{"id":8508,"date":"2022-05-07T00:52:09","date_gmt":"2022-05-07T00:52:09","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2022\/05\/07\/how-to-maintain-product-quality-with-deep-learning\/"},"modified":"2022-05-07T00:52:09","modified_gmt":"2022-05-07T00:52:09","slug":"how-to-maintain-product-quality-with-deep-learning","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2022\/05\/07\/how-to-maintain-product-quality-with-deep-learning\/","title":{"rendered":"How to maintain product quality with deep learning"},"content":{"rendered":"<div>\n<p>Deep Learning helps companies to automate operative processes in many areas. Industrial companies in particular also benefit from product quality assurance by automated failure and defect detection. Computer Vision enables automation to identify scratches and cracks on product item surfaces. You will find more information about how this works in the following infografic from <a href=\"https:\/\/www.datanomiq.de\" target=\"_blank\" rel=\"noopener\">DATANOMIQ<\/a> and <a href=\"https:\/\/www.pixolution.org\" target=\"_blank\" rel=\"noopener\">pixolution<\/a> you can download using the link below.<\/p>\n<div id=\"attachment_6083\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-science-blog.com\/en\/wp-content\/uploads\/sites\/4\/2022\/05\/Infografik_Fehlerhafte_Waren_Final.pdf\" target=\"_blank\" rel=\"noopener\"><img aria-describedby=\"caption-attachment-6083\" loading=\"lazy\" class=\"wp-image-6083\" src=\"https:\/\/data-science-blog.com\/en\/wp-content\/uploads\/sites\/4\/2022\/05\/Infografik_Fehlerhafte_Waren_Final.png\" alt=\"How to maintain product quality with automatic defect detection - Infographic\" width=\"604\" height=\"1957\"><\/a><\/p>\n<p id=\"caption-attachment-6083\" class=\"wp-caption-text\">How to maintain product quality with automatic defect detection \u2013 Infographic<\/p>\n<\/div>\n<div id=\"attachment_3613\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-science-blog.com\/en\/wp-content\/uploads\/sites\/4\/2022\/05\/Infografik_Fehlerhafte_Waren_Final.pdf\"><img aria-describedby=\"caption-attachment-3613\" loading=\"lazy\" class=\" wp-image-3613\" src=\"https:\/\/data-science-blog.com\/wp-content\/uploads\/2015\/07\/pdf-file.jpg\" alt=\"Download Infographic as PDF\" width=\"86\" height=\"107\"><\/a><\/p>\n<p id=\"caption-attachment-3613\" class=\"wp-caption-text\"><a href=\"https:\/\/data-science-blog.com\/en\/wp-content\/uploads\/sites\/4\/2022\/05\/Infografik_Fehlerhafte_Waren_Final.pdf\">Download Infographic as PDF<\/a><\/p>\n<\/div>\n<div id=\"author-bio-box\">\n<h3><a href=\"https:\/\/data-science-blog.com\/en\/blog\/author\/aunkofer2\/\" title=\"All posts by Benjamin Aunkofer\" rel=\"author\">Benjamin Aunkofer<\/a><\/h3>\n<div class=\"bio-gravatar\"><img loading=\"lazy\" data-del=\"avatar\" src=\"https:\/\/data-science-blog.com\/en\/wp-content\/uploads\/sites\/4\/2015\/05\/foto2-1-1-80x80.jpg\" class=\"avatar pp-user-avatar avatar-70 photo \" height=\"70\" width=\"70\"><\/div>\n<p><a target=\"_blank\" rel=\"nofollow noopener noreferrer\" href=\"http:\/\/www.datanomiq.de\" class=\"bio-icon bio-icon-website\"><\/a><a target=\"_blank\" rel=\"nofollow noopener noreferrer\" href=\"https:\/\/twitter.com\/WIngenieur\" class=\"bio-icon bio-icon-twitter\"><\/a><a target=\"_blank\" rel=\"nofollow noopener noreferrer\" href=\"https:\/\/www.linkedin.com\/in\/benjamin-aunkofer-98710714\" class=\"bio-icon bio-icon-linkedin\"><\/a><\/p>\n<p class=\"bio-description\">Benjamin Aunkofer is Lead Data Scientist at <a href=\"http:\/\/www.datanomiq.de\">DATANOMIQ<\/a>, a consulting company for applied data science in Berlin. He is lecturer for Data Science and Data Strategy at <a href=\"http:\/\/www.htw-berlin.de\">HTW Berlin<\/a> and gives trainings for Business Intelligence, Data Science and Machine Learning for companies.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/data-science-blog.com\/en\/blog\/2022\/05\/05\/how-to-maintain-product-quality-with-deep-learning\/<\/p>\n","protected":false},"author":0,"featured_media":8509,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[],"_links":{"self":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/8508"}],"collection":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/comments?post=8508"}],"version-history":[{"count":0,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/8508\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media\/8509"}],"wp:attachment":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media?parent=8508"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/categories?post=8508"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/tags?post=8508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}