Published Paper Details:

Due to the rise in automobiles, climate change, and population density, there are now an alarmingly large number of potholes in the world. Understanding the physical features of potholes and their surroundings, such as the surfaces they appear on, the size and depth of common potholes, and the kinds of wear and tear that might result in pothole formation, is usually necessary for their identification. It would also require familiarity with technologies like deep learning and machine learning techniques that are frequently used for pothole identification. Poor road conditions are one of the major causes for road accidents. Developing countries in particular are witnessing in- creased accident rates due to these poor road conditions. Potholes, deep ridges, missing pitches, improper speed breakers, poorly constructed manhole covers and slabs all combine to greatly increase the probability of serious accidents thus transforming roads into obstacle courses. In this study we have developed a model to detect unwanted potholes, deep ridges and speed breakers using computer vision and machine learning tools. We have developed a customized dataset (called Bumpy) that we use to train our machine learning algorithms. In this paper we propose a method where we use the Tensorflow pre-trained model to detect the potholes, deep ridges and speed breakers. Our experimental results demonstrate high accuracy although there are many obstacles on the road.