Longitudinal tear detection method of mine conveyor belt based on improved YOLOv5
YU Qing
LUO Minghua
XIANG Liang
YOU Lei
ZHU Xinglin
Abstract：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,pre-processes 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 mean average precision(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.
Keywords：belt conveyorconveying beltlongitudinal tearobject detectionYOLOv5feature fusion
Publication Date：2024-08-10
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1-8 )