Real-time conveyor belt damage detection method based on improved YOLO

Abstract To enhance the real-time performance and accuracy of damage detection in conveyor belts with poor image quality, insufficient sample feature information, and numerous scattered minor damages, we propose an improved visual detection method based on YOLOv5, incorporating Improved Depth-Separable Convolution (IDSC) and Scale Sequence Feature Fusion (SSFF). The IDSC method significantly reduces network parameters, achieving a lightweight model while maintaining detection accuracy, whereas the SSFF method effectively integrates spatial and multi-scale feature information, enhancing the identification of minor damage. Compared to existing methods, our approach provides a superior balance between detection speed and accuracy. The proposed IDSC-SSFF model achieves a mean average precision (mAP) of 95.65% while maintaining an inference speed of 139.1 FPS, meeting the stringent requirements of real-time defect detection in industrial environments.