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

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 vi...

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Published inSensors (Basel, Switzerland) Vol. 22; no. 18; p. 6851
Main Authors Chen, Yiming, Sun, Xu, Xu, Liang, Ma, Sencai, Li, Jun, Pang, Yusong, Cheng, Gang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
MDPI
Subjects
Algorithms
belt conveyor
Belt conveyors
Coal mining
Decomposition
Deep learning
Image processing
KinD++ algorithm
Light
low-light enhancement
Machine vision
Mean square errors
Methods
Testing
YOLOv4 algorithm
China
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s22186851

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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.
AbstractList 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.
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.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.
Audience Academic
Author Li, Jun
Xu, Liang
Ma, Sencai
Sun, Xu
Chen, Yiming
Pang, Yusong
Cheng, Gang
AuthorAffiliation 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
1 Shandong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang 277000, China
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CitedBy_id crossref_primary_10_3390_s24165251
crossref_primary_10_1109_ACCESS_2023_3317048
crossref_primary_10_1016_j_measurement_2023_112735
crossref_primary_10_1007_s40747_023_01011_9
crossref_primary_10_1007_s11760_024_03046_y
crossref_primary_10_1038_s41598_023_35962_3
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Snippet The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not...
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StartPage 6851
SubjectTerms Algorithms
belt conveyor
Belt conveyors
Coal mining
Decomposition
Deep learning
Image processing
KinD++ algorithm
Light
low-light enhancement
Machine vision
Mean square errors
Methods
Testing
YOLOv4 algorithm
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Title Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment
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