Publication

Abstract : In a developing country like India, the major difficulty lies in identifying and addressing problems. Improving road quality is crucial for economic growth, as well-maintained roads contribute significantly. The proposed model DeepPave, a combination of Visual Geometry Group 16 (VGG 16), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP), along with regularization techniques such as dropout and L2 regularization and optimization techniques, has been developed for the detection of potholes. The aim is to enhance road infrastructure and contribute positively to the nation's economy. The proposed pothole detection system holds great potential for enhancing road safety, optimizing maintenance operations, and reducing infrastructure maintenance expenses. By promptly identifying potholes, transportation authorities can efficiently allocate resources, improve road quality, and ensure safer driving conditions for motorists.