PEDESTRIAN RED LIGHT TRAFFIC RECOGNITION MODEL BASED ON YOLOV8 ALGORITHM

The object of the study is the process recognition of pedestrian red light traffic. The subject of the study are the methods of process recognition of pedestrian red light traffic. The purpose of the paper is to improve the efficiency of real-time pedestrian red light traffic recognition model. The results obtained. The pedestrian red light traffic recognition model based on real-time object detection architecture YOLOv8 was proposed. The architecture and characteristics of YOLOv8 model, including its improved network structure, multi-scale detection ability, and adaptive anchor adjustment were introduced in detail. To demonstrate the efficiency and benefits of applying the YOLOv8 model, its performance was evaluated in various scenarios. Conclusions. Experiments have confirmed the efficiency of the proposed method. The use of the developed method based on the YOLOv8 architecture allowed to increase precision up to 0.935. Overall, the average performance across all categories is 0.851, which means that the model has a relatively high detection accuracy. In addition, model has a high-speed index.