An automated defect detection method for optimizing industrial quality inspection

Detecting defects in industrial-quality inspection is an important task, but defect detection remains challenging due to limited size, various types of defects, and imbalanced samples in images. In this paper, we propose a novel automatic defect detection network that efficiently detects defects. We introduce the Automated Sample Assignment (SCID) algorithm, which automatically divides the distribution of positive and negative samples based on similarity scores using the Gaussian mixture model. The Automated Sample Assignment (SCID) algorithm solves the problem of sample imbalance in defect detection networks caused by manually setting the intersection over the union (IOU) threshold and assigning positive and negative labels to samples. Then, instead of the pooling method used by most backbone networks, we use a loss fusion pool named LE-Pool to retain more detailed information. Finally, we use the soft weighted attention mechanism of the detection head (S-head) to identify noise information. Experimental results on multiple defect detection datasets show that the proposed method is superior to other detection methods in terms of accuracy.