{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:13:41Z","timestamp":1779203621476,"version":"3.51.4"},"reference-count":19,"publisher":"Fuji Technology Press Ltd.","issue":"3","funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CUG240634"],"award-info":[{"award-number":["CUG240634"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B17040"],"award-info":[{"award-number":["B17040"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,5,20]]},"abstract":"<jats:p>Static weighting process is a critical component of industrial logistics, yet the prevailing approach to train positioning relies on manual visual guidance. This method is suboptimal, resulting in low efficiency and safety risks in harsh environments. To address these limitations, this study proposes a computer-vision-based positioning system for train static weighing. Industrial cameras are utilized to capture real-time images at both ends of the weighbridge, and a lightweight YOLO model is employed to detect couplers, wheels, and endpoints with an accuracy rate exceeding 98%. The process of camera calibration and the implementation of a pixel-world mapping model, founded on the principle of perspective transformation, is instrumental in the computation of the carriage\u2019s actual positional deviation, in turn, serving as a critical guide for the execution of precise parking maneuvers. Empirical evidence from practical deployment indicates that the system enhances weighing efficiency by 75%, while concurrently facilitating fully unmanned, safer operations on site.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0912","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:02:09Z","timestamp":1779202929000},"page":"912-920","source":"Crossref","is-referenced-by-count":0,"title":["Design and Implementation of a YOLO-Based Visual Positioning System for Train Static Weighing"],"prefix":"10.20965","volume":"30","author":[{"given":"Zhenmin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hunan Valin Lianyuan Iron & Steel Co., Ltd., Huangnitang, Loudi, Hunan 417009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5268-2731","authenticated-orcid":true,"given":"Jundong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7027-2851","authenticated-orcid":true,"given":"Yawu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0912-1","doi-asserted-by":"crossref","unstructured":"Z. Chen, Q. Wang, K. Yang, T. Yu, J. Yao, Y. Liu, P. Wang, and Q. He, \u201cDeep learning for the detection and recognition of rail defects in ultrasound B-scan images,\u201d Transportation Research Record, Vol.2675, No.11, pp. 888-901, 2021. https:\/\/doi.org\/10.1177\/03611981211021547","DOI":"10.1177\/03611981211021547"},{"key":"key-10.20965\/jaciii.2026.p0912-2","doi-asserted-by":"crossref","unstructured":"Y. Tang, and Y. Qian, \u201cA boiler heating surface overtemperature high-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment,\u201d High-speed Railway, Vol.2, No.1, pp. 42-50, 2024. https:\/\/doi.org\/10.1016\/j.hspr.2024.02.001","DOI":"10.1016\/j.hspr.2024.02.001"},{"key":"key-10.20965\/jaciii.2026.p0912-3","doi-asserted-by":"crossref","unstructured":"V. Skrickij, D. Shi, S. Ricci, L. Rizzetto, and G. Bureika, \u201cVisual measurement system for wheel-rail lateral position evaluation,\u201d Sensors, Vol.21, No.4, Article No.1297, 2021. https:\/\/doi.org\/10.3390\/s21041297","DOI":"10.3390\/s21041297"},{"key":"key-10.20965\/jaciii.2026.p0912-4","doi-asserted-by":"crossref","unstructured":"J. Hu, P. Qiao, H. Lv, L. Yang, A. Ouyang, Y. He, and Y. Liu, \u201cHigh speed railway fastener defect detection by Using improved YOLOx-nano model,\u201d Sensors, Vol.22, No.21, Article No.8399, 2022. https:\/\/doi.org\/10.3390\/s22218399","DOI":"10.3390\/s22218399"},{"key":"key-10.20965\/jaciii.2026.p0912-5","doi-asserted-by":"crossref","unstructured":"J. Wu, \u201cTraffic sign detection in autonomous driving: Optimization choices for YOLO models,\u201d Proc. of the 5th Int. Conf. on Advances in Electrical Engineering and Computer Applications, 2024. https:\/\/doi.org\/10.1109\/AEECA62331.2024.00097","DOI":"10.1109\/AEECA62331.2024.00097"},{"key":"key-10.20965\/jaciii.2026.p0912-6","doi-asserted-by":"crossref","unstructured":"S. Li and H. Yoon, \u201cVehicle localization in 3D world coordinates using single camera at traffic intersection,\u201d Sensors, Vol.23, No.7, Article No.3661, 2023. https:\/\/doi.org\/10.3390\/s23073661","DOI":"10.3390\/s23073661"},{"key":"key-10.20965\/jaciii.2026.p0912-7","doi-asserted-by":"crossref","unstructured":"X. Li, Q. Wang, X. Yang, K. Wang, and H. Zhang, \u201cTrack fastener defect detection model based on improved YOLOv5s,\u201d Sensors, Vol.23, No.14, Article No.6457, 2023. https:\/\/doi.org\/10.3390\/s23146457","DOI":"10.3390\/s23146457"},{"key":"key-10.20965\/jaciii.2026.p0912-8","doi-asserted-by":"crossref","unstructured":"L. Kang, Z. Lu, L. Meng, and Z. Gao, \u201cMulti-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition,\u201d Expert Systems with Applications, Vol.237, Part B, 2024. https:\/\/doi.org\/10.1016\/j.mechmachtheory.2022.105090","DOI":"10.1016\/j.mechmachtheory.2022.105090"},{"key":"key-10.20965\/jaciii.2026.p0912-9","doi-asserted-by":"crossref","unstructured":"Y. Huang, Q. Chai, and W. Wang, \u201cModified YOLO network for symptom detection in panoramic oral roentgenogram,\u201d Proc. of the 43rd Chinese Control Conf., Kun Ming, 2024. https:\/\/doi.org\/10.23919\/CCC63176.2024.10661828","DOI":"10.23919\/CCC63176.2024.10661828"},{"key":"key-10.20965\/jaciii.2026.p0912-10","doi-asserted-by":"crossref","unstructured":"S. Dadjouy and H. Sajedi, \u201cGallbladder cancer detection in ultrasound images based on YOLO and faster R-CNN,\u201d Proc. of the 10th Int. Conf. on Artificial Intelligence and Robotics, 2024. https:\/\/doi.org\/10.1109\/QICAR61538.2024.10496645","DOI":"10.1109\/QICAR61538.2024.10496645"},{"key":"key-10.20965\/jaciii.2026.p0912-11","doi-asserted-by":"crossref","unstructured":"Z. Zhai, H. Sun, J. Zhao, Z. Dong, S. He, and H. Zhao, \u201cDisplacement generating module based end-to-end micro-expression recognition network,\u201d 2021 IEEE Int. Conf. on Systems, Man, and Cybernetics, 2021. https:\/\/doi.org\/10.1109\/SMC52423.2021.9658691","DOI":"10.1109\/SMC52423.2021.9658691"},{"key":"key-10.20965\/jaciii.2026.p0912-12","doi-asserted-by":"crossref","unstructured":"D. Yang, M. Solihin, L. Ardiyanto, Y. Zhao, W. Li, B. Cai, and C. Chen, \u201cA streamlined approach for intelligent ship object detection using EL-YOLO algorithm,\u201d Scientific Reports, Vol.14, pp. 15254-15270, 2024. https:\/\/doi.org\/10.1038\/s41598-024-64225-y","DOI":"10.1038\/s41598-024-64225-y"},{"key":"key-10.20965\/jaciii.2026.p0912-13","doi-asserted-by":"crossref","unstructured":"Y. Luo, X. Wang, Y. Liao, Q. Fu, C. Shu, Y. Wu, and Y. He, \u201cA review of homography estimation: Advances and Challenges,\u201d Electronics, Vol.12, No.24, Article No.4977, 2023. https:\/\/doi.org\/10.3390\/electronics12244977","DOI":"10.3390\/electronics12244977"},{"key":"key-10.20965\/jaciii.2026.p0912-14","doi-asserted-by":"crossref","unstructured":"S. Ranjan, F. Marco, Q. Rizwan, B. Chetan, N. Upesh, and P. Alwin, \u201cYOLO advances to its genesis: A decadal and comprehensive review of the You Only Look Once (YOLO) series,\u201d Artificial Intelligence Review, Vol.58, Article No.274, 2025. https:\/\/doi.org\/10.1007\/s10462-025-11253-3","DOI":"10.1007\/s10462-025-11253-3"},{"key":"key-10.20965\/jaciii.2026.p0912-15","unstructured":"M. Lei, S. Li, Y. Wu, H. Hu, Y. Zhou, X. Zheng, G. Ding, S. Du, Z. Wu, and Y. Gao, \u201cYOLOv13: Real-time object detection with hypergraph-enhanced adaptive visual perception,\u201d arXiv:2506.17733, 2025. https:\/\/doi.org\/10.48550\/arXiv.2506.17733"},{"key":"key-10.20965\/jaciii.2026.p0912-16","doi-asserted-by":"crossref","unstructured":"Z. Zhang, \u201cA flexible new technique for camera calibration,\u201d IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.22, No.11, pp. 1330-1334, 2000. https:\/\/doi.org\/10.1109\/34.888718","DOI":"10.1109\/34.888718"},{"key":"key-10.20965\/jaciii.2026.p0912-17","doi-asserted-by":"crossref","unstructured":"Y. Feng, J. Huang, S. Du, S. Ying, J.-H. Yong, Y. Li, G. Ding, R. Ji, and Y. Gao, \u201cHyper-YOLO: When visual object detection meets hypergraph computation,\u201d IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.47, No.4, pp. 2388-2401, 2025. https:\/\/doi.org\/10.1109\/TPAMI.2024.3524377","DOI":"10.1109\/TPAMI.2024.3524377"},{"key":"key-10.20965\/jaciii.2026.p0912-18","doi-asserted-by":"crossref","unstructured":"S. Ruan, C. Zhan, B. Liu, and Q. Wan, \u201cSurface defect detection on bolt surface using a real-time fine-tuned YOLOv6 model,\u201d J. of Big Data, Vol.12, No.1, pp. 226-252, 2025. https:\/\/doi.org\/10.1186\/s40537-025-01226-2","DOI":"10.1186\/s40537-025-01226-2"},{"key":"key-10.20965\/jaciii.2026.p0912-19","unstructured":"X. Yang, Y. Chen, L. Wu, and J. Zhao, \u201cYOLO-VMTC: Enhancing steel surface defect detection with a lightweight and context-aware deep learning approach,\u201d J. of Electrical Systems, Vol.20, No.4, pp. 6096-6108, 2024."}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=jacii003000030022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:05:20Z","timestamp":1779203120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii003000030912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":19,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,5,20]]},"published-print":{"date-parts":[[2026,5,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2026.p0912","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"value":"1883-8014","type":"electronic"},{"value":"1343-0130","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,20]]}}}