SMART DETECTION OF DAMAGED ROADS USING MACHINE LEARNING

Abstract

Finding potholes is crucial to reducing accidents all throughout the world. Numerous studies have been conducted, however in order to collect sensor data, they need certain instruments or technologies. In this research, we suggest a method for using a smartphone to detect potholes, and categorization is utilizing. Transfer Learning. The experimental finding supports the suggested strategy. Gives us efficiency in terms of performance and implementation. An array of the gathered data was processed using several techniques, and features from various frequencies. In addition to different machine-learning classifiers, domains were retrieved According to the results, characteristics from the temporal and frequency domains performed better than other features at spotting potholes. The Random Forest classifier demonstrated the greatest classification performance for potholes among the studied classifiers, with a precision of 88.5 percent and recall of 75 percent. Finally, we tested the suggested method's resilience and universality using datasets obtained from various types of roads.