An Edge Computing Framework for Pothole Detection

The lack of adequate road drainage systems in India makes it a challenge to prevent accidents. This lack of adequate drainage leads to premature road damage and the formation of potholes, ultimately contributing to accidents. We talk about the creation of an edge computing and Internet of Things-based real-time pothole detection system. The need for effective road maintenance and the rising incidence of accidents are the driving forces behind this project. The current manual visual inspection method is inefficient and expensive, prompting the exploration of an automated approach through the integration of GPS sensors and machine learning models, the system is able to accurately identify potholes and determine their coordinates. The proposed system seeks to lower costs, increase overall road safety, and improve the effectiveness of road maintenance. The various machine learning techniques and the incorporation of GPS into the IoT-based gadget are also highlighted. The effectiveness of the system including the use of deep learning techniques will rely on obtaining a substantial amount of data, specifically a large number of images of potholes. Several research studies and models related to pothole detection using deep learning are referenced, emphasizing the potential of this approach. The implementation screenshots and conclusion highlight the feasibility and benefits of the proposed system. Both public and private entities involved in road management stand to gain from the system’s ability to greatly improve road maintenance and safety.