Steel surface defect detection based on YOLO V8

The detection of surface defects in steel is a crucial step in ensuring the quality of the steel. Traditional defect detection methods suffer from low accuracy. The complex shapes of steel surface defects and generally small target areas significantly affect the accuracy of steel surface defect detection. To address these issues, this paper proposes an improved steel surface defect detection algorithm based on a target detection model. This is achieved by incorporating a lightweight backbone network, a Prior Attention Mechanism module (CPCA), and redesigning the Neck module to enhance the accuracy of steel defect detection. Finally, the proposed defect detection algorithm is validated using the publicly available steel surface defect detection dataset NEU-DET. Experimental results show that the network model proposed in this paper has good detection accuracy, with an average precision of 79.1%, which is a 5% improvement over the original algorithm.