A Vehicle Detection and Alert System Based on YOLOv4: Design, Implementation, and Performance Evaluation

As living standards continue to rise, transportation systems have become increasingly convenient; however, associated safety risks have also escalated significantly. Insufficient pedestrian safety awareness has contributed to a rising incidence of traffic accidents. In response, this study leverages the relative maturity of object detection technologies by retraining the YOLOv4 algorithm using the VOC dataset to design and implement a vehicle alert system tailored for unsignalized intersections. The proposed system aims to mitigate collision risks under low-light and poor nighttime visibility conditions by detecting approaching vehicles and providing timely alerts to support safer pedestrian crossing decisions. Evaluation of the model’s performance demonstrates its effectiveness and practical applicability in real-world traffic environments.