Road Surveillance using Real-Time Detection and Alert System

This study introduces a novel approach to accident detection utilizing YOLOv8, a state-of-the-art object detection model, integrated with a real-time alert system aimed at swiftly notifying emergency services and nearby individuals to prevent injuries and fatalities. This study presents an innovative approach to road surveillance using real-time detection and alert systems, integrating advanced machine learning models and communication technologies. The architectural framework encompasses Mask R-CNN for precise object detection and segmentation, DeepDream for anomaly accentuation, Variational Autoencoders (VAEs) for feature extraction and anomaly detection, and YOLO for instantaneous object detection. The amalgamation of these models facilitates comprehensive monitoring of road environments, proficiently identifying vehicles, pedestrians, and potential hazards with high accuracy. Furthermore, the Twilio API is incorporated to facilitate instant communication and alerts based on detected events, ensuring swift responses from relevant authorities. This holistic approach not only enhances road safety by proactively identifying risks but also streamlines emergency response mechanisms. The system’s effectiveness is demonstrated through rigorous testing on real-world road scenarios, showcasing its potential for wide-scale deployment in intelligent transportation systems for enhanced traffic management and accident prevention.