Steel Defect Detection: From Theory to Practice

Train a Surface Defect Detection Model Based on YOLO

Understanding the theory behind CNNs and YOLO was only half of the journey. The real question for me was how these ideas can be applied in a real industrial setting.

Steel defect detection turned out to be a good test case.

Unlike natural images, industrial images are often repetitive, visually subtle, and far less forgiving. Defects may occupy only a small region of the surface, while the background remains nearly uniform. Minor variations in texture can easily blur the boundary between “normal” and “defective”.

From a modeling perspective, steel defect detection fits well into the object detection paradigm:

Please read the full article in my Medium stories. Here is the link: 👉 Steel Defect Detection: From Theory to Practice