Abstract
Steel defect detection is a crucial aspect of steel production and quality control. Therefore, focusing on small-scale defects in complex production environments remains a critical challenge. To address this issue, we propose an innovative perception-efficient network designed for the fast and accurate detection of multi-scale surface defects. First, we introduce the Defect Capture Path Aggregation Network, which enhances the feature fusion network's ability to learn multi-scale representations. Second, we design a Perception-Efficient Head (PEHead) to effectively mitigate local aliasing issues, thereby reducing the occurrence of missed detections. Finally, we propose the Receptive Field Extension Module (RFEM) to strengthen the backbone network's ability to capture global features and address extreme aspect ratio variations. These three modules can be seamlessly integrated into the YOLO framework. The proposed method is evaluated on three public steel defect datasets: NEU-DET, GC10-DET, and Severstal. Compared to the original YOLOv8n model, PEYOLO achieves mAP50 improvements of 3.5%, 9.1%, and 3.3% on these datasets, respectively. While maintaining similar detection accuracy, PEYOLO retains a high inference speed, making it suitable for real-time applications. Experimental results demonstrate that the proposed PEYOLO can be effectively applied to real-time steel defect detection.