Abstract
Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.
Highlights
Belt conveyor is one of the most important transportation equipment in the field of bulk material transportation, widely used in coal mines, docks, ports, chemical industries, and other fields
The work of this paper mainly focuses on improving the detection speed of conveyor belt damage based on deep learning method, which is to be realized through the lightweight of target detection network
Aiming at the problem of conveyor belt damage detection, the paper proposed a detection method based on a lightweight neural network, which aims to increase the detection speed to meet the development needs of high-speed belt conveyors, to match the cameras with high frame rate, making the signal processing speed more real-time
Summary
Belt conveyor is one of the most important transportation equipment in the field of bulk material transportation, widely used in coal mines, docks, ports, chemical industries, and other fields. The current research work on the intelligent development of belt conveyors is focused on: energy-efficient equipment or energy-saving technology for belt conveyors, especially load-based energy-saving speed regulation systems for belt conveyors [5,6,7,8,9,10,11]; expert-based fault diagnosis systems based on noise and vibration monitoring [12,13]; running state detection technology based on vision and image processing: including deviation monitoring [14], belt speed monitoring [15], material flow detection [16], foreign body identification [17], tear detection, roller temperature monitoring [18], etc. This article focuses on the visual monitoring of mining conveyor belt damage
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