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

Journal article (2022)

Authors

Yiming Chen Shandong Zhongheng Optoelectronic Technology Co.
Xu Sun China University of Mining and Technology
Liang Xu China University of Mining and Technology
Sencai Ma China University of Mining and Technology
Jun Li China University of Mining and Technology
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Gang Cheng China University of Mining and Technology
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Yiming Chen, Xu Sun, Liang Xu, Sencai Ma, Jun Li, Y. Pang, Gang Cheng
DOI: https://doi.org/10.3390/s22186851
More Info
expand_more

Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

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.