An Intelligent Neural Network Approach for Automated Pothole Detection

AbstractPotholes cause accidents, vehicle damage, and significant road safety issues, yet current detection and reporting methodsare slow, inefficient, and often unreliable. This delays necessary repairs, putting lives at risk. To address this, we’vedeveloped a system that integrates machine learning with a user-friendly grievance reporting platform. By using TensorFlowand annotated datasets from LabelImg, our system detects potholes in real-time from dash cam footage, eliminating the needfor costly sensors while ensuring accurate and efficient detection. Additionally, the grievance reporting portal enables usersto upload pictures of potholes along with location details. These reports are automatically registered and accessible toauthorities, facilitating quicker resolution. The solution is scalable, cost-effective, and user-friendly, this makes it appropriatefor broad use. In addition to improving road safety by lowering pothole-related collisions, this integrated strategy offers aworkable, effective way to maintain roads. Through its ease of use and scalability, this system has the potential to become avaluable tool for citizens and authorities in effectively managing pothole-related issues.