|Table of Contents|

[1] Deng Lu, Deng Jiayu, Wang Wei, et al. Identification of the spatiotemporal location of vehicle loads on highway bridges based on multi-view information fusion [J]. Journal of Southeast University (English Edition), 2024, 40 (1): 1-12. [doi:10.3969/j.issn.1003-7985.2024.01.001]

Identification of the spatiotemporal location of vehicle loads on highway bridges based on multi-view information fusion()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2024 1
Research Field:
Traffic and Transportation Engineering
Publishing date:


Identification of the spatiotemporal location of vehicle loads on highway bridges based on multi-view information fusion
Deng Lu1 2 Deng Jiayu1 Wang Wei1 He Wei1 Zhang Longwei3
1College of Civil Engineering, Hunan University, Changsha 410082, China
2Hunan Provincial Key Laboratory for Damage Diagnosis of Engineering Structures, Hunan University, Changsha 410082, China
3College of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
bridge engineering vehicle loads spatiotemporal location multi-view information fusion vehicle axle identification bridge weigh-in-motion bridge health monitoring
To solve the problem that existing methods have difficulty in accurately obtaining the spatiotemporal distribution of vehicle loads on bridges in complicated traffic scenes, a spatiotemporal location identification method for vehicle loads based on multi-view information fusion is proposed. First, the vadYOLO-StrongSORT model is developed to detect and track vehicles simultaneously in a single view. Furthermore, based on image calibration and cross-view vehicle matching, an adaptive weighted least squares method is used for multi-view information fusion to correct the vehicle trajectory. Finally, the spatiotemporal distribution of axle loads is reconstructed by combining vehicle trajectories with axle configurations. The performance of the proposed method under typical traffic conditions is evaluated using model tests. The results show that the multi-view information fusion method significantly improves tracking stability, localization accuracy, and anti-occlusion performance compared with the single view-based vehicle location identification method. In the lane-changing scenes, the highest average localization error of the proposed method is less than 2.0 cm, which is significantly better than the 17.0 cm of the single-view method. In multivehicle occlusion scenes, the proposed method achieves a vehicle capture rate of up to 100%, compared with a maximum of only 72.5% for the single-view method. Meanwhile, vadYOLO-StrongSORT achieves the highest identification accuracy in the experiment compared with other detection and tracking models.


[1] Zhao H W, Ding Y L, Li A Q, et al. Digital modeling of vehicle load-bridge effect and system condition monitoring[J]. Journal of Southeast University(Natural Science Edition), 2022, 52(2): 203-211. DOI:10.3969/j.issn.1001-0505.2022.02.001. (in Chinese)
[2] Zheng J Y, Tang J Y, Zhou Z X, et al. Intelligent cognition of traffic loads on road bridges: From measurement to simulation—A review[J].Measurement, 2022, 200: 111636. DOI: 10.1016/j.measurement.2022.111636.
[3] Zhao K L, Zong H, Zhu Q X, et al. Analysis on vehicle loads on Nanjing Qixiashan Yangtze River Bridge based on long-term field measurement[J]. Journal of Southeast University(Natural Science Edition), 2021, 51(6):979-985. DOI:10.3969/j.issn.1001-0505.2021.06.009. (in Chinese)
[4] Zhang L, Feng D M, Wu G. Dynamic vehicle load identification method based on LSTM network[J]. Journal of Southeast University(Natural Science Edition), 2023, 53(2):187-192. DOI:10.3969/j.issn.1001-0505.2023.02.001. (in Chinese)
[5] Yan J Y, Deng L, He W. Evaluation of existing prestressed concrete bridges considering the randomness of live load distribution factor due to random vehicle loading position[J].Advances in Structural Engineering, 2017, 20(5): 737-746. DOI: 10.1177/1369433216664350.
[6] Moses F. Weigh-in-motion system using instrumented bridges[J].Transportation Engineering Journal of ASCE, 1979, 105(3): 233-249. DOI: 10.1061/tpejan.0000783.
[7] Ren W X, Zuo X H, Wang N B, et al. Review of non-pavement bridge weigh-in-motion[J].China Journal of Highway and Transport, 2014, 27(7): 45-53. DOI:10.19721/j.cnki.1001-7372.2014.07.006. (in Chinese)
[8] Žnidaric A, Lavric I, Kalin J. Free-of-axle detector bridge WIM measurements on short slab bridges in proceedings of the 3rd international WIM conference[C]//Proceedings of the 3rd International WIM Conference. Florida, USA, 2002: 231-239.
[9] Deng L, Shi H, He W, et al. Vehicles’ BWIM based on virtual simply-supported beam method[J]. Journal of Vibration and Shock, 2018, 37(15): 209-215. DOI:10.13465/j.cnki.jvs.2018.15.029. (in Chinese)
[10] Lansdell A, Song W, Dixon B. Development and testing of a bridge weigh-in-motion method considering nonconstant vehicle speed[J].Engineering Structures, 2017, 152: 709-726. DOI: 10.1016/j.engstruct.2017.09.044.
[11] Zhuo Y, An J H, Zhang B, et al. Application of non-constant speed algorithm in bridge weigh-in-motion[J].Highway Engineering, 2020, 45(5): 100-107, 128. DOI:10.19782/j.cnki.1674-0610.2020.05.017. (in Chinese)
[12] Buch N, Velastin S A, Orwell J. A review of computer vision techniques for the analysis of urban traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 920-939. DOI: 10.1109/TITS.2011.2119372.
[13] Peng B, Cai X Y, Zhang Y J, et al. Automatic vehicle detection from UAV videos based on symmetrical frame difference and background block modeling[J]. Journal of Southeast University(Natural Science Edition), 2017, 47(4): 685-690. DOI:10.3969/j.issn.1001-0505.2017.04.010. (in Chinese)
[14] Peng B, Cai X Y, Tang J, et al. Automatic vehicle detection with UAV videos based on modified faster R-CNN[J]. Journal of Southeast University(Natural Science Edition), 2019, 49(6): 1199-1204. DOI:10.3969/j.issn.1001-0505.2019.06.025. (in Chinese)
[15] Chen Z C, Li H, Bao Y Q, et al. Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology[J]. Structural Control and Health Monitoring, 2016, 23(3): 517-534. DOI: 10.1002/stc.1780.
[16] Dan D H, Ge L F, Yan X F. Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision[J].Measurement, 2019, 144: 155-166. DOI: 10.1016/j.measurement.2019.05.042.
[17] Zhang B, Zhou L M, Zhang J. A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision[J].Computer-Aided Civil and Infrastructure Engineering, 2019, 34(6): 471-487. DOI: 10.1111/mice.12434.
[18] Xia Y, Jian X D, Deng L, et al. Research on traffic-video-aided bridge weigh-in-motion approach[J]. China Journal of Highway and Transport, 2021, 34(12): 104-114. DOI:10.19721/j.cnki.1001-7372.2021.12.009. (in Chinese)
[19] Zhao D D, He W, Deng L, et al. Trajectory tracking and load monitoring for moving vehicles on bridge based on axle position and dual camera vision[J]. Remote Sensing, 2021, 13(23): 4868. DOI: 10.3390/rs13234868.
[20] Yang G, Wang P, Han W S, et al. Automatic generation of fine-grained traffic load spectrum via fusion of weigh-in-motion and vehicle spatial-temporal information[J].Computer-Aided Civil and Infrastructure Engineering, 2022, 37(4): 485-499. DOI: 10.1111/mice.12746.
[21] Xu Z F, Wei B, Zhang J. Reproduction of spatial-temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data[J].Structures, 2023, 53: 1476-1488. DOI: 10.1016/j.istruc.2023.05.023.
[22] Dong Y Q, Wang D L, Pan Y, et al.Large field monitoring system of vehicle load on long-span bridge based on the fusion of multiple vision and WIM data[J]. Automation in Construction, 2023, 154: 104985. DOI: 10.1016/j.autcon.2023.104985.
[23] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Vancouver, BC, Canada, 2023: 7464-7475.
[24] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 7132-7141.
[25] Zheng Z H, Wang P, Ren D W, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J].IEEE Transactions on Cybernetics, 2022, 52(8): 8574-8586. DOI: 10.1109/TCYB.2021.3095305.
[26] Zhang Y F, Ren W Q, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J].Neurocomputing, 2022, 506: 146-157. DOI: 10.1016/j.neucom.2022.07.042.
[27] Du Y H, Zhao Z C, Song Y, et al.StrongSORT: Make DeepSORT great again[J]. IEEE Transactions on Multimedia, 2023, 25: 8725-8737. DOI: 10.1109/TMM.2023.3240881.
[28] Zhang X, Hao X Y, Li J S, et al. Fusion and visualization method of dynamic targets in surveillance video with geospatial information[J].Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1415-1423.(in Chinese)
[29] He W, Deng L, Shi H, et al. Novel virtual simply supported beam method for detecting the speed and axles of moving vehicles on bridges[J].Journal of Bridge Engineering, 2017, 22(4):04016141. DOI:10.1061/(asce)be.1943-5592.0001019.


Biographies: Deng Lu(1984─), male, doctor, professor; He Wei(corresponding author), male, doctor, wei_he@hnu.edu.cn.
Foundation items: The National Natural Science Foundation of China Youth Program(No. 52108139), Hunan Provincial Natural Science Foundation Youth Program(No. 2023JJ40290).
Citation: Deng Lu, Deng Jiayu, Wang Wei, et al. Identification of the spatiotemporal location of vehicle loads on highway bridges based on multi-view information fusion[J].Journal of Southeast University(English Edition), 2024, 40(1):1-12.DOI:10.3969/j.issn.1003-7985.2024.01.001.
Last Update: 2024-03-20