ABSTRACT
Accidents caused by uneven road conditions can harm drivers, passengers, and pedestrians. Monitoring the state of the roads is essential to creating a network of safe and enjoyable mobility. Road accidents are affected by a number of variables, including speeding, reckless driving, and poor road conditions. Accidents that happen through no fault of the motorist happen rather frequently. One of the main contributing causes to these incidents is bad road conditions. Due to the rising number of potholes, accident rates are rising year after year. Because road maintenance is typically performed manually, it takes a long time, involves effort, and is prone to human mistake. Since potholes are one of the main cause of accidents, it is crucial to identify and categories them using image processing techniques. On roads and highways, potholes are areas of uneven pavement that are caused by continual automobile traffic as well as environmental factors. A system for measuring pothole size and detecting them is suggested. Potholes are a nuisance, especially in the developing world, and can often result in vehicle damage or physical harm to the vehicle occupants. Drivers can be warned to take evasive action if potholes are detected in real-time. Moreover, their location can be logged and shared to aid other drivers and road maintenance agencies. This project is developed using deep learning cnn in image processing in python.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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