Treffer: Design and research of bridge collision avoidance system based on camera calibration technology and motion detection.
Title:
Design and research of bridge collision avoidance system based on camera calibration technology and motion detection.
Authors:
Wang X; 9th Company of China First Highway Engineering co., LTD., Guangzhou, Guangdong, 511300, China., Wang S; 9th Company of China First Highway Engineering co., LTD., Guangzhou, Guangdong, 511300, China. Wsc123312@163.com., Wei Z; 9th Company of China First Highway Engineering co., LTD., Guangzhou, Guangdong, 511300, China., Ren R; 9th Company of China First Highway Engineering co., LTD., Guangzhou, Guangdong, 511300, China., Huang J; 9th Company of China First Highway Engineering co., LTD., Guangzhou, Guangdong, 511300, China.
Source:
Scientific reports [Sci Rep] 2025 Oct 08; Vol. 15 (1), pp. 35146. Date of Electronic Publication: 2025 Oct 08.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Ma, D. et al. A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines. Tunn. Undergr. Sp Technol. 141, 105345 (2023). (PMID: 10.1016/j.tust.2023.105345)
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Fu, Y. et al. Image segmentation of cabin assembly scene based on improved RGB-D mask R-CNN. IEEE Trans. Instrum. Meas. 71, 1–12 (2022).
Azfar, T. et al. Deep learning-based computer vision methods for complex traffic environments perception: A review. Data Sci. Transp. 6 (1), 1 (2024). (PMID: 10.1007/s42421-023-00086-7)
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Li, F. J., Zhang, C. Y. & Chen, C. L. P. STS-DGNN: vehicle trajectory prediction via dynamic graph neural network with spatial–temporal synchronization. IEEE Trans. Instrum. Meas. 72, 1–13 (2023).
Fadila, J. N. et al. Comprehensive review of smart urban traffic management in the context of the fourth industrial revolution. IEEE Access, 12 (2024).
Yu, X., Chen, Y. & He, Y. Vulnerability assessment of reinforced concrete piers under vehicle collision considering the influence of uncertainty. Buildings 15 (8), 1222 (2025). (PMID: 10.3390/buildings15081222)
Mohanty, A., Mohapatra, A. G. & Mohanty, S. K. Real-time traffic monitoring with AI in smart cities, in Internet of Vehicles and Computer Vision Solutions for Smart City Transformations, Springer, (ed. Abraham, A.) 135–165 (2025).
Tan, X., Wu, G., Li, Z., Liu, K. & Zhang, C. Autonomous emergency collision avoidance and collaborative stability control technologies for intelligent vehicles: a survey. IEEE Trans. Intell. Veh, 13 (2024).
Li, Q., Shao, Y., Li, L., Li, J. & Hao, H. Advancements in 3D displacement measurement for civil structures: A monocular vision approach with moving cameras. Measurement 242, 116060 (2025). (PMID: 10.1016/j.measurement.2024.116060)
Chhimpa, G. R., Kumar, A., Garhwal, S., Khan, F. & Moon, Y. K. and others, Revolutionizing Gaze-based Human-Computer Interaction using Iris Tracking: A Webcam-Based Low-Cost Approach with Calibration, Regression and Real-Time Re-calibration, IEEE Access, (2024).
Cheng, L. et al. CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement, arXiv Prepr. arXiv2502.17648, (2025).
Fei, T. et al. Spatial environment perception and sensing in automated systems: A review. IEEE Sens. J. 24 (14), 21813–21833 (2024). (PMID: 10.1109/JSEN.2024.3379222)
Hosain, M. T. et al. Synchronizing object detection: applications, advancements and existing challenges. IEEE Access, 12(2024).
D’Angelo, M. et al. Bridge collapses in Italy across the 21st century: survey and statistical analysis. Struct Infrastruct. Eng, pp. 1–23, (ed. Sayed, N.) (2025).
Saxena, V. Predictive analytics in occupational health and safety, arXiv Prepr. arXiv2412.16038, (2024).
Jagatheesaperumal, S. K., Bibri, S. E., Huang, J., Rajapandian, J. & Parthiban, B. Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety. Comput. Urban Sci. 4 (1), 10 (2024). (PMID: 10.1007/s43762-024-00120-6)
Dehghanian, Z., Ardekhani, P., Vahedi, A., Beigy, H. & Rabiee, H. R. Camera Trajectory Generation: A Comprehensive Survey of Methods, Metrics, and Future Directions, arXiv Prepr. arXiv2506.00974, (2025).
Bian, H. et al. UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices, Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 8, no. 4, pp. 1–26, (2024).
Hagen, A. & Andersen, T. M. Asset management, condition monitoring and digital twins: damage detection and virtual inspection on a reinforced concrete Bridge. Struct. Infrastruct. Eng. 20, 7–8 (2024). (PMID: 10.1080/15732479.2024.2311911)
Khanam, R., Hussain, M., Hill, R. & Allen, P. A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access, 12 (2024).
Cabral, R., Ribeiro, D. & Rakoczy, A. Engineering the future: A deep dive into remote inspection and reality capture for railway infrastructure digitalization, in Digital Railway Infrastructure, Springer, 229–256, (ed. Ribeiro, D.) (2024).
Karampinis, V. et al. Ensuring uav safety: A vision-only and real-time framework for collision avoidance through object detection, tracking, and distance estimation, arXiv Prepr. arXiv2405.06749, (2024).
Kulinan, A. S., Park, M., Aung, P. P. W., Cha, G. & Park, S. Advancing construction site workforce safety monitoring through BIM and computer vision integration. Autom. Constr. 158, 105227 (2024). (PMID: 10.1016/j.autcon.2023.105227)
Zheng, N. N. et al. Toward intelligent driver-assistance and safety warning system. IEEE Intell. Syst. 19 (2), 8–11 (2004). (PMID: 10.1109/MIS.2004.1274904)
Mukhtar, A., Xia, L. & Tang, T. B. Vehicle detection techniques for collision avoidance systems: A review. IEEE Trans. Intell. Transp. Syst. 16 (5), 2318–2338 (2015). (PMID: 10.1109/TITS.2015.2409109)
Dai, F., Park, M. W., Sandidge, M. & Brilakis, I. A vision-based method for on-road truck height measurement in proactive prevention of collision with overpasses and tunnels. Autom. Constr. 50, 29–39 (2015). (PMID: 10.1016/j.autcon.2014.10.005)
Zaarane, A., Slimani, I., Al Okaishi, W., Atouf, I. & Hamdoun, A. Distance measurement system for autonomous vehicles using stereo camera. Array 5, 100016 (2020). (PMID: 10.1016/j.array.2020.100016)
Ramos, M. A., Thieme, C. A., Utne, I. B. & Mosleh, A. Human-system concurrent task analysis for maritime autonomous surface ship operation and safety. Reliab. Eng. Syst. Saf. 195, 106697 (2020). (PMID: 10.1016/j.ress.2019.106697)
Halfawy, M. R. & Hengmeechai, J. Optical flow techniques for Estimation of camera motion parameters in sewer closed circuit television inspection videos. Autom. Constr. 38, 39–45 (2014). (PMID: 10.1016/j.autcon.2013.10.016)
Aly, A. M., Chacon, P., Gol-Zaroudi, H., Choi, J. W. & Voyiadjis, G. Proposed practical overheight detection and alert system. Autom. Control Comput. Sci. 56 (5), 467–480 (2022). (PMID: 10.3103/S0146411622050017)
Seisa, A. S., Lindqvist, B., Satpute, S. G. & Nikolakopoulos, G. An edge architecture for enabling autonomous aerial navigation with embedded collision avoidance through remote nonlinear model predictive control. J. Parallel Distrib. Comput. 188, 104849 (2024). (PMID: 10.1016/j.jpdc.2024.104849)
Zhang, L., Chen, P., Li, M., Chen, L. & Mou, J. A data-driven approach for ship-bridge collision candidate detection in Bridge waterway. Ocean. Eng. 266, 113137 (2022). (PMID: 10.1016/j.oceaneng.2022.113137)
Ye, Z. et al. IoT-enhanced smart road infrastructure systems for comprehensive real-time monitoring. Internet Things Cyber-Physical Syst. 4, 235–249 (2024). (PMID: 10.1016/j.iotcps.2024.01.002)
Ma, D. et al. A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines. Tunn. Undergr. Sp Technol. 141, 105345 (2023). (PMID: 10.1016/j.tust.2023.105345)
Dong, Y., Pan, Y., Wang, D. & Chen, A. Traffic load simulation for Long-Span bridges using a transformer model incorporating In-Lane transverse vehicle movements. IEEE Trans. Intell. Transp. Syst, 25 (2024).
Djenouri, Y., Belbachir, A. N., Michalak, T., Belhadi, A. & Srivastava, G. Enhancing smart road safety with federated learning for near crash detection to advance the development of the internet of vehicles. Eng. Appl. Artif. Intell. 133, 108350 (2024). (PMID: 10.1016/j.engappai.2024.108350)
Thombre, S. et al. Sensors and AI techniques for situational awareness in autonomous ships: A review. IEEE Trans. Intell. Transp. Syst. 23 (1), 64–83 (2020). (PMID: 10.1109/TITS.2020.3023957)
Fahimullah, M., Ahvar, S., Agarwal, M. & Trocan, M. Machine learning-based solutions for resource management in fog computing. Multimed Tools Appl. 83 (8), 23019–23045 (2024). (PMID: 10.1007/s11042-023-16399-2)
Yang, M. T. & Zheng, J. Y. On-road collision warning based on multiple FOE segmentation using a dashboard camera. IEEE Trans. Veh. Technol. 64 (11), 4974–4984 (2014). (PMID: 10.1109/TVT.2014.2378373)
Fu, Y. et al. Image segmentation of cabin assembly scene based on improved RGB-D mask R-CNN. IEEE Trans. Instrum. Meas. 71, 1–12 (2022).
Azfar, T. et al. Deep learning-based computer vision methods for complex traffic environments perception: A review. Data Sci. Transp. 6 (1), 1 (2024). (PMID: 10.1007/s42421-023-00086-7)
Conde, M. V. & Turgutlu, K. Exploring vision transformers for fine-grained classification, arXiv Prepr. arXiv2106.10587, (2021).
Arroyo, H. et al. Segmentation of drone collision hazards in airborne RADAR point clouds using PointNet. IEEE Trans. Intell. Transp. Syst, 25 (2024).
Li, F. J., Zhang, C. Y. & Chen, C. L. P. STS-DGNN: vehicle trajectory prediction via dynamic graph neural network with spatial–temporal synchronization. IEEE Trans. Instrum. Meas. 72, 1–13 (2023).
Contributed Indexing:
Keywords: Bridge collision avoidance; Camera calibration; Infrastructure safety; Motion detection; Smart transportation; Vision transformer (ViT)
Entry Date(s):
Date Created: 20251008 Latest Revision: 20251011
Update Code:
20251011
PubMed Central ID:
PMC12508082
DOI:
10.1038/s41598-025-19096-2
PMID:
41062567
Database:
MEDLINE
Weitere Informationen
Bridge collisions, particularly those involving over-height vehicles, pose significant threats to public infrastructure, economic stability, and human safety. This study presents an intelligent, vision-based Bridge Collision Avoidance System (BCAS) that leverages advanced camera calibration techniques, motion detection algorithms, and real-time risk assessment frameworks to proactively detect and mitigate potential collisions. The system architecture integrates high-resolution video feeds with precise intrinsic and extrinsic camera calibration to accurately transform 2D motion into real-world coordinates. Motion detection and object segmentation are performed using a hybrid approach combining traditional background subtraction with deep learning-based models such as YOLOv11 and Vision Transformers (ViT), ensuring robustness in dynamic lighting and occlusion-prone environments. Object trajectory estimation is achieved through frame-wise velocity computation and spatial projection, enabling predictive collision path analysis. A risk evaluation model classifies threat levels using spatial thresholds, velocity vectors, and entropy-calibrated confidence scores. Real-time alerts are dispatched through low-latency edge-cloud frameworks with visual and auditory feedback to connected operators. Experimental validation across diverse scenarios-including occlusion, night conditions, and dense traffic-demonstrates superior performance in terms of accuracy (95.7%), false alarm rate (3.2%), and average system response latency (162 ms), when benchmarked against traditional rule-based and motion detection systems. This research contributes a modular, scalable, and fault-tolerant solution suitable for real-world deployment to enhance bridge safety in smart urban infrastructures.
(© 2025. The Author(s).)
Declarations. Competing interests: The authors declare no competing interests.