Machine vision based damage detection for conveyor belt safety using fusion knowledge distillation

Guo, Xiaoqiang and Liu, Xinhua and Gardoni, Paolo and Glowacz, Adam and Królczyk, Grzegorz and Incecik, Atilla and Li, Zhixiong (2023) Machine vision based damage detection for conveyor belt safety using fusion knowledge distillation. Alexandria Engineering Journal, 71. pp. 161-172. ISSN 1110-0168 (https://doi.org/10.1016/j.aej.2023.03.034)

[thumbnail of Guo-etal-AEJ-2023-Machine-vision-based-damage-detection-for-conveyor]
Preview
 Text. Filename: Guo_etal_AEJ_2023_Machine_vision_based_damage_detection_for_conveyor.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (2MB)| Preview

Abstract

A belt conveyor system is one of the essential equipment in coal mining. The damages to conveyor belts are hazardous because they would affect the stable operation of a belt conveyor system whilst impairing the coal mining efficiency. To address these problems, a novel conveyor belt damage detection method based on CenterNet is proposed in this paper. The fusion of feature-wise and response-wise knowledge distillation is proposed, which balances the performance and size of the proposed deep neural network. The Fused Channel-Spatial Attention is proposed to compress the latent feature maps efficiently, and the Kullback-Leibler divergence is introduced to minimize the distribution distance between student and teacher networks. Experimental results show that the proposed lightweight object detection model reaches 92.53% mAP and 65.8 FPS. The proposed belt damage detection system can detect conveyor belt damages efficiently and accurately, which indicates its high potential to deploy on end devices.

CORE (COnnecting REpositories)