Pothole Detection System for Automobiles using Machine Learning

Abstract

Abstract: In recent years, rapid economic growth and technological progress have had a profound impact on traditional transportation systems. One notable concern in terms of traffic safety is the discomfort and risk posed by rough roads, particularly those marred by potholes. This research project seeks to address these issues by not only improving road safety but also enhancing the level of automation in driving. With the proliferation of self-driving vehicles equipped with advanced autopilot systems, there arises a pressing need for real-time sensing of road conditions. To achieve this goal, the project employs machine learning techniques and image processing to detect potholes in real-time and relay this information to other vehicles via an internet connection. The research phase involved a thorough evaluation of various software algorithms and hardware tools, with an emphasis on criteria such as efficiency, cost-effectiveness, accuracy, and practicality for assembly. In addition to these considerations, deep learning and machine learning methodologies were integrated with hardware tools to formulate a refined set of requirements for the pothole detection system.