ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution.

Abstract

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network's robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.