Edge-Enhance YOLO for Steel Surface Defect Detection.

Surface defect detection is an important task for quality assurance in steel manufacturing. Although YOLO-style detectors are widely used due to their strong performance, they often struggle to accurately localize edge-dominant defects such as crazing and fine cracks. This limitation arises because such defects exhibit weak feature representations. In addition, their high-frequency structural details are progressively degraded during repeated downsampling. To address this issue, a YOLO-based detection framework named EDEN-YOLO is proposed. It incorporates an in-place Edge-Enhance module into the YOLOv8 baseline to improve structural sensitivity. Specifically, a Local Feature Enhancement (LFE) module is designed to capture edge-sensitive patterns. A Gated Module is further introduced to perform spatially selective recalibration of backbone features. This design enhances edge responses while suppressing noise. Experiments on the NEU-DET benchmark demonstrate the effectiveness of the proposed method. EDEN-YOLO achieves 80.5% mAP@0.5 on NEU-DET, showing an improvement over the reproduced YOLOv8 baseline while introducing a moderate increase in model complexity by 0.52M parameters and 1.3 GFLOPs. A supplementary evaluation on the GC10-DET dataset shows that EDEN-YOLO achieves 65.2% mAP@0.5, compared with 61.0% for the reproduced YOLOv8 baseline. The qualitative results show that the proposed module produces more compact feature responses.