{"id":32988,"date":"2024-04-24T10:39:01","date_gmt":"2024-04-24T08:39:01","guid":{"rendered":"https:\/\/www.westcam-group.com\/produkte\/quality-assurance-with-ml-ai-artificial-intelligence\/"},"modified":"2025-05-13T19:37:40","modified_gmt":"2025-05-13T17:37:40","slug":"quality-assurance-with-ml-ai-artificial-intelligence","status":"publish","type":"product","link":"https:\/\/www.westcam-group.com\/en\/products\/imaging-solutions\/machine-learning-ai\/quality-assurance-with-ml-ai-artificial-intelligence\/","title":{"rendered":"Quality assurance with ML\/AI Artificial Intelligence"},"content":{"rendered":"\n
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Instead of classic Image processing and “intelligent” plug & play sensors <\/div>\n\n\n\n

Quality assurance with ML\/AI Artificial Intelligence<\/h1>\n\n\n\n

We work intensively with machine learning – a sub-area of the field of artificial intelligence.<\/strong><\/p>\n\n\n\n

Coming from the field of classic Image processing, our Machine Learning<\/strong> also revolves around the processing of image data. <\/p>\n\n\n\n

We can also realize more complex tasks that require the combination of classic algorithms and machine learning or the combination of different data sources (analysis of several images of an object, integration of additional sensor data or user input).<\/p>\n\n\n\n

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Request now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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\"Quality<\/figure>\n\n\n\n
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Highlights<\/div>\n\n\n\n
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  • Classification<\/li>\n\n\n\n
  • Multimodal data processing<\/li>\n\n\n\n
  • Object detection & localization<\/li>\n\n\n\n
  • Anomaly detection<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    \"\"<\/figure>
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    Classification<\/h3>\n\n\n\n
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    • Differentiation into any classes (e.g. [good , bad], or [good , error type 1 , error type 2]).<\/li>\n\n\n\n
    • Training based on a large amount of training data for all classes.<\/span><\/li>\n\n\n\n
    • Detection of very subtle differences possible.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n
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      Multimodal data processing (“extended classification”)<\/h3>\n\n\n\n
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      • Differentiation into any classes (e.g. [optimum quality , average quality , rejects]).<\/li>\n\n\n\n
      • Training based on a large amount of training data for all classes.<\/span><\/li>\n\n\n\n
      • Input data from different sources is combined, e.g. several camera images, additional product information such as color, shape or weight or user input.<\/span><\/li>\n\n\n\n
      • Combination of different algorithms possible.<\/span><\/li>\n\n\n\n
      • Recognition of differences, which is only possible from the combination of different data. Inspection of an entire product is possible (e.g. the entire surface through several images which are evaluated together). <\/li>\n<\/ul>\n<\/div>
        \"\"<\/figure><\/div>\n\n\n\n
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        Recognition & detection<\/h3>\n\n\n\n

        Object detection & localization <\/span><\/strong><\/p>\n\n\n\n

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        • Recognizing objects, including determining their position.<\/span><\/li>\n\n\n\n
        • Training based on training data of all objects.<\/span><\/li>\n\n\n\n
        • Among other things, this enables presence checks or the counting of objects.<\/li>\n<\/ul>\n\n\n\n

          Anomaly detection<\/strong><\/p>\n\n\n\n

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          • Differentiation between “normal” and “anomalous” data.<\/li>\n\n\n\n
          • Training is usually carried out with a small amount of normal and abnormal data. If not otherwise possible, training on the basis of 100% normal examples is possible. <\/span><\/li>\n\n\n\n
          • Depending on the degree of anomalies, medium to good detection accuracy is possible.<\/li>\n<\/ul>\n<\/div><\/div>\n<\/div>\n\n\n\n
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            Frequently asked questions (FAQs)<\/h3>\n\n\n\n