Road Condition Monitoring Using IOT & Analytics

Abstract

This project explores pothole identification utilizing innovative approaches in the dynamic field of smart infrastructure. In order to determine how well state-of-the-art object detection models—YOLOv8, Faster R-CNN, SSD-MobileNetV2, and RetinaNet identify road flaws, the study thoroughly compares them. The investigation offers a comprehensive answer by smoothly integrating Internet of Things(IoT) technology, going beyond algorithmic prowess. The combination of these technologies results in a novel method for seeing identified potholes together with their exact positions in a mobile application. In addition to improving road maintenance, this smooth integration of cutting-edge computer vision, Internet of Things connectivity, and intuitive visualization paves the way for an intelligent and participatory urban infrastructure paradigm.