Real-Time Pothole Detection Using Deep Learning

TL;DR

This paper presents a real-time pothole detection system using deep learning, achieving high accuracy and speed, which can enhance road safety and autonomous vehicle performance.

Contribution

The study compares multiple deep learning architectures for pothole detection, identifying YOLOv4 as the most effective in accuracy and speed for real-time application.

Findings

YOLOv4 achieved 85.39% mAP in pothole detection.

The system operates at 20 frames per second.

Detects potholes from 100 meters away.

Abstract

Roads are connecting line between different places, and used daily. Roads' periodic maintenance keeps them safe and functional. Detecting and reporting the existence of potholes to responsible departments can help in eliminating them. This study deployed and tested on different deep learning architecture to detect potholes. The images used for training were collected by cellphone mounted on the windshield of the car, in addition to many images downloaded from the internet to increase the size and variability of the database. Second, various object detection algorithms are employed and compared to detect potholes in real-time like SDD-TensorFlow, YOLOv3Darknet53 and YOLOv4Darknet53. YOLOv4 achieved the best performance with 81% recall, 85% precision and 85.39% mean Average Precision (mAP). The speed of processing was 20 frame per second. The system was able to detect potholes from a range on 100 meters away from the camera. The system can increase the safety of drivers and improve the performance of self-driving cars by detecting pothole time ahead.