Abstract
The road accident prediction system leverages deep learning techniques, specifically YOLO (You Only Look Once), for the detection of potholes in road footage. By employing YOLO, the system can efficiently identify and localize potholes within video frames, enabling rapid and accurate detection. Furthermore, the system incorporates a severity prediction module that utilizes the dimensions and characteristics of detected potholes to assess their severity levels. This predictive capability empowers authorities and road maintenance teams to prioritize repair efforts and allocate resources effectively, ultimately contributing to the reduction of road accidents and ensuring safer road conditions for motorists and pedestrians alike. Through the seamless integration of pothole detection and severity prediction functionalities, the road accident prediction system offers a proactive approach to road maintenance and safety management, enhancing overall road infrastructure resilience and public safety.
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