A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection

For strip surface defect detection, the key is to achieve reliable detection results with high detection speed. This paper mainly focuses on the ability to distinguish defects with similar optical characteristics, and the balance between detection accuracy and speed. Firstly, the dataset with 2020 pictures containing 6 types of defects was established by the figures inspected in a rolled titanium strip production line. Then, a novel detection model named Yolo-SAGC was proposed by applying two strategies to the fast response Yolo-v5 model. One is to improve the feature recognition capability by combining self-attention and graphic convolution in the head module. The other is to make a thorough slim of the whole network architecture by using slim modules combined with depth-wise separable convolution (i.e., DWconv). Finally, the advancement of this novel detection model was verified by the self-established database. The results demonstrate a significant reduction in cases where detection is missed for the 6 types of defects, dropping from 32.75% to 6.67% when the two strategies are implemented. Notably, the most difficult-to-detect label "Pit" defect shows an 11.9% improvement in average precision with the introduction of self-attentional graphic convolution. Similarly, the densely distributed small target "Little_lightspot" exhibits a 5.0% increase in average precision when DWconv is applied. Furthermore, the mAP@0.5 of Yolo-SAGC is comparable to that of Yolo-v8, while the model parameters are decreased by 48.7% and FPS is increased by 3. These phenomena show the great potential of Yolo-SAGC in industrial applications.