CEC-YOLO: An Improved Steel Defect Detection Algorithm Based on YOLOv5

Steel surface defect detection is a critical step in the steel manufacturing process and an important guarantee to improve the quality of steel production. However, the contrast of steel surface defect images is poor, the defects are complex and irregularly distributed, and the existing steel surface defect detection algorithms have some problems, such as low detection accuracy and slow detection speed. In this paper, a steel surface defect detection algorithm named CEC-YOLO is proposed. Building upon YOLOv5, we first replaced the C3 module with the C2f and C2f-DSC modules to enhance the detection accuracy of slender and weak local structural features while maintaining a lightweight model. Additionally, we introduce an Explicit Vision Center (EVC) block in the backbone network to capture global remote dependencies among top-level features and extract feature representations of local corner regions for comprehensive analysis. Finally, we integrate Coordinate Attention (CA) into the detection head to fuse feature information effectively and improve the network's ability to locate defects accurately. We conducted extensive experiments on two real-world datasets: NEU-DET and Micro surface defect database. The experimental results demonstrate that the improved model achieves average accuracies of 80.8% and 91.3%, respectively, at mAP@IoU = 0.5, surpassing the baseline by 4.6% and 5.5%. Furthermore, comparative experiments highlight the superiority of our improved model over other state-of-the-art approaches.