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LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion
Huan Zhao1, Fang Wan1, Guangbo Lei1
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|July 29, 2023
Summary
This study introduces LSD-YOLOv5, a lightweight model for detecting steel strip surface defects. It significantly reduces model size and boosts detection speed while enhancing accuracy, optimizing quality control in metallurgy.
Area of Science:
- Metallurgical quality control
- Computer vision for industrial inspection
Background:
- Accurate detection of surface defects in metallic materials is vital for quality control.
- Existing defect detection models often suffer from large parameter counts and suboptimal detection rates.
Purpose of the Study:
- To develop a lightweight and efficient model for recognizing surface damage on steel strips.
- To improve upon the performance of existing defect detection methods, specifically YOLOv5s.
Main Methods:
- Proposed a novel lightweight recognition model, LSD-YOLOv5, incorporating a shallow feature enhancement module.
- Integrated Coordinate Attention mechanism within MobileNetV2 bottleneck and introduced a smaller Bi-directional Feature Pyramid Network (BiFPN-S).
- Utilized Soft-DIoU-NMS algorithm for improved recognition efficiency in overlapping target scenarios.
Main Results:
- LSD-YOLOv5 achieved a 61.5% reduction in model parameters compared to YOLOv5s.
- Demonstrated a 28.7% improvement in detection speed.
- Increased recognition accuracy by 2.4% while maintaining a lightweight structure.
Conclusions:
- LSD-YOLOv5 offers an optimal balance between detection accuracy, speed, and model size for steel strip surface defect detection.
- The model presents a significant advancement for automated quality control in metallurgical applications.
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