journal article·2025-04-07Open access

An Intelligent Monitoring System for the Driving Environment of Explosives Transport Vehicles Based on Consumer-Grade Cameras

Jinshan Sun, Jianhui Tang, Ronghuan Zheng, Xuan Liu, Weitao Jiang, Jie Xu

Applied Sciences· Volume 15· Issue 7

Abstract

With the development of industry and society, explosives are widely used in social production as an important industrial product and require transportation. Explosives transport vehicles are susceptible to various objective factors during driving, increasing the risk of transportation. At present, new transport vehicles are generally equipped with intelligent driving monitoring systems. However, for old transport vehicles, the cost of installing such systems is relatively high. To enhance the safety of older explosives transport vehicles, this study proposes a cost-effective intelligent monitoring system using consumer-grade IP cameras and edge computing. The system integrates YOLOv8 for real-time vehicle detection and a novel hybrid ranging strategy combining monocular (fast) and binocular (accurate) techniques to measure distances, ensuring rapid warnings and precise proximity monitoring. An optimized stereo matching workflow reduces processing latency by 23.5%, enabling real-time performance on low-cost devices. Experimental results confirm that the system meets safety requirements, offering a practical, application-specific solution for improving driving safety in resource-limited explosive transport environments.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Fire Detection and Safety Systems · IoT and GPS-based Vehicle Safety Systems · Currency Recognition and Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China

Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, China

School of Civil Engineering, Tianjin University, Tianjin 300350, China

Key Laboratory of Coast Civil Structure Safety of the Ministry of Education, Tianjin University, Tianjin 300350, China

Funding

State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering, Jianghan University

References (28)

Show source-provided references
1. United Nations Classification of Hazardous Goods and Hazardous Chemicals
2. Guo, M. (2018). Study on Risk Assessment of Explosives in Highway Transportation. Sci. Informationization, 27.
3. Sun, J., Zheng, R., and Huang, N. (2023, January 10–12). Driver behavior monitoring system for explosive transport. Proceedings of the ITSSC 2023, Xi’an, China.10.1117/12.3023935 ↗
4. How to improve the supervision of “two customers and one danger”
5. Han, G., Li, Y., and Tan, Z. (2020). Danger analysis and emergency disposal of liquefied petroleum gas highway transportation-Zhejiang Wenling ‘6.13’ tank truck explosion accident warning. Guangdong Chem., 47.
6. Analysis on Safety Problems in the Transportation of Dangerous Chemicals
7. Survey on Testing of Intelligent Systems in Autonomous Vehicles
8. Doppler lidar is used to achieve simultaneous distance and velocity measurements
9. Wei, X., Che, C., and Song, C. (2014). A review of ultrasonic sensor applications. Ind. Control. Comput., 27.
10. Sun, J., Zheng, R., and Liu, X. (2024). A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm. Sensors, 24.10.3390/s24196339 ↗
11. Xu, X., Zhao, M., and Shi, P. (2022). Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN. Sensors, 22.10.3390/s22031215 ↗
12. Scale-Aware Fast R-CNN for Pedestrian Detection
13. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
14. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016). SSD: Single shot MultiBox detector. Computer Vision—ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016, Proceedings, Part I, Springer.10.1007/978-3-319-46448-0_2 ↗
15. Yun, J., Jiang, D., and Liu, Y. (2022). Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network. Front. Bioeng. Biotechnol., 10.10.3389/fbioe.2022.861286 ↗
16. An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD10.1016/j.pce.2018.12.001 ↗
17. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection10.1016/j.ins.2020.02.067 ↗
18. YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images10.1109/ACCESS.2023.3233964 ↗
19. Li, P., and Zhao, W. (2020). Image fire detection algorithms based on convolutional neural networks. Case Stud. Therm. Eng., 19.10.1016/j.csite.2020.100625 ↗
20. Zhao, C., Zhao, Z., and Chen, W. (2018, January 10–12). Low Luminance Measurement Based on Binocular Ranging. Proceedings of the 2018 5th International Conference on Systems and Informatics(ICSAI), Nanjing, China.10.1109/ICSAI.2018.8599401 ↗
21. Zaarane, A., Slimani, I., and Al Okaishi, W. (2020). Distance measurement system for autonomous vehicles using stereo camera. Array, 5.10.1016/j.array.2020.100016 ↗
22. Target ranging system based on binocular stereo vision
23. Hao, Y., Wen, S., and Wu, Y. (2021). Design and implementation of model-based driving assistance system. Comput. Simul., 38.
24. (2024, May 06). Hikvision-Leading the New Future of Intelligent Internet of Things. Available online: https://www.hikvision.com/en/.
25. Han, Q., Liu, X., and Xu, J. (2022). Detection and Location of Steel Structure Surface Cracks Based on Unmanned Aerial Vehicle Images. J. Build. Eng., 50.10.1016/j.jobe.2022.104098 ↗
26. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS10.3390/make5040083 ↗
27. (2024, May 21). TWOWIN Technology -AI Edge Computer_5G Mobile BOX Jetson Nano Orin nx AGX Xavier. Available online: https://twowinit.com/.
28. Acousto-Optic Double Control Automatic Alarm and Lighting Circuit Design