Back to Search Start Over

A Method to Improve the Accuracy of Pavement Crack Identification by Combining a Semantic Segmentation and Edge Detection Model

Authors :
Peigen Li
Haiting Xia
Bin Zhou
Feng Yan
Rongxin Guo
Source :
Applied Sciences, Vol 12, Iss 9, p 4714 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

In recent years, deep learning-based detection methods have been applied to pavement crack detection. In practical applications, surface cracks are divided into inner and edge regions for pavements with rough surfaces and complex environments. This creates difficulties in the image detection task. This paper is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed. The model can be used for other tasks that need both semantic segmentation and edge detection. Finally, the segmentation and edge images are fused using different methods to improve the crack detection accuracy. The experimental results show that mean intersection over union reaches 69.32 on our dataset and 61.05 on another pavement dataset group that did not participate in training. Our model is better than other detection methods based on deep learning. The proposed method can increase the MIoU value by up to 5.55 and increase the MPA value by up to 10.41 when compared to previous semantic segmentation models.

Details

Language :
English
ISSN :
12094714 and 20763417
Volume :
12
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.bf01d39c9a7840c888ae4c0d2398c574
Document Type :
article
Full Text :
https://doi.org/10.3390/app12094714