CONVOLUTIONAL NEURAL NETWORK FOR POTHOLE IDENTIFICATION IN URBAN ROADS

Abstract

Safe and effective mobility relies on regularly inspecting and maintaining urban road infrastructure. Vehicles and road users are put in danger by potholes, which is why their quick identification and repair are of the utmost importance. Using Convolutional Neural Networks (CNN) architecture, this research presents a new method for detecting and reporting potholes. To reliably detect and classifier pothole damage under a variety of lighting and environmental conditions, the proposed method incorporates a CNN model trained on a broad collection of road surface images. Urban roads and automobiles equipped with Internet of Things (IoT) sensors enhance the system to allow real-time reporting and location of potholes. Due to their built-in cameras and GPS modules, these devices can take images of the road and send their findings of potholes and exact locations to a central server. After these detections are made, the server uses them to prioritize the repair work and alert the proper authorities and road users via a specialized mobile app where the potholes are detected. The continuous problem of road maintenance may be solved in an efficient and scalable manner by integrating CNN with an IoT infrastructure. The device increases road safety and vehicle operating conditions while also making pothole identification and reporting procedures more efficient. Extensive testing has shown the suggested method is accurate in detecting potholes, can withstand many types of operations, and helps with proactive road repair plans. Smart city technologies demonstrating integration of IoT, and advanced machine learning algorithms may enhance the management of municipal infrastructure.

Keywords

• road infrastructure maintenance, real-time reporting, geographical localization, road surface inspection, remote monitoring.