Restricted accessResearch articleFirst published online 2025
DEMA-YOLO: An Effective Industrial Defect Detection Algorithm Based on a Double-flow Edge Detail Enhancement Module and a Multi-scale Attention Mechanism
Accurate industrial defect detection is crucial for industrial quality control, yet challenges such as complex backgrounds and small targets hinder performance. This study presents DEMA-YOLO, an efficient neural network based on the YOLOv10 architecture, integrating a dual-stream edge detail enhancement module and a multiscale attention mechanism. These components enhance feature representation and fine-grained perception. An improved NWD loss further stabilizes small object detection. Extensive experiments on PCB board, NEU-DET, and mixed-type WM38 datasets show that DEMA-YOLO achieves mAP scores of 93.9%, 90.5%, and 98.7%, respectively, outperforming YOLOv10s by 6.7% and 0.9% on PCB and NEU-DET. In the mixed-type WM38 dataset, while accuracy is comparable, DEMA-YOLO reduces parameters by 0.3M and increases the inference speed by 5.8 FPS. Inference speeds reach 119.2, 112.0, and 134.2 FPS on the three datasets, respectively. These results demonstrate the model’s effectiveness and efficiency in deep learning-based computer vision for industrial defect detection.
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