Industrial surface defect detection is crucial for quality control in manufacturing, yet remains challenging due to the small scale, low contrast, and texture variability of defects. While YOLOv8n offers high inference speed and efficiency, its accuracy is limited by insufficient feature representation and inadequate data diversity. This paper proposes a detection framework integrating Channel–Spatial Modulation Attention (CASM) and Small-Scale Grid Texture Shuffling Augmentation (SG-TSA) into YOLOv8n to improve detection performance without sacrificing efficiency. CASM introduces a parallel channel–spatial attention structure with adaptive fusion to better capture fine-grained defect features, while SG-TSA increases sample diversity by introducing realistic texture perturbations within defect regions. Experiments on the NEU-DET dataset show that our method improves mAP@0.5:0.95 by 3.01% and mAP@0.5 by 2.84% over baseline YOLOv8n. These results highlight the importance of architecture-specific optimization for lightweight detectors in industrial scenarios.
An et al. (Thu,) studied this question.