A Deep Learning Approach for Street Pothole Detection
Publication Date
8-1-2020
Document Type
Conference Proceeding
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
Department
Computer Engineering
Recommended Citation
Ping Ping, Xiaohui Yang, and Zeyu Gao. "A Deep Learning Approach for Street Pothole Detection" 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService) (2020): 198-204. https://doi.org/10.1109/BigDataService49289.2020.00039
