{"id":31578,"date":"2025-08-01T12:58:27","date_gmt":"2025-08-01T10:58:27","guid":{"rendered":"https:\/\/i-mas.com\/how-to-train-ai-to-detect-defects-data-models-and-validation\/"},"modified":"2025-08-01T12:58:28","modified_gmt":"2025-08-01T10:58:28","slug":"how-to-train-ai-to-detect-defects-data-models-and-validation","status":"publish","type":"post","link":"https:\/\/i-mas.com\/en\/how-to-train-ai-to-detect-defects-data-models-and-validation\/","title":{"rendered":"How to train AI to detect defects: data, models, and validation"},"content":{"rendered":"\n

Automated inspection<\/strong> using artificial intelligence<\/strong><\/a> is transforming quality in industrial processes<\/strong>. From defects in metal surfaces to dimensional errors in plastic parts, training AI to detect faults requires much more than a neural network: it involves building a robust system based on quality data, well-selected models, and a rigorous validation process. <\/p>\n\n

In this article, we analyze the key steps for developing an effective AI-based <\/strong>defect detection solution, with direct applications in industrial automation<\/strong>, machine vision<\/strong>, and robotics<\/strong>. <\/p>\n\n

Data collection and preparation<\/strong> <\/h2>\n\n

Any AI model <\/strong>is only as good as the data it is trained on. In the case of defect detection using computer vision<\/a><\/strong>, large volumes of correctly labeled visual data are required: images of parts in normal conditions and with various defects (cracks, burrs, stains, deformations, etc.). <\/p>\n\n

It is essential to balance the database to avoid bias: often there are few samples of defects compared to thousands of correct parts. To correct this, techniques such as the following can be applied: <\/p>\n\n