## 5 Conclusions  
This article proposes an intelligent monitoring system for the driving environment of explosives transport vehicles based on consumer-grade cameras. The system uses a consumer-grade IP camera to capture images of road conditions in real time and quickly transmits these images to edge computing devices. On the edge device, combined with deep learning and computer vision technology, target detection and ranging calculation are performed. Subsequently, the processed data are presented to the driver in a visual  
Figure 19: Visual display of this system.  
form so that the driver can grasp the traffic conditions and participate in the traffic operation. The system significantly improves driving safety and helps to effectively prevent traffic accidents.  
1. A low-cost monitoring solution based on IP cameras and edge computing is proposed, filling the applicability gap of traditional high-cost systems in old transport vehicles. The system captures real-time data via IP cameras and processes them on edge devices, enabling efficient monitoring of the driving environment at a fraction of the cost of traditional systems.
2. The system integrates YOLOv8 for target detection with a hybrid ranging approach, combining the speed of monocular ranging and the precision of binocular ranging. This dual-mode strategy ensures rapid warnings for distant obstacles and accurate measurements in high-risk proximity zones, tailored to the unique demands of explosive transport.
3. A key innovation is the optimized binocular ranging algorithm, which performs stereo matching only within YOLOv8-detected bounding boxes. This optimization reduces processing latency by 23.5%, enabling real-time performance on low-cost edge devices while maintaining high accuracy.
4. Tests show that the system works well in real-world conditions. It keeps distance measurement errors below 5% within a 50 m range, meeting the safety needs of explosive transport. This proves that the system is reliable and robust for actual driving scenarios.  
Research shortcomings and prospects: This study found that the current system, relying on consumer-grade cameras, is limited in performance under extreme lighting conditions. Additionally, its practical effectiveness in complex traffic scenarios such as rain, snow, and nighttime has not been fully validated. In future research, we will explore more advanced camera technologies to enhance system performance under extreme lighting conditions and further validate its application in complex traffic scenarios, aiming to provide more reliable outcomes for relevant research and practice.  
Conceptualization, [PERSON]; methodology, [PERSON], J.T., R.Z., X.L., W.J. and J.X.; validation, J.T.; formal analysis, [PERSON]; investigation, [PERSON], X.L. and J.T.; resources, [PERSON]; data curation, J.T.; writing--original draft preparation, J.S. and J.T.; writing--review and editing, X.L., W.J. and J.X.; visualization, R.Z. and J.T.; supervision, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering, Jianghan University (Grant No. PBSKL2022B03).  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
The authors declare no conflicts of interest.