{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:12:43Z","timestamp":1779311563730,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22186851","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"6851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment"],"prefix":"10.3390","volume":"22","author":[{"given":"Yiming","family":"Chen","sequence":"first","affiliation":[{"name":"Shandong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang 277000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sencai","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-3436","authenticated-orcid":false,"given":"Yusong","family":"Pang","sequence":"additional","affiliation":[{"name":"Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107856","DOI":"10.1016\/j.measurement.2020.107856","article-title":"Infrared spectrum analysis method for detection and early warning of longitudinal tear of mine conveyor belt","volume":"165","author":"Yang","year":"2020","journal-title":"Measurement"},{"key":"ref_2","unstructured":"Guo, Y., Zhang, Y., Li, F., Wang, S., and Cheng, G. 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