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Volume 22
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10.3390/s22186851
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Article

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

by
Yiming Chen
1,
Xu Sun
2,
Liang Xu
2,
Sencai Ma
2,
Jun Li
2,
Yusong Pang
3 and
Gang Cheng
2,*
1
Shandong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang 277000, China
2
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
3
Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(18), 6851; https://doi.org/10.3390/s22186851
Submission received: 6 August 2022 / Revised: 4 September 2022 / Accepted: 7 September 2022 / Published: 10 September 2022
(This article belongs to the Section Environmental Sensing)
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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.
Keywords: belt conveyor; machine vision; KinD++ algorithm; YOLOv4 algorithm; low-light enhancement belt conveyor; machine vision; KinD++ algorithm; YOLOv4 algorithm; low-light enhancement

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MDPI and ACS Style

Chen, Y.; Sun, X.; Xu, L.; Ma, S.; Li, J.; Pang, Y.; Cheng, G. Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment. Sensors 2022, 22, 6851. https://doi.org/10.3390/s22186851

AMA Style

Chen Y, Sun X, Xu L, Ma S, Li J, Pang Y, Cheng G. Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment. Sensors. 2022; 22(18):6851. https://doi.org/10.3390/s22186851

Chicago/Turabian Style

Chen, Yiming, Xu Sun, Liang Xu, Sencai Ma, Jun Li, Yusong Pang, and Gang Cheng. 2022. "Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment" Sensors 22, no. 18: 6851. https://doi.org/10.3390/s22186851

APA Style

Chen, Y., Sun, X., Xu, L., Ma, S., Li, J., Pang, Y., & Cheng, G. (2022). Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment. Sensors, 22(18), 6851. https://doi.org/10.3390/s22186851

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