Efficient minor defects detection on steel surface via res-attention and position encoding

Abstract

Impurities and complex manufacturing processes result in many minor, dense steel defects. This situation requires precise defect detection models for effective protection. The single-stage model (based on YOLO) is a popular choice among current models, renowned for its computational efficiency and suitability for real-time online applications. However, existing YOLO-based models often fail to detect small features. To address this issue, we introduce an efficient steel surface defect detection model in YOLOv7, incorporating a feature preservation block (FPB) and location awareness feature pyramid network (LAFPN). The FPB uses shortcut connections that allow the upper layers to access detailed information directly, thus capturing minor defect features more effectively. Furthermore, LAFPN integrates coordinate data during the feature fusion phase, enhancing the detection of minor defects. We introduced a new loss function to identify and locate minor defects accurately. Extensive testing on two public datasets has demonstrated the superior performance of our model compared to five baseline models, achieving an impressive 80.8 mAP on the NEU-DET dataset and 72.6 mAP on the GC10-DET dataset.