## 1 Introduction
The system will focus on real-time monitoring of the front and rear distance of the vehicle and surrounding distances to enhance the safety of road driving. In the research of vehicle object detection, deep learning-based object detection algorithms have been widely developed and applied, including two-stage object and single-stage object detection algorithms. The two-stage detection algorithm needs to first generate candidate boxes and then perform classification regression, which has a high accuracy but slow speed. The commonly used algorithm is R-CNN [11; 12; 13]. The single-stage detection algorithm directly performs classification regression with low accuracy but fast speed. Commonly used algorithms include SSD [14; 15; 16] and YOLO [17; 18; 19]. Compared with two-stage detection algorithms, single-stage detection algorithms are more suitable for real-time detection during driving. In the research of binocular ranging technology based on computer vision (CV), [PERSON] et al. proposed a distance measurement method suitable for low-brightness environments based on the principle of binocular vision and the reconstruction theory of triangles [20]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensors** & **Precision** & **Speed** & **Ranging Range** & **Environmental Adaptability** & **Cost** \\ \hline Lidar & High & Fast & 50–300 m & Light-sensitive & High \\ Ultrasonic radar & Low & Slow & \(\leq\)5 m & Temperature- and humidity-sensitive & low \\ IP cameras & Medium–high & Fast & 5–100 m & Adaptable & low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of distance measuring sensors. In order to achieve dynamic ranging, [PERSON] first uses a single camera to capture images of vehicles and detect targets and then uses template matching technology to detect the same vehicle captured by the second camera [21]. [PERSON] first used MATLAB (R2013b) software to calibrate the camera and then used OpenCV (2.4.7) to perform stereo matching and correction on the image. The disparity of the image was used to calculate the distance between the target vehicle and the camera, thus achieving the goal of distance measurement [22]. The analysis of existing research results indicates that intelligent driving technology can already provide early warning and monitoring of hazardous road conditions and obstacles during the driving process of explosives transport vehicles. However, there remains a significant issue in the current application process: intelligent driving currently relies on embedded development, which necessitates industrial control computers and supporting sensors capable of stable long-term operation. The demand for high-end software and hardware facilities results in excessively high costs [23]. To tackle this issue, the YOLOv8 object detection algorithm, along with monocular and binocular camera ranging, is applied to establish an intelligent monitoring system for the driving environment of explosives transport vehicles. The system uses IP cameras that are flexibly arranged around the vehicle body to capture road images in real time and quickly transmits these images to an edge computing device for fast processing. The processed data will be displayed to the driver in an intuitive visual format, enabling the driver to grasp the dynamics of the surrounding environment of the vehicle in real time. With the help of this intelligent monitoring system, drivers can participate in traffic operation more accurately, so as to effectively monitor and prevent possible accidents. The application process of the system is shown in Figure 1.  
The main contributions of this article are as follows:  
1. A low-cost monitoring solution based on consumer-grade IP cameras and edge computing is proposed, addressing the applicability gap of traditional high-cost systems in older explosives transport vehicles. 2. A novel integration of YOLOv8 with hybrid ranging techniques is introduced.