Title

A Deep Learning Approach for Street Pothole Detection

Publication Date

8-1-2020

Document Type

Conference Proceeding

Department

Computer Engineering

Publication Title

2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)

DOI

10.1109/BigDataService49289.2020.00039

First Page

198

Last Page

204

Abstract

Potholes are a structural damage to the road with hollow which can cause severe traffic accidents and impact road efficiency. In this paper, we propose an efficient pothole detection system using deep learning algorithms which can detect potholes on the road automatically. Four models are trained and tested with preprocessed dataset, including YOLO V3, SSD, HOG with SVM and Faster R-CNN. In the phase one, initial images with potholes and non-potholes are collected and labeled. In the phase two, the four models are trained and tested for the accuracy and loss comparison with the processed image dataset. Finally, the accuracy and performance of all four models are analyzed. The experimental results show that the YOLO V3 model performs best for its faster and more reliable detection results.

Keywords

CNN, Deep learning, Pothole detection, SVM, YOLO

Share

COinS