LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

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In the field of metallurgy, the timely and accurate detection of surface defects on metallic materials is a crucial quality control ialoral task.However, current defect detection approaches face challenges with large model parameters and low detection rates.To address these issues, this paper proposes a lightweight recognition model for surface damage on steel strips, named LSD-YOLOv5.First, we design a shallow feature enhancement module to replace the first Conv structure in the backbone network.Second, the Coordinate Attention mechanism is introduced into the MobileNetV2 bottleneck structure to maintain the lightweight nature of the model.

Then, we propose a smaller bidirectional feature pyramid network (BiFPN-S) and combine it with Concat operation 2004 honda civic exhaust system for efficient bidirectional cross-scale connectivity and weighted feature fusion.Finally, the Soft-DIoU-NMS algorithm is employed to enhance the recognition efficiency in scenarios where targets overlap.Compared with the original YOLOv5s, the LSD-YOLOv5 model achieves a reduction of 61.5% in model parameters and a 28.7% improvement in detection speed, while improving recognition accuracy by 2.

4%.This demonstrates that the model achieves an optimal balance between detection accuracy and speed, while maintaining a lightweight structure.