{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:58:05Z","timestamp":1782403085008,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.<\/jats:p>","DOI":"10.3390\/a12100205","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:03:15Z","timestamp":1569553395000},"page":"205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Chan","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, J., Tian, C., Xing, L., Bian, Z., and Miao, X. 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