Crashsense: Real-Time Road Collision Detection and Notification

Introduction: In today's fast-paced technology, road safety demands intelligent, efficient response systems.An advanced Road Accident Detection System by combining Generative Adversarial Networks (GANs) for real-time image dehazing with YOLOv11 for precise object detection. The GAN-based model enhances visibility in adverse weather, enabling accurate accident detection even in low-visibility scenarios. YOLOv11 effectively identifies various objects, including vehicles, pedestrians, and collision events. Upon detecting an accident, the system triggers automated emergency notifications, sending real-time alerts with precise location details to responders such as police stations, hospitals, Regional Transport Offices (RTOs) and traffic management authorities, facilitating prompt traffic clearance. By minimizing response time, this system enhances survivability rates. Extensive experimental evaluations confirm its robustness across diverse environmental conditions, outperforming traditional dehazing and object detection methods, thereby improving accident detection and optimizing emergency response mechanisms. Objectives: The objective of this project is to develop a real-time road accident detection system using GAN-based dehazing and YOLOv11 for precise object detection. The system enhances visibility in adverse weather conditions and ensures quick emergency response by automatically notifying relevant authorities, reducing response time, and improving survivability rates. Methods :The proposed system integrates Generative Adversarial Networks (GANs) for real-time image dehazing and YOLOv11 for high-speed object detection. Video frames from surveillance cameras or dashcams are preprocessed using GAN-based dehazing to improve visibility under adverse weather conditions. A transformer-based attention mechanism prioritizes critical areas for detection, enhancing precision. When an accident is detected, the system triggers automated emergency notifications with precise location details, alerting police stations, hospitals, Regional Transport Offices (RTOs), and traffic management authorities. This approach ensures rapid response, minimizing casualties Results: The proposed system effectively enhances road accident detection by integrating GAN-based dehazing and YOLOv11 object detection. The dehazing model improves image clarity, allowing for better feature extraction in low-visibility conditions, while YOLOv11 ensures accurate identification of vehicles, pedestrians, and collision events. The system significantly reduces false positives and enhances detection accuracy compared to traditional methods. Additionally, the automated emergency notification mechanism enables faster response times, demonstrating the model’s reliability in real-world scenarios and its potential for improving road safety and intelligent transportation systems. Conclusions: Enhancing road safety through real-time accident detection plays a vital role in minimizing casualties and improving emergency response efficiency. The developed system ensures accurate accident identification, even in challenging weather conditions. It delivers instant alerts to relevant authorities, significantly improving response time and making it a reliable solution for road safety and emergency management.