Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment

Yiming Chen, Xu Sun, Liang Xu, Sencai Ma, Jun Li, Yusong Pang, Gang Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the YOLOv4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective.

Original languageEnglish
Article number6851
Number of pages20
JournalSensors
Volume22
Issue number18
DOIs
Publication statusPublished - 2022

Keywords

  • belt conveyor
  • KinD++ algorithm
  • low-light enhancement
  • machine vision
  • YOLOv4 algorithm

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