{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T21:17:59Z","timestamp":1787606279516,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Key Research and Development Program Project","award":["2020ZDLGY04-06"],"award-info":[{"award-number":["2020ZDLGY04-06"]}]},{"name":"Shaanxi Provincial Key Research and Development Program Project","award":["2023KXJ-249"],"award-info":[{"award-number":["2023KXJ-249"]}]},{"name":"Qin Chuang Yuan \u201cScientist + Engineer\u201d team construction project","award":["2020ZDLGY04-06"],"award-info":[{"award-number":["2020ZDLGY04-06"]}]},{"name":"Qin Chuang Yuan \u201cScientist + Engineer\u201d team construction project","award":["2023KXJ-249"],"award-info":[{"award-number":["2023KXJ-249"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dynamic weighing systems, an advanced technology for traffic management, are designed to measure the weight of moving vehicles without obstructing traffic flow. These systems play a critical role in monitoring freight vehicle overloading, collecting weight-based tolls, and assessing the structural health of roads and bridges. However, due to the complex road traffic environment in real-world applications of dynamic weighing systems, some vehicles cannot be accurately weighed, even though precise parameter calibration was conducted prior to the system\u2019s official use. The variation in driving behaviors among different drivers contributes to this issue. When different types and sizes of vehicles pass through the dynamic weighing area simultaneously, changes in the vehicles\u2019 motion states are the main factors affecting weighing accuracy. This study proposes an improved SSD vehicle detection model to address the high sensitivity to vehicle occlusion and frequent vehicle ID changes in current multi-target tracking methods. The goal is to reduce detection omissions caused by vehicle occlusion. Additionally, to obtain more stable trajectory and speed data, a Gaussian Smoothing Interpolation (GSI) method is introduced into the DeepSORT algorithm. The fusion of dynamic weighing data is used to analyze the impact of changes in vehicle size and motion states on weighing accuracy, followed by compensation and experimental validation. A compensation strategy is implemented to address the impact of speed fluctuations on the weighing accuracy of vehicles approximately 12.5 m in length. This is completed to verify the feasibility of the compensation method proposed in this paper, which is based on vehicle information. A dataset containing vehicle length, width, height, and speed fluctuation information in the dynamic weighing area is constructed, followed by an analysis of the key factors influencing dynamic weighing accuracy. Finally, the improved dynamic weighing model for extracting vehicle motion state information is validated using a real dataset. The results demonstrate that the model can accurately detect vehicle targets in video footage and shows strong robustness under varying road illumination conditions.<\/jats:p>","DOI":"10.3390\/s25103105","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T10:27:41Z","timestamp":1747218461000},"page":"3105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Study of Multi-Objective Tracking Method to Extract Multi-Vehicle Motion Tracking State in Dynamic Weighing Region"],"prefix":"10.3390","volume":"25","author":[{"given":"Yan","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3027-223X","authenticated-orcid":false,"given":"Shuanfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maoquan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1109\/TITS.2020.3014013","article-title":"Vehicle detection and tracking in adverse weather using a deep learning framework","volume":"22","author":"Hassaballah","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hassani, S., and Dackermann, U. (2023). A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring. Sensors, 23.","DOI":"10.3390\/s23042204"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/TNNLS.2020.3047335","article-title":"Dynamically weighted balanced loss: Class imbalanced learning and confidence calibration of deep neural networks","volume":"33","author":"Fernando","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1177\/0361198120937977","article-title":"Assessment of factors affecting measurement accuracy for high-quality weigh-in-motion sites in the long-term pavement performance database","volume":"2674","author":"Haider","year":"2020","journal-title":"Transp. Res. Rec."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.apm.2021.12.005","article-title":"Dynamic response analysis of vehicle and asphalt pavement coupled system with the excitation of road surface unevenness","volume":"104","author":"Ma","year":"2022","journal-title":"Appl. Math. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1109\/TMM.2022.3145663","article-title":"Multi-modality sensing and data fusion for multi-vehicle detection","volume":"25","author":"Roy","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1007\/s10489-021-02491-3","article-title":"YOLOv3-MT: A YOLOv3 using multi-target tracking for vehicle visual detection","volume":"52","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4104","DOI":"10.1109\/TVT.2020.2976095","article-title":"Multi-frame track-before-detect algorithm for maneuvering target tracking","volume":"69","author":"Yi","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_11","first-page":"1056","article-title":"Optimized DeepSort-based multi-target tracking of forward vehicles","volume":"55","author":"Jin","year":"2021","journal-title":"J. Zhejiang Univ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"012057","DOI":"10.1088\/1742-6596\/2303\/1\/012057","article-title":"Research on two-way detection of YOLO V5s+Deep Sort road vehicles based on attention mechanism","volume":"2303","author":"Gao","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"209528","DOI":"10.1109\/ACCESS.2020.3024928","article-title":"Multi-target tracking and detection based on hybrid filter algorithm","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/02564602.2020.1740615","article-title":"PCA-based feature reduction for hyperspectral remote sensing image classification","volume":"38","author":"Uddin","year":"2021","journal-title":"IETE Tech. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, C., Fang, Y., Gao, L., Zhang, C., Du, M., and Xu, F. (2024, January 17\u201319). Vehicle and Pedestrian Detection in Urban Traffic Based on SSD-DeepSort. Proceedings of the 2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys), Hangzhou, China.","DOI":"10.1109\/AIoTSys63104.2024.10780475"},{"key":"ref_16","first-page":"545","article-title":"Video face detection based on improved SSD model and target tracking algorithm","volume":"21","author":"Liu","year":"2022","journal-title":"J. Web Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e3734","DOI":"10.1002\/ett.3734","article-title":"An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter","volume":"31","author":"Abbas","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_18","unstructured":"Zhao, H., and Zhai, J. A Cascade Matching Algorithm for Multi-Object Tracking Systems Based on Detection Box Scores. Proceedings of the Chinese Intelligent Systems Conference."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"170461","DOI":"10.1109\/ACCESS.2020.3021508","article-title":"Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review","volume":"8","author":"Aziz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9643","DOI":"10.1007\/s00521-023-08963-6","article-title":"Monocular vehicle speed detection based on improved YOLOX and DeepSORT","volume":"36","author":"Zhang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1007\/s12652-020-02085-w","article-title":"Inception-SSD: An improved single shot detector for vehicle detection","volume":"13","author":"Chen","year":"2022","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, J., Song, C., Song, S., Peng, S., Wang, D., Shao, Y., and Xiao, F. (2020). Front vehicle detection algorithm for smart car based on improved SSD model. Sensors, 20.","DOI":"10.3390\/s20164646"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110785","DOI":"10.1016\/j.patcog.2024.110785","article-title":"Visual multi-object tracking with re-identification and occlusion handling using labeled random finite sets","volume":"156","author":"Nguyen","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_24","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., and Leal-Taix\u00e9, L. (2020). Mot20: A benchmark for multi object tracking in crowded scenes. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., and Meng, H. (2023). StrongSORT: Make DeepSORT great again. arXiv.","DOI":"10.1109\/TMM.2023.3240881"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TIV.2020.2991952","article-title":"Attention based vehicle trajectory prediction","volume":"6","author":"Messaoud","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_27","first-page":"32","article-title":"An approach for image annotation automatization for artificial intelligence models learning","volume":"1","author":"Yakovlev","year":"2020","journal-title":"A\u0434a\u043fmu\u0432\u043di Cucme\u043c\u0438 A\u0432mo\u043camu\u0447\u043do\u0433o \u0423\u043fpa\u0432\u043b\u0456\u043d\u043d\u044f"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1007\/s10044-020-00874-9","article-title":"Local binary pattern-based on-road vehicle detection in urban traffic scene","volume":"23","author":"Hassaballah","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/14680629.2019.1614970","article-title":"Effect of vehicle speed and overload on dynamic response of semi-rigid base asphalt pavement","volume":"22","author":"Assogba","year":"2021","journal-title":"Road Mater. Pavement Des."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1007\/s11042-021-11804-0","article-title":"Moving vehicle detection and tracking at roundabouts using deep learning with trajectory union","volume":"81","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.istruc.2022.08.089","article-title":"Machine learning-driven seismic failure mode identification of reinforced concrete shear walls based on PCA feature extraction","volume":"44","author":"Xiong","year":"2022","journal-title":"Structures"},{"key":"ref_32","first-page":"43","article-title":"Impact of implementing GB1589-2016 on highway trucks","volume":"39","author":"Shengyu","year":"2017","journal-title":"J. Compr. Transp."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1177\/0954407020905151","article-title":"Truck mass estimation method based on the on-board sensor","volume":"234","author":"Wang","year":"2020","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_34","unstructured":"Wang, M. (2020). Impact of Operational Speed Characteristics of Heavy Vehicles on High-Speed Highways. [Master\u2019s Thesis, York University]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/10\/3105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:39Z","timestamp":1760031159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/10\/3105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["s25103105"],"URL":"https:\/\/doi.org\/10.3390\/s25103105","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}