{"id":7655,"date":"2025-07-18T23:35:35","date_gmt":"2025-07-18T21:35:35","guid":{"rendered":"https:\/\/cubert-hyperspectral.com\/?p=7655"},"modified":"2026-10-03T21:03:25","modified_gmt":"2026-10-03T19:03:25","slug":"ai-supported-anomaly-detection-in-industry","status":"publish","type":"post","link":"https:\/\/cubert-hyperspectral.com\/en\/knowledge\/ai-supported-anomaly-detection-in-industry\/","title":{"rendered":"AI-supported anomaly detection in industry"},"content":{"rendered":"<style>.kadence-column7655_533eca-33 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7655_533eca-33 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7655_533eca-33 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7655_533eca-33 > .kt-inside-inner-col{background-color:rgba(0, 0, 0, 0);}.kadence-column7655_533eca-33 > .kt-inside-inner-col:before{opacity:0.85;background-color:var(--global-palette7, #EDF2F7);}.kadence-column7655_533eca-33{position:relative;}@media all and (max-width: 1024px){.kadence-column7655_533eca-33 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7655_533eca-33 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7655_533eca-33 kb-section-has-overlay\"><div class=\"kt-inside-inner-col\"><style>.kb-row-layout-id7655_4f058f-37 > .kt-row-column-wrap{align-content:center;}:where(.kb-row-layout-id7655_4f058f-37 > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:center;}.kb-row-layout-id7655_4f058f-37 > .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-column7655_052e18-05 > .kt-inside-inner-col{display:flex;}.kadence-column7655_052e18-05 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7655_052e18-05 > .kt-inside-inner-col > .aligncenter{width:100%;}.kt-row-column-wrap > .kadence-column7655_052e18-05{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7655_052e18-05 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7655_052e18-05{z-index:10;position:relative;}.kadence-column7655_052e18-05, .kt-inside-inner-col > .kadence-column7655_052e18-05:not(.specificity){margin-right:0px;}@media all and (max-width: 1024px){.kt-row-column-wrap > .kadence-column7655_052e18-05{align-self:center;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05{align-self:auto;}}@media all and (max-width: 1024px){.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 1024px){.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kt-row-column-wrap > .kadence-column7655_052e18-05{align-self:center;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05{align-self:auto;}.kt-inner-column-height-full:not(.kt-has-1-columns) > .wp-block-kadence-column.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}.kadence-column7655_052e18-05 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7655_052e18-05\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedbtn.kb-btns7655_50f201-a3{padding-bottom:var(--global-kb-spacing-md, 2rem);gap:var(--global-kb-gap-xs, 0.5rem );justify-content:flex-start;align-items:center;}.kt-btns7655_50f201-a3 .kt-button{font-weight:normal;font-style:normal;}.kt-btns7655_50f201-a3 .kt-btn-wrap-0{margin-right:5px;}.wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button{color:#555555;border-color:#555555;}.wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button:focus{color:#ffffff;border-color:#444444;}.wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button::before{display:none;}.wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns7655_50f201-a3 .kt-btn-wrap-0 .kt-button:focus{background:#444444;}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns7655_50f201-a3\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn7655_11f4c9-a4.kb-button{width:initial;}<\/style><span class=\"kb-button kt-button button kb-btn7655_11f4c9-a4 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-btn7655_d6b889-7e.kb-button{width:initial;}<\/style><span class=\"kb-button kt-button button kb-btn7655_d6b889-7e 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-heading7655_80426c-93, .wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93[data-kb-block=\"kb-adv-heading7655_80426c-93\"]{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-heading7655_80426c-93 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93[data-kb-block=\"kb-adv-heading7655_80426c-93\"] 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-heading7655_80426c-93 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93[data-kb-block=\"kb-adv-heading7655_80426c-93\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93, .wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93[data-kb-block=\"kb-adv-heading7655_80426c-93\"]{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-heading7655_80426c-93, .wp-block-kadence-advancedheading.kt-adv-heading7655_80426c-93[data-kb-block=\"kb-adv-heading7655_80426c-93\"]{text-shadow:2px 2px 5px rgba(0, 0, 0, 0.2);font-size:30px;line-height:40px;}}<\/style>\n<h1 class=\"kt-adv-heading7655_80426c-93 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7655_80426c-93\"><mark true=\"true\" class=\"kt-highlight\">AI-supported<\/mark> anomaly detection in industry<\/h1>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7655_5625e3-6b, .wp-block-kadence-advancedheading.kt-adv-heading7655_5625e3-6b[data-kb-block=\"kb-adv-heading7655_5625e3-6b\"]{max-width:700px;font-size:28px;line-height:36px;font-weight:100;font-style:normal;font-family:Roboto;}.wp-block-kadence-advancedheading.kt-adv-heading7655_5625e3-6b mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_5625e3-6b[data-kb-block=\"kb-adv-heading7655_5625e3-6b\"] 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-heading7655_5625e3-6b img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_5625e3-6b[data-kb-block=\"kb-adv-heading7655_5625e3-6b\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading7655_5625e3-6b wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading7655_5625e3-6b\">How hyperspectral imaging and AI are used for automated anomaly detection.\n<\/p>\n\n\n<style>.wp-block-kadence-iconlist.kt-svg-icon-list-items7655_994d70-07:not(.this-stops-third-party-issues){margin-top:0px;margin-bottom:0px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7655_994d70-07 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-items7655_994d70-07 ul.kt-svg-icon-list{grid-row-gap:5px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7655_994d70-07 .kb-svg-icon-wrap{font-size:25px;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7655_994d70-07 ul.kt-svg-icon-list .kt-svg-icon-list-item-wrap .kt-svg-icon-list-single{margin-right:15px;}.kt-svg-icon-list-items7655_994d70-07 ul.kt-svg-icon-list .kt-svg-icon-list-item-wrap, .kt-svg-icon-list-items7655_994d70-07 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-items7655_994d70-07 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-items7655_994d70-07 .wp-block-kadence-listitem a{text-decoration:underline;}.wp-block-kadence-iconlist.kt-svg-icon-list-items7655_994d70-07 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-items7655_994d70-07 kt-svg-icon-list-columns-1 alignnone kt-list-icon-aligntop\"><ul class=\"kt-svg-icon-list\"><style>.kt-svg-icon-list-item-7655_340e4d-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-7655_340e4d-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\">Author: <a href=\"#author\" title=\"\">Dr. Matthias Locherer<\/a><\/span><\/li>\n\n\n<style>.kt-svg-icon-list-item-7655_60c9e2-bf .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-7655_60c9e2-bf\"><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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October 2026<\/span><\/li>\n<\/ul><\/div>\n<\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n\n<style>.kadence-column7655_c934fc-cb > .kt-inside-inner-col{padding-top:var(--global-kb-spacing-3xl, 6.5rem);}.kadence-column7655_c934fc-cb > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7655_c934fc-cb > .kt-inside-inner-col{flex-direction:column;}.kadence-column7655_c934fc-cb > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7655_c934fc-cb > .kt-inside-inner-col:before{opacity:0.9;background-color:var(--global-palette9, #ffffff);}.kadence-column7655_c934fc-cb, .kadence-column7655_c934fc-cb h1, .kadence-column7655_c934fc-cb h2, .kadence-column7655_c934fc-cb h3, .kadence-column7655_c934fc-cb h4, .kadence-column7655_c934fc-cb h5, .kadence-column7655_c934fc-cb h6{color:var(--global-palette4, #2D3748);}.kadence-column7655_c934fc-cb a{color:var(--global-palette4, #2D3748);}.kadence-column7655_c934fc-cb a:hover{color:var(--global-palette3, 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1rem);}.kadence-column7655_aabd6b-ec > .kt-inside-inner-col{flex-direction:column;}.kadence-column7655_aabd6b-ec > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7655_aabd6b-ec > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7655_aabd6b-ec{position:relative;}@media all and (max-width: 1024px){.kadence-column7655_aabd6b-ec > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7655_aabd6b-ec > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7655_aabd6b-ec kb-section-is-sticky\"><div class=\"kt-inside-inner-col\"><style>.kb-table-of-content-nav.kb-table-of-content-id11019_4ec261-a1 .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-id11019_4ec261-a1 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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-id11019_4ec261-a1 .kb-table-of-contents-title{font-size:20px;}}<\/style><\/div><\/div>\n\n\n<style>.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7655_e3ecbd-a2 a{color:#e64600;}.kadence-column7655_e3ecbd-a2 a:hover{color:var(--global-palette13, #3182CE);}.kadence-column7655_e3ecbd-a2{position:relative;}.kadence-column7655_e3ecbd-a2, .kt-inside-inner-col > .kadence-column7655_e3ecbd-a2:not(.specificity){margin-top:-7px;}@media all and (max-width: 1024px){.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7655_e3ecbd-a2 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7655_e3ecbd-a2\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74, .wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74[data-kb-block=\"kb-adv-heading7655_596a51-74\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74[data-kb-block=\"kb-adv-heading7655_596a51-74\"] 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-heading7655_596a51-74 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74[data-kb-block=\"kb-adv-heading7655_596a51-74\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74, .wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74[data-kb-block=\"kb-adv-heading7655_596a51-74\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74, .wp-block-kadence-advancedheading.kt-adv-heading7655_596a51-74[data-kb-block=\"kb-adv-heading7655_596a51-74\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7655_596a51-74 wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7655_596a51-74\">Use Case<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This use case focuses on automated visual inspection of products within industrial manufacturing processes. The goal is to detect defects such as moisture in dry materials, contamination, or insufficient mixing. A camera captures full-surface images of the products, and a <a href=\"https:\/\/github.com\/cubert-hyperspectral\/cuvis.ai.examples\/tree\/main\/EfficientAD\" target=\"_blank\" rel=\"noopener\" title=\"\">trained algorithm<\/a> evaluates whether the image matches the expected condition or indicates an anomaly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A key challenge in industrial environments lies in their inherent complexity: not all potential defects can be clearly defined in advance. Therefore, the ability to detect previously unknown anomalies is especially important.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Since time is a critical resource in most production processes, this project focused on developing a solution that delivers precise results with minimal acquisition and evaluation effort\u2014in real time and with high coverage.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The presented solution reliably detects anomalies as small as a pea on an area of 60x30 cm\u2014based on just a single image per product. In the demonstrated example, sawdust was examined for unwanted materials and moisture spots. While many foreign objects are detectable in the visible spectral range, water usually remains invisible. In this case, moist areas can be clearly highlighted using infrared light\u2014especially in the SWIR range\u2014and made accessible for machine evaluation.<\/p>\n\n\n<style>.kb-image7655_511e33-f0 .kb-image-has-overlay:after{opacity:0.3;}.kb-image7655_511e33-f0 img.kb-img, .kb-image7655_511e33-f0 .kb-img img{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<div class=\"wp-block-kadence-image kb-image7655_511e33-f0\"><figure class=\"aligncenter 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><\/div>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33, .wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33[data-kb-block=\"kb-adv-heading7655_2e18ae-33\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33[data-kb-block=\"kb-adv-heading7655_2e18ae-33\"] 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-heading7655_2e18ae-33 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33[data-kb-block=\"kb-adv-heading7655_2e18ae-33\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33, .wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33[data-kb-block=\"kb-adv-heading7655_2e18ae-33\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33, .wp-block-kadence-advancedheading.kt-adv-heading7655_2e18ae-33[data-kb-block=\"kb-adv-heading7655_2e18ae-33\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7655_2e18ae-33 wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7655_2e18ae-33\">Complexity demands a deep learning approach<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">IClassical image processing falls short for this task: production processes vary, training data on defects are often incomplete, and deviations are diverse.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Neural networks, especially deep learning models, offer the solution: they learn the features of the normal state from defect-free examples and later identify previously unknown anomalies independently.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">During training, the network repeatedly processes the data, capturing patterns and deviations in a high-dimensional space. This ability to generalize makes deep learning the ideal method for companies seeking reliable quality assurance in dynamic production environments.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b, .wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b[data-kb-block=\"kb-adv-heading7655_d58ff7-4b\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b[data-kb-block=\"kb-adv-heading7655_d58ff7-4b\"] 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-heading7655_d58ff7-4b img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b[data-kb-block=\"kb-adv-heading7655_d58ff7-4b\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b, .wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b[data-kb-block=\"kb-adv-heading7655_d58ff7-4b\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b, .wp-block-kadence-advancedheading.kt-adv-heading7655_d58ff7-4b[data-kb-block=\"kb-adv-heading7655_d58ff7-4b\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7655_d58ff7-4b wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7655_d58ff7-4b\">Why SWIR? Spectral advantages in the short-wave infrared range<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The SWIR method exploits the absorption properties of water in the short-wave infrared range: moist areas appear with much higher contrast here than in conventional color images. This enables reliable detection of production defects that are barely visible to the naked eye.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Combining spectral data from multiple wavelengths with machine learning results in robust and efficient anomaly detection\u2014a clear advantage that surpasses traditional methods in both precision and efficiency.<\/p>\n\n\n<style>.kb-image7655_cf4485-f7 .kb-image-has-overlay:after{opacity:0.3;}.kb-image7655_cf4485-f7 img.kb-img, .kb-image7655_cf4485-f7 .kb-img img{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<figure class=\"wp-block-kadence-image kb-image7655_cf4485-f7 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-heading7655_617fab-8f, .wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f[data-kb-block=\"kb-adv-heading7655_617fab-8f\"]{font-size:30px;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f[data-kb-block=\"kb-adv-heading7655_617fab-8f\"] 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-heading7655_617fab-8f img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f[data-kb-block=\"kb-adv-heading7655_617fab-8f\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f, .wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f[data-kb-block=\"kb-adv-heading7655_617fab-8f\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f, .wp-block-kadence-advancedheading.kt-adv-heading7655_617fab-8f[data-kb-block=\"kb-adv-heading7655_617fab-8f\"]{font-size:24px;}}<\/style>\n<h2 class=\"kt-adv-heading7655_617fab-8f wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading7655_617fab-8f\">Camera setup: precise data acquisition<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A multispectral camera system was used for data acquisition\u2014a high-resolution color camera combined with SWIR sensors tuned to specific spectral bands in the infrared range. This configuration delivers consistent data cubes containing information across multiple wavelength ranges for each pixel. Precise calibration and synchronization of the sensors ensure reliable image quality for model training.<\/p>\n\n\n<style>.kb-image7655_47e8a3-b1 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image7655_47e8a3-b1\"><figure class=\"aligncenter 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><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">ML Network: Deep learning for non-deterministic scenarios<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">EfficientAD, a specialized autoencoder architecture, was trained for this use case. The advantage: only a dataset of defect-free training data is needed to learn the normal state of the production environment. Deviations from this pattern are later reliably detected as potential anomalies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This method of anomaly detection reduces the need for comprehensive defect catalogs and is therefore ideal for companies with heterogeneous processes and variable surface structures.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter 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<\/div>\n\n\n<h2 class=\"wp-block-heading\">Cuvis.AI: Platform for hyperspectral anomaly detection<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Cubert offers Cuvis.AI, a platform specifically designed for developing, monitoring, and inferring anomaly detection based on hyperspectral data. It enables efficient training of machine learning models with spectral images and integration into industrial production lines\u2014modular, scalable, and future-proof.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Dataset: High variability as a foundation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The dataset used consisted of multispectral images of sawdust with a wide variety of textures, colors, and spectral properties. This high variability ensures that the trained model can reliably detect deviations even in real production environments with complex surfaces.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Precise segmentation of the images allows for objective evaluation of the anomaly detection performance and continuous improvement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: Intelligent quality assurance for modern production<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">By combining hyperspectral imaging, spectral diversity across multiple wavelengths, advanced machine learning methods, and a robust platform, Cubert has developed a solution that elevates efficiency, precision, and automation in quality assurance to a new level.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For companies investing in Industry 4.0 processes, automated anomaly detection becomes a strategic tool: disruptions are detected early, equipment is better monitored, and machines operate more efficiently.<\/p>\n\n\n<style>.kb-image7655_d5b051-ce .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image7655_d5b051-ce\"><figure class=\"aligncenter 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><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">In short: With intelligent image processing and spectral anomaly detection, you ensure the quality of your production\u2014reliably, scalably, and forward-looking.<\/p>\n<\/div><\/div>\n\n<\/div><\/div><\/div><\/div>\n\n\n<style>.kadence-column7655_25ffad-51 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column7655_25ffad-51 > .kt-inside-inner-col{flex-direction:column;}.kadence-column7655_25ffad-51 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column7655_25ffad-51 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column7655_25ffad-51{position:relative;}@media all and (max-width: 1024px){.kadence-column7655_25ffad-51 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column7655_25ffad-51 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column7655_25ffad-51\"><div class=\"kt-inside-inner-col\"><style>.kb-row-layout-id5063_159358-f6 > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id5063_159358-f6 > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id5063_159358-f6 > 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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-heading5063_8f9f6d-54 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading5063_8f9f6d-54[data-kb-block=\"kb-adv-heading5063_8f9f6d-54\"] img.kb-inline-image{width:150px;vertical-align:baseline;}@media all and (max-width: 1024px){.wp-block-kadence-advancedheading.kt-adv-heading5063_8f9f6d-54, .wp-block-kadence-advancedheading.kt-adv-heading5063_8f9f6d-54[data-kb-block=\"kb-adv-heading5063_8f9f6d-54\"]{font-size:30px;}}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading5063_8f9f6d-54, .wp-block-kadence-advancedheading.kt-adv-heading5063_8f9f6d-54[data-kb-block=\"kb-adv-heading5063_8f9f6d-54\"]{font-size:24px;}}<\/style>\n<p class=\"kt-adv-heading5063_8f9f6d-54 wp-block-kadence-advancedheading has-theme-palette-7-color has-text-color\" data-kb-block=\"kb-adv-heading5063_8f9f6d-54\"><strong>About the Author<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dr. Matthias Locherer is Director Global Market Development at Cubert GmbH.&nbsp; With a PhD in Earth Observation from Ludwig Maximilian University of Munich, he brings extensive expertise in remote sensing, spectral imaging, and data analysis. Matthias has contributed to numerous research projects and publications, particularly in the hyperspectral monitoring of biophysical and biochemical parameters using hyperspectral satellite missions. His deep knowledge of optical measurement techniques and physical modeling makes him a key driver in advancing innovative hyperspectral technologies at Cubert.<\/p>\n\n\n<style>.wp-block-kadence-advancedbtn.kb-btns5063_1cce14-b7{gap:var(--global-kb-gap-xs, 0.5rem );justify-content:flex-start;align-items:center;}.kt-btns5063_1cce14-b7 .kt-button{font-weight:normal;font-style:normal;}.kt-btns5063_1cce14-b7 .kt-btn-wrap-0{margin-right:5px;}.wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button{color:#555555;border-color:#555555;}.wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button:focus{color:#ffffff;border-color:#444444;}.wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button::before{display:none;}.wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns5063_1cce14-b7 .kt-btn-wrap-0 .kt-button:focus{background:#444444;}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns5063_1cce14-b7\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn5063_dff2c2-cd.kb-button{width:initial;}.wp-block-kadence-advancedbtn .kb-btn5063_dff2c2-cd.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-btn5063_dff2c2-cd.kb-button .kb-svg-icon-wrap{font-size:30px;--kb-button-icon-size:30px;}.wp-block-kadence-advancedbtn .kb-btn5063_dff2c2-cd.kb-button:hover, .wp-block-kadence-advancedbtn .kb-btn5063_dff2c2-cd.kb-button:focus{color:var(--global-palette10, #3182CE);background:var(--global-palette13, #3182CE);box-shadow:var(--kb-btn-shadow-hover, none);}.kb-btn5063_dff2c2-cd.kb-button .kt-btn-inner-text{display:none;}<\/style><a class=\"kb-button kt-button button kb-btn5063_dff2c2-cd 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><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>How hyperspectral imaging and AI are used for automated anomaly detection.<\/p>\n","protected":false},"author":1,"featured_media":12972,"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":"AI-Powered Anomaly Detection in Industry","description":"Detect anomalies early with AI: improved quality control, reduced downtime, and maximum process reliability in industrial applications. 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