International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The Accident Detection and Alert System is designed to provide real-time accident detection using deep learning techniques, specifically the YOLOv8 model. This system aims to enhance the speed and accuracy of accident detection through image processing. The platform is developed with a simple, user-friendly interface using HTML, CSS, and JavaScript for frontend development, enabling users to easily log in, register, and interact with the prediction page.
Once a user successfully logs in, they are directed to the prediction page, where they can upload an image. The uploaded image is then processed by the backend, which is powered by Python and the Flask framework. The YOLOv8 model is responsible for analysing the image and detecting if an accident is present. YOLOv8, being a fast and accurate deep learning-based object detection model, is ideal for real-time applications like accident detection, as it can process images quickly while maintaining high accuracy.

If the model detects an accident in the uploaded image, the system automatically triggers an email alert to the admin. The email contains essential details such as the image and a notification of the accident, allowing the admin to take immediate action. If no accident is detected, the user is notified via the interface, informing them that no accident was found in the image.The system's workflow aims to provide efficient and reliable detection of accidents, reducing response times in emergency situations. It can be applied in various real-time monitoring scenarios, including traffic management, surveillance systems, and emergency response systems. By automating the accident detection and alerting process, the system enhances communication between users and admins, ensuring that the right actions are taken as quickly as possible. The YOLOv8 model ensures that the detection process remains both fast and accurate, making the solution effective for use in dynamic, high-demand environments where real-time responses are critical.

Keywords: Accident Detection, YOLOv8, Flask, Real-Time Image Processing, Email Notification, Python, Machine Learning, Traffic Monitoring, Admin Alert, Image Upload, Web Interface, Emergency Response, Security Systems, Object Detection.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412107