Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2024, 7(3); doi: 10.25236/AJCIS.2024.070313.

Road Safety Monitoring Model Based on YOLOV8


Ding Yushen

Corresponding Author:
Ding Yushen

Tiangong University, Tianjin, China, 300387

Computer Science, Tiangong University, Tianjin, China


In urban traffic, pedestrians frequently break into the road has become an important cause of traffic accidents. To solve this problem, a road safety monitoring model based on YOLOv8 object detection algorithm is constructed in this study. The model is designed to monitor vehicles, pedestrians, bicycles and other elements on the road in real time and record pedestrian violations, with a special focus on trespassing in prohibited areas. Through advanced computer vision technology and Streamlit, an efficient traffic monitoring system is realized, which improves the level of urban traffic safety and reduces the potential risk of traffic accidents. This innovative project will play an important role in the field of urban safety management.


Computer vision, Machine learning, Deep learning, Yolov8, Object detection, Traffic safety

Cite This Paper

Ding Yushen. Road Safety Monitoring Model Based on YOLOV8. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 91-96. https://doi.org/10.25236/AJCIS.2024.070313.


[1] Zhuang Xiangling. "Study on the Factors and Safety Auxiliary Design Strategies in Pedestrian Crossing Safety." 2015.

[2] Qian Tianzi. "Research on Factors Influencing Unsafe Behaviors of Pedestrians at Road Intersections." Ph.D. diss., Beijing Jiaotong University, 2015.

[3] Cheng Jie, Chen Jianhua, and Xu Shuhan. "Intelligent Glove and Translation System for Sign Language Collection and Input." Electronic Products World, vol. 30, no. 8, 2023, pp. 61-65.

[4] Solawetz J., & Francesco. "What is YOLOv8? The Ultimate Guide." 2023.

[5] Feng Jing, Fu Dandan, Liu Qiang, and Zhu Kejia. "Research on Aerial Vehicle Detection Technology Based on YOLOv8." Computer Science and Applications, vol. 13, no. 12, 2023, pp. 2399-2407.

[6] Zhao Enxing, & Wang Chao. "Traffic Signal Recognition Based on YOLOv8." Artificial Intelligence and Robotics Research, vol. 12, no. 3, 2023, pp. 246-254. 

[7] Narayanan Arvind, Shi Elaine, & Rubinstein, Benjamin I. P. "Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge." 2011.