Intelligent Road Anomaly Detection with Real-time Notification System for Enhanced Road Safety

TL;DR

This paper presents a real-time, AI-powered system for detecting and classifying road anomalies like potholes and cracks, transmitting data to authorities, and warning drivers to improve overall road safety.

Contribution

The study introduces an integrated system utilizing deep learning and cloud connectivity for real-time detection, classification, and notification of road anomalies.

Findings

Effective detection of potholes and cracks using deep learning.

Real-time anomaly counting and classification capabilities.

Proactive alerts to drivers and authorities enhance safety.

Abstract

This study aims to improve transportation safety, especially traffic safety. Road damage anomalies such as potholes and cracks have emerged as a significant and recurring cause for accidents. To tackle this problem and improve road safety, a comprehensive system has been developed to detect potholes, cracks (e.g. alligator, transverse, longitudinal), classify their sizes, and transmit this data to the cloud for appropriate action by authorities. The system also broadcasts warning signals to nearby vehicles warning them if a severe anomaly is detected on the road. Moreover, the system can count road anomalies in real-time. It is emulated through the utilization of Raspberry Pi, a camera module, deep learning model, laptop, and cloud service. Deploying this innovative solution aims to proactively enhance road safety by notifying relevant authorities and drivers about the presence of potholes and cracks to take actions, thereby mitigating potential accidents arising from this prevalent road hazard leading to safer road conditions for the whole community.