ADVANCED PEDESTRIAN DETECTION AND INTELLIGENT TRAFFIC MANAGEMENT VIA DEEP LEARNING

Abstract

In this study, we developed a deep learning-based pedestrian detection system to prevent pedestrian collisions. These collisions account for a significant portion of urban traffic accidents. We collected and annotated a custom dataset of 620 high-resolution pedestrian images using the MakeSense labeling tool. Using this dataset, we trained YOLOv8, YOLOv11, and YOLOv12 models and evaluated them based on precision, recall, mAP, and F1-score. The training processes were conducted in the Google Colab environment using Python, supported by GPU acceleration. Among the models, YOLOv11-S achieved the highest performance with an F1-score of 94.9%. We then integrated the trained model into a PyQt5-based desktop simulation interface, enabling real-time pedestrian detection and automated traffic light control. The results demonstrate that deep learning-based pedestrian detection systems can operate effectively in real-time scenarios and provide a sustainable, scalable solution for smart city infrastructures.

Keywords

• Computer Vision
• Deep Learning
• YOLO
• Traffic Management
• Pedestrian Detection

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