/div>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c, .wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c[data-kb-block=\"kb-adv-heading7510_41839e-6c\"]{padding-top:0px;margin-top:0px;font-size:60px;line-height:68px;font-style:normal;text-shadow:2px 2px 5px rgba(0, 0, 0, 0.2);}.wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c[data-kb-block=\"kb-adv-heading7510_41839e-6c\"] mark.kt-highlight{font-style:normal;color:var(--global-palette8, #F7FAFC);-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c[data-kb-block=\"kb-adv-heading7510_41839e-6c\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c, .wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c[data-kb-block=\"kb-adv-heading7510_41839e-6c\"]{text-shadow:2px 2px 5px rgba(0, 0, 0, 0.2);font-size:40px;line-height:48px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c, .wp-block-kadence-advancedheading.kt-adv-heading7510_41839e-6c[data-kb-block=\"kb-adv-heading7510_41839e-6c\"]{text-shadow:2px 2px 5px rgba(0, 0, 0, 0.2);font-size:30px;line-height:40px;}}<\/style>\n<h1 class=\"kt-adv-heading7510_41839e-6c wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_41839e-6c\">Automatisierte <mark true=\"true\" class=\"kt-highlight\">Anomalieerkennung<\/mark> in industriellen Prozessen<\/h1>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6, .wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6[data-kb-block=\"kb-adv-heading7510_0b9c7d-c6\"]{max-width:700px;font-size:28px;line-height:36px;font-weight:100;font-style:normal;font-family:Roboto;}.wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6[data-kb-block=\"kb-adv-heading7510_0b9c7d-c6\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_0b9c7d-c6[data-kb-block=\"kb-adv-heading7510_0b9c7d-c6\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading7510_0b9c7d-c6 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_0b9c7d-c6\">Wie hyperspektrale Bildgebung und KI bei der automatisierten Anomalieerkennung eingesetzt werden.<\/p>\n\n\n<style>.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2:not(.this-stops-third-party-issues){margin-top:0px;margin-bottom:0px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list:not(.this-prevents-issues):not(.this-stops-third-party-issues):not(.tijsloc){margin-top:0px;margin-right:0px;margin-bottom:var(--global-kb-spacing-sm, 1.5rem);margin-left:0px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list{grid-row-gap:5px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 .kb-svg-icon-wrap{font-size:25px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list .kt-svg-icon-list-item-wrap .kt-svg-icon-list-single{margin-right:15px;}.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list .kt-svg-icon-list-item-wrap, .kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list .kt-svg-icon-list-item-wrap a{font-size:18px;line-height:30px;letter-spacing:0.5px;}.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list .kt-svg-icon-list-level-0 .kt-svg-icon-list-single svg{font-size:20px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 .wp-block-kadence-listitem a{text-decoration:underline;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7510_23e9a1-b2 ul.kt-svg-icon-list .wp-block-kadence-listitem a{color:var(--global-palette8, #F7FAFC);}<\/style>\n<div class=\"wp-block-kadence-iconlist kt-svg-icon-list-items kt-svg-icon-list-items7510_23e9a1-b2 kt-svg-icon-list-columns-1 alignnone kt-list-icon-aligntop\"><ul class=\"kt-svg-icon-list\"><style>.kt-svg-icon-list-item-7510_d3aa05-3c .kt-svg-icon-list-text mark.kt-highlight{background-color:unset;font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<li class=\"wp-block-kadence-listitem kt-svg-icon-list-item-wrap kt-svg-icon-list-item-7510_d3aa05-3c\"><span class=\"kb-svg-icon-wrap kb-svg-icon-kb-custom-9483 kt-svg-icon-list-single\"><svg viewBox=\"0 0 30 24\"  fill=\"currentColor\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"  aria-hidden=\"true\"><defs ><style \/><\/defs><path class=\"st0\" d=\"M26,0H4C1.8,0,0,1.8,0,4v16c0,2.2,1.8,4,4,4h22c2.2,0,4-1.8,4-4V4c0-2.2-1.8-4-4-4ZM10,10c0-2.7,2.3-5,5-5s5,2.2,5,5-2.3,5-5,5-5-2.2-5-5ZM15,17c4.1,0,7.6,2.1,8.6,5H6.3c1.1-2.9,4.6-5,8.6-5ZM28,20c0,1.1-.9,2-2,2h-.3c-.8-2.9-3.3-5.3-6.7-6.4,1.8-1.3,3-3.3,3-5.7,0-3.8-3.2-7-7-7s-7,3.1-7,7,1.2,4.4,3,5.7c-3.3,1.1-5.9,3.4-6.7,6.4h-.2c-1.1,0-2-.9-2-2V4c0-1.1.9-2,2-2h22c1.1,0,2,.9,2,2v16Z\"\/><\/svg><\/span><span class=\"kt-svg-icon-list-text\">Autor: <a href=\"#author\" title=\"\">Dr. Matthias Locherer<\/a><\/span><\/li>\n\n\n<style>.kt-svg-icon-list-item-7510_a162b5-79 .kt-svg-icon-list-text mark.kt-highlight{background-color:unset;font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<li class=\"wp-block-kadence-listitem kt-svg-icon-list-item-wrap kt-svg-icon-list-item-7510_a162b5-79\"><span class=\"kb-svg-icon-wrap kb-svg-icon-kb-custom-9484 kt-svg-icon-list-single\"><svg viewBox=\"0 0 30 24\"  fill=\"currentColor\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"  aria-hidden=\"true\"><defs ><style \/><\/defs><path class=\"st0\" d=\"M26,24H4c-2.2,0-4-1.8-4-4V4C0,1.8,1.8,0,4,0h22c2.2,0,4,1.8,4,4v16c0,2.2-1.8,4-4,4ZM4,2c-1.1,0-2,.9-2,2v16c0,1.1.9,2,2,2h22c1.1,0,2-.9,2-2V4c0-1.1-.9-2-2-2H4Z\"\/><polygon class=\"st0\" points=\"15.4 13 8 13 8 11 14.6 11 18.3 7.3 19.7 8.7 15.4 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Oktober 2026<\/span><\/li>\n<\/ul><\/div>\n<\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n\n<style>.kadence-column7510_054dc1-b5 > .kt-inside-inner-col{padding-top:var(--global-kb-spacing-3xl, 6.5rem);}.kadence-column7510_054dc1-b5 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7510_054dc1-b5 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7510_054dc1-b5 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7510_054dc1-b5 > .kt-inside-inner-col:before{opacity:0.9;background-color:var(--global-palette9, #ffffff);}.kadence-column7510_054dc1-b5, .kadence-column7510_054dc1-b5 h1, .kadence-column7510_054dc1-b5 h2, .kadence-column7510_054dc1-b5 h3, .kadence-column7510_054dc1-b5 h4, .kadence-column7510_054dc1-b5 h5, .kadence-column7510_054dc1-b5 h6{color:var(--global-palette4, #2D3748);}.kadence-column7510_054dc1-b5 a{color:var(--global-palette4, #2D3748);}.kadence-column7510_054dc1-b5 a:hover{color:var(--global-palette3, 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1rem);}.kadence-column7510_e43c4a-02 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7510_e43c4a-02 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7510_e43c4a-02 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7510_e43c4a-02{position:relative;}@media all and (max-width: 1024px){.kadence-column7510_e43c4a-02 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7510_e43c4a-02 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7510_e43c4a-02 kb-section-is-sticky\"><div class=\"kt-inside-inner-col\"><style>.kb-table-of-content-nav.kb-table-of-content-id11687_25a3fb-1f .kb-table-of-content-wrap{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id11687_25a3fb-1f 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.kb-table-of-contents__entry{color:#be2800;}@media all and (max-width: 767px){.kb-table-of-content-nav.kb-table-of-content-id11687_25a3fb-1f .kb-table-of-contents-title{font-size:20px;}}<\/style><\/div><\/div>\n\n\n<style>.kadence-column7510_5e85f6-ff > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7510_5e85f6-ff > .kt-inside-inner-col{flex-direction:column;}.kadence-column7510_5e85f6-ff > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7510_5e85f6-ff > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7510_5e85f6-ff a{color:#e64600;}.kadence-column7510_5e85f6-ff a:hover{color:var(--global-palette13, #3182CE);}.kadence-column7510_5e85f6-ff{position:relative;}.kadence-column7510_5e85f6-ff, .kt-inside-inner-col > .kadence-column7510_5e85f6-ff:not(.specificity){margin-top:-7px;}@media all and (max-width: 1024px){.kadence-column7510_5e85f6-ff > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7510_5e85f6-ff > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7510_5e85f6-ff\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f, .wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f[data-kb-block=\"kb-adv-heading7510_6aa80b-6f\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f[data-kb-block=\"kb-adv-heading7510_6aa80b-6f\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f[data-kb-block=\"kb-adv-heading7510_6aa80b-6f\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f, .wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f[data-kb-block=\"kb-adv-heading7510_6aa80b-6f\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f, .wp-block-kadence-advancedheading.kt-adv-heading7510_6aa80b-6f[data-kb-block=\"kb-adv-heading7510_6aa80b-6f\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7510_6aa80b-6f H_overline wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7510_6aa80b-6f\">Anwendungsfall<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Die zuverl\u00e4ssige Identifizierung und Trennung von Kunststoffen ist ein zentraler Bestandteil moderner Recyclingsysteme. PVC (Polyvinylchlorid) stellt aufgrund seiner spektralen Variabilit\u00e4t eine besondere Herausforderung dar, die durch Pigmente, Additive, thermische Alterung und die MaterialIn diesem Use Case geht es um die automatisierte visuelle Pr\u00fcfung von Produkten innerhalb industrieller Fertigungsprozesse. Ziel ist die Detektion von Fehlstellungen wie etwa Feuchtigkeit in trockenen Materialien, Verunreinigungen oder mangelhafter Durchmischung. Dabei erfasst eine Kamera vollfl\u00e4chige Aufnahmen der Produkte, und ein darauf <a href=\"https:\/\/github.com\/cubert-hyperspectral\/cuvis.ai.examples\/tree\/main\/EfficientAD\">trainierter Algorithmus<\/a> bewertet, ob die Aufnahme dem erwarteten Zustand entspricht oder auf eine Anomalie hinweist.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ein zentrales Problem industrieller Umgebungen liegt in deren inh\u00e4renter Komplexit\u00e4t: Nicht alle potenziellen Fehlerbilder sind im Vorfeld eindeutig definierbar. Umso wichtiger ist die F\u00e4higkeit, auch bislang unbekannte Anomalien erkennen zu k\u00f6nnen.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Da Zeit in den meisten Produktionsprozessen eine kritische Ressource darstellt, lag der Fokus dieses Projekts auf der Entwicklung einer L\u00f6sung, die mit minimalem Erfassungs- und Auswertungsaufwand pr\u00e4zise Ergebnisse liefert \u2013 in Echtzeit und bei hoher Fl\u00e4chendeckung.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Die hier vorgestellte L\u00f6sung ist in der Lage, Anomalien in der Gr\u00f6\u00dfe einer Erbse auf einer Fl\u00e4che von 60x30cm zuverl\u00e4ssig zu detektieren \u2013 basierend auf nur einer einzigen Aufnahme pro Produkt. Im gezeigten Demonstrator wurden S\u00e4gesp\u00e4ne hinsichtlich unerw\u00fcnschter Materialien und Feuchtigkeitsstellen untersucht. W\u00e4hrend viele Fremdk\u00f6rper bereits im sichtbaren Spektralbereich detektierbar sind, bleibt Wasser dort in der Regel unsichtbar. In diesem Fall lassen sich feuchte Stellen durch den Einsatz von Infrarotlicht \u2013 insbesondere im SWIR-Bereich \u2013&nbsp; deutlich hervorheben und f\u00fcr die maschinelle Auswertung zug\u00e4nglich machen.<\/p>\n\n\n<style>.kb-image7510_af4cb4-f9 .kb-image-has-overlay:after{opacity:0.3;}.kb-image7510_af4cb4-f9 img.kb-img, .kb-image7510_af4cb4-f9 .kb-img img{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7510_af4cb4-f9 size-full\"><a href=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/Cubert-2.png\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"322\" height=\"434\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/Cubert-2.png\" alt=\"Cubert (2)\" class=\"kb-img wp-image-7512\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/Cubert-2.png 322w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/Cubert-2-223x300.png 223w\" sizes=\"auto, (max-width: 322px) 100vw, 322px\" \/><\/a><\/figure>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab, .wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab[data-kb-block=\"kb-adv-heading7510_66151b-ab\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab[data-kb-block=\"kb-adv-heading7510_66151b-ab\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab[data-kb-block=\"kb-adv-heading7510_66151b-ab\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab, .wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab[data-kb-block=\"kb-adv-heading7510_66151b-ab\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab, .wp-block-kadence-advancedheading.kt-adv-heading7510_66151b-ab[data-kb-block=\"kb-adv-heading7510_66151b-ab\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7510_66151b-ab H_overline wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7510_66151b-ab\">Komplexit\u00e4t erfordert Deep-Learning-ANSATZ<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00fcr klassische Bildverarbeitung ist die vorliegende Aufgabe zu komplex: Produktionsprozesse sind variabel, Trainingsdaten zu Fehlerbildern oft unvollst\u00e4ndig, Abweichungen vielf\u00e4ltig.<br>Neuronale Netze, insbesondere Deep-Learning-Modelle, sind hier die L\u00f6sung: Sie lernen die Merkmale des Normalzustands aus fehlerfreien Beispielen und identifizieren sp\u00e4ter selbst bislang unbekannte Anomalien.<br>Im Trainingsprozess durchl\u00e4uft das Netzwerk die Daten wiederholt, erfasst Muster und Abweichungen im hochdimensionalen Raum. Diese F\u00e4higkeit zur Generalisierung macht Deep Learning zur idealen Methode f\u00fcr Unternehmen, die in dynamischen Anlagen eine verl\u00e4ssliche Qualit\u00e4tssicherung sicherstellen m\u00f6chten.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80, .wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80[data-kb-block=\"kb-adv-heading7510_f0b449-80\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80[data-kb-block=\"kb-adv-heading7510_f0b449-80\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80[data-kb-block=\"kb-adv-heading7510_f0b449-80\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80, .wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80[data-kb-block=\"kb-adv-heading7510_f0b449-80\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80, .wp-block-kadence-advancedheading.kt-adv-heading7510_f0b449-80[data-kb-block=\"kb-adv-heading7510_f0b449-80\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7510_f0b449-80 H_overline wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7510_f0b449-80\">Warum SWIR? \u2013 Spektrale Vorteile im kurzwelligen Infrarotbereich<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Das SWIR-Verfahren nutzt die Absorptionseigenschaften von Wasser im kurzwelligen Infrarotbereich: Feuchte Zonen erscheinen hier deutlich kontrastreicher als in klassischen Farbbildern. Dadurch k\u00f6nnen St\u00f6rungen in der Produktion, die optisch kaum erkennbar sind, zuverl\u00e4ssig erkannt werden.<br>Die Kombination von spektralen Daten aus mehreren Wellenl\u00e4ngen mit Machine Learning f\u00fchrt zu einer robusten und effizienten Anomalieerkennung \u2013 ein Vorteil, der klassische Verfahren in puncto Pr\u00e4zision und Effizienz weit \u00fcbertrifft.<\/p>\n\n\n<style>.kb-image7510_706c0e-eb .kb-image-has-overlay:after{opacity:0.3;}.kb-image7510_706c0e-eb img.kb-img, .kb-image7510_706c0e-eb .kb-img img{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7510_706c0e-eb size-full\"><a href=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/swir.png\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"846\" height=\"214\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/swir.png\" alt=\"Swir\" class=\"kb-img wp-image-7514\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/swir.png 846w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/swir-300x76.png 300w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/swir-768x194.png 768w\" sizes=\"auto, (max-width: 846px) 100vw, 846px\" \/><\/a><\/figure>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25, .wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25[data-kb-block=\"kb-adv-heading7510_bc8bcc-25\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25[data-kb-block=\"kb-adv-heading7510_bc8bcc-25\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25[data-kb-block=\"kb-adv-heading7510_bc8bcc-25\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25, .wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25[data-kb-block=\"kb-adv-heading7510_bc8bcc-25\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25, .wp-block-kadence-advancedheading.kt-adv-heading7510_bc8bcc-25[data-kb-block=\"kb-adv-heading7510_bc8bcc-25\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7510_bc8bcc-25 H_overline wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7510_bc8bcc-25\">Kamera-Set-up: Pr\u00e4zise Datenerfassung<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">F\u00fcr die Datenerfassung kam ein multispektrales Kamerasystem zum Einsatz: eine hochaufl\u00f6sende Farbkamera kombiniert mit SWIR-Sensoren, die auf spezifische Spektralb\u00e4nder im Infrarotbereich abgestimmt sind. Diese Konfiguration liefert konsistente Datenw\u00fcrfel, die f\u00fcr jedes Pixel Informationen \u00fcber mehrere Wellenl\u00e4ngenbereiche enthalten. Die pr\u00e4zise Kalibrierung und Synchronisierung der Sensoren gew\u00e4hrleisten eine verl\u00e4ssliche Bildqualit\u00e4t f\u00fcr das Training der Modelle.<\/p>\n\n\n<style>.kb-image7510_84ac0c-47 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7510_84ac0c-47 size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"225\" height=\"300\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/deep-learning-225x300.png\" alt=\"Deep learning\" class=\"kb-img wp-image-7516\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/deep-learning-225x300.png 225w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/deep-learning.png 336w\" sizes=\"auto, (max-width: 225px) 100vw, 225px\" \/><\/figure>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f, .wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f[data-kb-block=\"kb-adv-heading7510_a8d141-2f\"]{font-size:var(--global-kb-font-size-lg, 2rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f[data-kb-block=\"kb-adv-heading7510_a8d141-2f\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_a8d141-2f[data-kb-block=\"kb-adv-heading7510_a8d141-2f\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h2 class=\"kt-adv-heading7510_a8d141-2f wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_a8d141-2f\">ML Network: Deep Learning f\u00fcr nicht deterministische Szenarien<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">fficientAD, eine spezialisierte Autoencoder-Architektur, wurde f\u00fcr diesen Anwendungsfall trainiert. Der Vorteil: Es gen\u00fcgt ein Datensatz fehlerfreier Trainingsdaten, um den Normalzustand der Produktionsumgebung zu lernen. Abweichungen von diesem Muster werden sp\u00e4ter zuverl\u00e4ssig als potenzielle Anomalien erkannt.<br>Diese Methode der Anomaliedetektion reduziert die Notwendigkeit umfassender Fehlerkataloge und eignet sich daher ideal f\u00fcr Unternehmen mit heterogenen Prozessen und variablen Oberfl\u00e4chenstrukturen.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"803\" height=\"197\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert.png\" alt=\"Cubert\" class=\"wp-image-7526\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert.png 803w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert-300x74.png 300w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert-768x188.png 768w\" sizes=\"auto, (max-width: 803px) 100vw, 803px\" \/><\/figure>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d, .wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d[data-kb-block=\"kb-adv-heading7510_5d42db-3d\"]{font-size:var(--global-kb-font-size-lg, 2rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d[data-kb-block=\"kb-adv-heading7510_5d42db-3d\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_5d42db-3d[data-kb-block=\"kb-adv-heading7510_5d42db-3d\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h2 class=\"kt-adv-heading7510_5d42db-3d wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_5d42db-3d\">Cuvis.AI: Plattform f\u00fcr hyperspektrale Anomalieerkennung<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Cubert bietet mit Cuvis.AI eine Plattform, die speziell f\u00fcr die Entwicklung, \u00dcberwachung und Inferenz von Anomaliedetektion auf Basis hyperspektraler Daten entwickelt wurde. Sie erlaubt es, Machine-Learning-Modelle effizient mit spektralen Bildern zu trainieren und in industrielle Produktionslinien zu integrieren \u2013 modular, skalierbar, zukunftsf\u00e4hig.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc, .wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc[data-kb-block=\"kb-adv-heading7510_af418b-bc\"]{font-size:var(--global-kb-font-size-lg, 2rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc[data-kb-block=\"kb-adv-heading7510_af418b-bc\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_af418b-bc[data-kb-block=\"kb-adv-heading7510_af418b-bc\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h2 class=\"kt-adv-heading7510_af418b-bc wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_af418b-bc\">Datensatz: Hohe Variabilit\u00e4t als Grundlage<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Der verwendete Datensatz bestand aus multispektralen Aufnahmen von S\u00e4gesp\u00e4nen mit unterschiedlichsten Texturen, Farben und spektralen Eigenschaften. Diese hohe Varianz stellt sicher, dass das trainierte Modell auch in realen Produktionsumgebungen mit komplexen Oberfl\u00e4chen zuverl\u00e4ssig Abweichungen erkennen kann.<br>Die pr\u00e4zise Segmentierung der Bilder erm\u00f6glicht es, die Leistung der Anomaliedetektion objektiv zu evaluieren und kontinuierlich weiterzuentwickeln.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8, .wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8[data-kb-block=\"kb-adv-heading7510_c0d335-c8\"]{font-size:var(--global-kb-font-size-lg, 2rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8[data-kb-block=\"kb-adv-heading7510_c0d335-c8\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7510_c0d335-c8[data-kb-block=\"kb-adv-heading7510_c0d335-c8\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<h2 class=\"kt-adv-heading7510_c0d335-c8 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7510_c0d335-c8\">Fazit: Intelligente Qualit\u00e4tssicherung f\u00fcr moderne Produktion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Durch die Kombination von hyperspektraler Bildgebung, spektraler Vielfalt \u00fcber mehrere Wellenl\u00e4ngen, modernsten Machine-Learning-Methoden und einer robusten Plattform hat Cubert eine L\u00f6sung entwickelt, die Effizienz, Pr\u00e4zision und Automatisierung in der Qualit\u00e4tssicherung auf ein neues Niveau hebt.<br>F\u00fcr Unternehmen, die in Industrie 4.0-Prozesse investieren, wird die automatisierte Anomalieerkennung zum strategischen Instrument: St\u00f6rungen werden fr\u00fch erkannt, Anlagen besser \u00fcberwacht, Maschinen effizienter betrieben.<\/p>\n\n\n<style>.kb-image7510_bb8d63-72 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7510_bb8d63-72 size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"667\" height=\"852\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert2.png\" alt=\"Cubert2\" class=\"kb-img wp-image-7528\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert2.png 667w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/cubert2-235x300.png 235w\" sizes=\"auto, (max-width: 667px) 100vw, 667px\" \/><\/figure>\n\n\n<style>.kb-image7510_39a325-fe .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7510_39a325-fe size-full\"><a href=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2026\/03\/Graphic3.png\" class=\"kb-advanced-image-link\"><img loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"255\" src=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2026\/03\/Graphic3.png\" alt=\"Graphic3\" class=\"kb-img wp-image-8687\" srcset=\"https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2026\/03\/Graphic3.png 512w, https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2026\/03\/Graphic3-300x149.png 300w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \/><\/a><figcaption>Abb.3 Beispiel einer spektralen Signatur von Kunststofftypen, aufgenommen mit der <strong>ULTRIS SWIR1<\/strong>.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Kurzum: Mit intelligenter Bildverarbeitung und spektraler Anomaliedetektion sichern Sie die Qualit\u00e4t Ihrer Produktion \u2013 zuverl\u00e4ssig, skalierbar, zukunftsweisend.<\/p>\n<\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n\n<style>.kb-row-layout-id5079_f6ee9c-c6 > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id5079_f6ee9c-c6 > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id5079_f6ee9c-c6 > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-md, 2rem);max-width:var( --global-content-width, 1490px );padding-left:var(--global-content-edge-padding);padding-right:var(--global-content-edge-padding);padding-top:var(--global-kb-spacing-4xl, 8rem);padding-bottom:var(--global-kb-spacing-4xl, 8rem);grid-template-columns:minmax(0, 1fr);}.kb-row-layout-id5079_f6ee9c-c6 > .kt-row-layout-overlay{opacity:0.90;background-color:var(--global-palette9, #ffffff);}@media all and (max-width: 1024px){.kb-row-layout-id5079_f6ee9c-c6 > .kt-row-column-wrap{grid-template-columns:minmax(0, 1fr);}}@media all and (max-width: 767px){.kb-row-layout-id5079_f6ee9c-c6 > 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.kt-row-column-wrap{padding-right:0px;padding-left:0px;grid-template-columns:minmax(0, 1fr);}}<\/style><div class=\"kb-row-layout-wrap kb-row-layout-id5079_1517e8-62 alignnone kt-row-has-bg wp-block-kadence-rowlayout\" id=\"author\"><div class=\"kt-row-layout-overlay kt-row-overlay-normal\"><\/div><div class=\"kt-row-column-wrap kt-has-2-columns kt-row-layout-right-golden kt-tab-layout-row kt-mobile-layout-row kt-row-valign-top kt-inner-column-height-full kb-theme-content-width\">\n<style>.kadence-column5079_cdc403-ae > .kt-inside-inner-col{display:flex;}.kadence-column5079_cdc403-ae > .kt-inside-inner-col{border-right:4px solid var(--global-palette2, #2B6CB0);}.kadence-column5079_cdc403-ae > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5079_cdc403-ae > .kt-inside-inner-col{flex-direction:column;justify-content:flex-start;}.kadence-column5079_cdc403-ae > .kt-inside-inner-col > .aligncenter{width:100%;}.kt-row-column-wrap > .kadence-column5079_cdc403-ae{align-self:flex-start;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae > .kt-inside-inner-col{flex-direction:column;justify-content:flex-start;}.kadence-column5079_cdc403-ae > .kt-inside-inner-col{background-image:url('https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2024\/08\/cubert-sales-director-matthias-locherer.jpg');background-size:cover;background-position:50% 23%;background-attachment:scroll;background-repeat:no-repeat;}.kadence-column5079_cdc403-ae > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5079_cdc403-ae{position:relative;}@media all and (max-width: 1024px){.kadence-column5079_cdc403-ae > .kt-inside-inner-col{min-height:550px;border-right:0px solid var(--global-palette2, #2B6CB0);border-bottom:5px solid var(--global-palette2, #2B6CB0);flex-direction:column;justify-content:flex-start;}}@media all and (max-width: 1024px){.kt-row-column-wrap > .kadence-column5079_cdc403-ae{align-self:flex-start;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae{align-self:auto;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae > .kt-inside-inner-col{flex-direction:column;justify-content:flex-start;}}@media all and (max-width: 767px){.kadence-column5079_cdc403-ae > .kt-inside-inner-col{min-height:220px;border-right:0px solid var(--global-palette2, #2B6CB0);border-bottom:5px solid var(--global-palette2, #2B6CB0);flex-direction:column;justify-content:flex-start;}.kt-row-column-wrap > .kadence-column5079_cdc403-ae{align-self:flex-start;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_cdc403-ae > .kt-inside-inner-col{flex-direction:column;justify-content:flex-start;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5079_cdc403-ae\"><div class=\"kt-inside-inner-col\"><\/div><\/div>\n\n\n<style>.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{display:flex;}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{padding-top:var(--global-kb-spacing-md, 2rem);padding-right:var(--global-kb-spacing-lg, 3rem);padding-bottom:var(--global-kb-spacing-lg, 3rem);padding-left:var(--global-kb-spacing-sm, 1.5rem);}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col > .aligncenter{width:100%;}.kt-row-column-wrap > .kadence-column5079_667b2a-e9{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column5079_667b2a-e9{position:relative;}@media all and (max-width: 1024px){.kt-row-column-wrap > .kadence-column5079_667b2a-e9{align-self:center;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9{align-self:auto;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 1024px){.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kt-row-column-wrap > .kadence-column5079_667b2a-e9{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column5079_667b2a-e9 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column5079_667b2a-e9\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54, .wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54[data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54[data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54[data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54, .wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54[data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54, .wp-block-kadence-advancedheading.kt-adv-heading5079_4cf3aa-54[data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"]{font-size:24px;}}<\/style>\n<p class=\"kt-adv-heading5079_4cf3aa-54 wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading5079_4cf3aa-54\"><strong>\u00dcber den Autor<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dr. Matthias Locherer ist seit 2017 Director Global Market Development bei Cubert GmbH. Mit einem PhD in Erdbeobachtung von der Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen bringt er umfangreiche Expertise in der Fernerkundung, spektraler Bildgebung und Datenanalyse mit. Matthias hat an verschiedenen Forschungsprojekten und Publikationen mitgewirkt, insbesondere im Bereich der hyperspektralen \u00dcberwachung biophysikalischer und biochemischer Parameter mit hyperspektralen Satellitenmissionen. Seine fundierten Kenntnisse in optischer Messtechnik und physikalischer Modellierung machen ihn zu einem wichtigen Treiber f\u00fcr die Weiterentwicklung innovativer hyperspektraler Technologien bei Cubert.<\/p>\n\n\n<style>.wp-block-kadence-advancedbtn.kb-btns5079_75c033-21{gap:var(--global-kb-gap-xs, 0.5rem );justify-content:flex-start;align-items:center;}.kt-btns5079_75c033-21 .kt-button{font-weight:normal;font-style:normal;}.kt-btns5079_75c033-21 .kt-btn-wrap-0{margin-right:5px;}.wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button{color:#555555;border-color:#555555;}.wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button:focus{color:#ffffff;border-color:#444444;}.wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button::before{display:none;}.wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns5079_75c033-21 .kt-btn-wrap-0 .kt-button:focus{background:#444444;}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns5079_75c033-21\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn5079_720cc0-18.kb-button{width:initial;}.wp-block-kadence-advancedbtn .kb-btn5079_720cc0-18.kb-button{color:var(--global-palette10, #3182CE);background:var(--global-palette1, #3182CE);padding:var(--kb-btn-padding);margin:var(--kb-btn-margin);padding-top:5px;padding-right:5px;padding-bottom:5px;padding-left:5px;box-shadow:none;}.kb-btn5079_720cc0-18.kb-button .kb-svg-icon-wrap{font-size:30px;--kb-button-icon-size:30px;}.wp-block-kadence-advancedbtn .kb-btn5079_720cc0-18.kb-button:hover, .wp-block-kadence-advancedbtn .kb-btn5079_720cc0-18.kb-button:focus{color:var(--global-palette10, #3182CE);background:var(--global-palette13, #3182CE);box-shadow:var(--kb-btn-shadow-hover, none);}.kb-btn5079_720cc0-18.kb-button .kt-btn-inner-text{display:none;}<\/style><a class=\"kb-button kt-button button kb-btn5079_720cc0-18 kt-btn-size-standard kt-btn-width-type-auto kb-btn-global-fill kt-btn-has-text-false kt-btn-has-svg-true wp-block-kadence-singlebtn\" href=\"https:\/\/www.linkedin.com\/in\/matthias-locherer-b6a103136\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"kb-svg-icon-wrap kb-svg-icon-fa_linkedin-in kt-btn-icon-side-right\"><svg viewBox=\"0 0 448 512\"  fill=\"currentColor\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"  aria-hidden=\"true\"><path d=\"M100.3 480H7.4V180.9h92.9V480zM53.8 140.1C24.1 140.1 0 115.5 0 85.8 0 56.1 24.1 32 53.8 32c29.7 0 53.8 24.1 53.8 53.8 0 29.7-24.1 54.3-53.8 54.3zM448 480h-92.7V334.4c0-34.7-.7-79.2-48.3-79.2-48.3 0-55.7 37.7-55.7 76.7V480h-92.8V180.9h89.1v40.8h1.3c12.4-23.5 42.7-48.3 87.9-48.3 94 0 111.3 61.9 111.3 142.3V480z\"\/><\/svg><\/span><\/a><\/div>\n<\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Wie hyperspektrale Bildgebung und KI bei der automatisierten Anomalieerkennung eingesetzt werden.<\/p>\n","protected":false},"author":3,"featured_media":7541,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"bBlocksCursorData":"","_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","slim_seo":{"title":"KI-gest\u00fctzte Anomalieerkennung in der Industrie","description":"Mit KI Anomalien fr\u00fchzeitig erkennen: Effizientere Qualit\u00e4tskontrolle, weniger Ausf\u00e4lle und maximale Prozesssicherheit in der Industrie. Mehr erfahren!"},"_slim_seo_primary_term_category":14,"_slim_seo_primary_term_post_tag":0,"footnotes":""},"categories":[14,20],"tags":[],"hf_cat_post":[],"class_list":["post-7510","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-anwendungen","category-machine-vision"],"taxonomy_info":{"category":[{"value":14,"label":"Anwendungen"},{"value":20,"label":"Machine Vision"}]},"featured_image_src_large":["https:\/\/cubert-hyperspectral.com\/wp-content\/uploads\/2025\/07\/programmer-analyzing-complex-ai-brain-models-on-la-2025-02-20-03-50-59-utc-1024x683.jpg",1024,683,true],"author_info":{"display_name":"Deborah Kliebhan","author_link":"https:\/\/cubert-hyperspectral.com\/de\/author\/deborah\/"},"comment_info":0,"category_info":[{"term_id":14,"name":"Anwendungen","slug":"anwendungen","term_group":0,"term_taxonomy_id":14,"taxonomy":"category","description":"","parent":0,"count":19,"filter":"raw","cat_ID":14,"category_count":19,"category_description":"","cat_name":"Anwendungen","category_nicename":"anwendungen","category_parent":0},{"term_id":20,"name":"Machine Vision","slug":"machine-vision","term_group":0,"term_taxonomy_id":20,"taxonomy":"category","description":"","parent":14,"count":7,"filter":"raw","cat_ID":20,"category_count":7,"category_description":"","cat_name":"Machine Vision","category_nicename":"machine-vision","category_parent":14}],"tag_info":false,"_links":{"self":[{"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/posts\/7510","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/comments?post=7510"}],"version-history":[{"count":6,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/posts\/7510\/revisions"}],"predecessor-version":[{"id":13511,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/posts\/7510\/revisions\/13511"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/media\/7541"}],"wp:attachment":[{"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/media?parent=7510"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/categories?post=7510"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/tags?post=7510"},{"taxonomy":"hf_cat_post","embeddable":true,"href":"https:\/\/cubert-hyperspectral.com\/de\/wp-json\/wp\/v2\/hf_cat_post?post=7510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}