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

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

Chen, Yiming (Shandong Zhongheng Optoelectronic Technology Co.)
Sun, Xu (China University of Mining and Technology)
Xu, Liang (China University of Mining and Technology)
Ma, Sencai (China University of Mining and Technology)
Li, Jun (China University of Mining and Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)
Cheng, Gang (China University of Mining and Technology)

2022

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.

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

http://resolver.tudelft.nl/uuid:13d00ad1-4c3a-4b13-a4c3-cf77e8ec4a87

https://doi.org/10.3390/s22186851

1424-8220

Sensors, 22 (18)

Institutional Repository

journal article

© 2022 Yiming Chen, Xu Sun, Liang Xu, Sencai Ma, Jun Li, Y. Pang, Gang Cheng