Tear detection of conveyor belt based on machine vision

Honglei Wang; Jiacheng Li; Taihui Wu; Xiaoming Liu; Junsheng Zhang

doi:10.1117/12.2625250

12 December 2021 Tear detection of conveyor belt based on machine vision

Honglei Wang, Jiacheng Li, Taihui Wu, Xiaoming Liu, Junsheng Zhang

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 121270J (2021) https://doi.org/10.1117/12.2625250
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

Abstract

Belt conveyor is one of the main transportation equipment in coal mine. The belt is easy to tear in production. If the tear damage of belt surface cannot be detected in time, it may lead to serious production accidents. In this paper, a belt tear detection method based on industrial camera monitoring is proposed, which can identify the belt tear in time and output the quantitative evaluation result. After filtering the image, Canny edge detection algorithm is used to identify the tear region. A sliding window is used to evaluate the degree of damage area and further determine the control of belt conveyor. Experiments show that the average processing time of a single frame image is 0.4s, which can meet the needs of real-time detection in the production.

Citation Download Citation

Honglei Wang, Jiacheng Li, Taihui Wu, Xiaoming Liu, and Junsheng Zhang "Tear detection of conveyor belt based on machine vision", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 121270J (12 December 2021); https://doi.org/10.1117/12.2625250

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