Research And Application Of Foreign Object Recognition Method On Belt Conveyor

Belt conveyor is the key equipment of mine transportation system.Due to the complex underground mining environment,a large number of foreign objects often appear in the process of belt transportation,which may easily lead to abnormal conditions such as belt tearing and blockage of the transfer.However,in the existing methods,the manual detection efficiency is low,and the safety hazard is large;the radar detection cost is high,and the maintenance is difficult;the metal detector is difficult to deploy and has few detection categories.In response to the above problems,this paper studied the foreign object recognition technology based on deep learning,designed a belt conveyor foreign object recognition system with high precision and real-time edge computing on an embedded platform,and conducted experiments and performance analysis on the industrial site.The main work of this paper is as follows:(1)Aiming at the problems that the existing target detection algorithm is not efficient for foreign object feature extraction and prone to false detection,a foreign object recognition algorithm based on YOLOv5 is proposed.First,the foreign matter data of ore flow is collected,and the samples are expanded and labeled by the data enhancement method based on background segmentation to construct a foreign matter data set.Then,the backbone network based on CBAM attention mechanism and Ghost module,the feature fusion network based on weighted bidirectional feature pyramid and the loss function based on Focal Loss are studied to improve the feature extraction efficiency of the model in complex backgrounds.Finally,Kmeans algorithm is used to initialize anchors box parameters,the foreign object recognition model YOLOv5l＿GC is trained,and the effectiveness of the improved algorithm is verified on the foreign object data set.(2)In view of the problems that the above models have a large amount of parameters and are difficult to deploy on embedded platforms,network pruning and knowledge distillation algorithms are studied.First,the multi-layer network channel pruning algorithm is used to prune YOLOv5l＿GC,and the redundant parameters of the model are eliminated.Then,the knowledge distillation algorithm is studied to make up for the loss of accuracy caused by model compression.Finally,the effectiveness of the model compression algorithm is verified on the NVIDIA Jetson Xavier NX embedded platform.(3)According to the actual needs of the industrial site,a set of belt conveyor foreign body identification system has been developed.By processing and analyzing the monitoring video stream,it can identify the belt foreign body in real time and send an alarm signal.The results show that the system realized high-precision real-time edge computing on the embedded platform,which is of great significance to the safe and stable operation of the mine transportation system.