Local Pothole detection using deep learning techniques

Abstract

Potholes are surface irregularities on roads that are deeper than 20 mm and extend more than 75 mm horizontally. They can be caused by a number of circumstances, including poor construction, water buildup during the rainy season, deterioration of the rock, and the pressure from heavy cars. Potholes have been found to be a major contributing factor in over 57,000 documented road accident deaths and 58,208 injuries over the previous 20 years, which is troubling given recent figures. In the year 2022, potholes are a common occurrence, which puts riders at greater risk. nearly the past 20 years, there have been nearly 57,000 road accident fatalities and 58,208 injuries documented in our nation, according to recent figures. This is a worrying trend. One important contributing factor to these accidents is potholes. Motorcycle riders in the modern world of 2022 are at greater risk since potholes are so common. This problem necessitates the use of machine learning in a real-time pothole detection system to alert drivers in a timely manner, reducing inconvenience and averting accidents. Although there have been earlier attempts in this area, our suggested strategy makes use of machine learning algorithms that are applied to a well chosen dataset, producing encouraging outcomes. By accurately identifying potholes in real time, our model's application might potentially save many lives.