Longitudinal tear detection method of mine conveyor belt based on improved YOLOv5

Longitudinal tearing of belt conveyor may lead to significant safety accidents. However, existing algorithms suffer from low detection accuracy and poor anti-interference capability. This study proposes a longitudinal tear detection system based on multi-scale feature fusion. The system captures belt images in real-time using linear lasers and high-speed cameras, preprocesses the images using the LoG algorithm to extract key regions, thereby reducing data redundancy, and finally detects tears using the multi-scale feature fusion neural network. In terms of the detection algorithm, the ConvNeXt feature enhancement module is introduced into neural network backbone network to improve the feature extraction ability of the model for minor tear texture. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) is employed in the Neck part to fuse shallow detail texture features, reducing the loss of detail information in deep layers during down-sampling. The experimental results show that the detection accuracy P and average accuracy mean mAP for longitudinal tear fault detection of the improved algorithm reach 96. 34% and 94. 36%, respectively, which is superior to other mainstream detection methods.