Edge-based AI solution for enhancing urban safety: helmet compliance monitoring with YOLOv9 on Raspberry Pi

Abstract

Abstract This paper presents an automated helmet detection system leveraging Raspberry Pi and advanced deep learning techniques, specifically the YOLOv9 object detection model that focuses on real-time object detection. The system captures real-time video frames of motorcyclists, processes these frames to detect helmet usage, and interfaces with the vehicle’s ignition control unit to inhibit engine start if a helmet is not detected. This edge computing solution addresses the inefficiencies of manual inspections by providing a scalable, cost-effective, and real-time enforcement mechanism for helmet compliance. The YOLOv9 model, trained on a robust dataset, ensures high detection accuracy and low latency, demonstrating effective performance in real-world scenarios. The YOLOv9 model achieved a precision of 0.894, recall of 0.943, mAP50 of 0.967, and an inference speed of 21.0 ms per image. This research highlights significant advancements in intelligent transportation systems and road safety, achieved through integrating convolutional neural networks and embedded hardware platforms.

Keywords

• Helmet detection
• Raspberry Pi
• Deep learning
• YOLOv9
• Edge computing
• Computer vision