Pixel-Level Recognition of Pavement Distresses Based on U-Net

This study develops and tests an automatic pixel-level image recognition model to reduce the amount of manual labor required to collect data for road maintenance. Firstly, images of six kinds of pavement distresses, namely, transverse cracks, longitudinal cracks, alligator cracks, block cracks, poth...

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Main Authors: Deru Li, Zhongdong Duan, Xiaoyang Hu, Dongchang Zhang
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2021/5586615
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author Deru Li
Zhongdong Duan
Xiaoyang Hu
Dongchang Zhang
author_facet Deru Li
Zhongdong Duan
Xiaoyang Hu
Dongchang Zhang
author_sort Deru Li
collection DOAJ
description This study develops and tests an automatic pixel-level image recognition model to reduce the amount of manual labor required to collect data for road maintenance. Firstly, images of six kinds of pavement distresses, namely, transverse cracks, longitudinal cracks, alligator cracks, block cracks, potholes, and patches, are collected from four asphalt highways in three provinces in China to build a labeled pixel-level dataset containing 10,097 images. Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. Data augmentation, batch normalization, momentum, transfer learning, and discriminative learning rates are used to train the model. Thirdly, the trained models are validated on the test dataset, and the results of experiments show the following: if the types of pavement distresses are not distinguished, the pixel accuracy (PA) values of the recognition models using ResNet-34 and ResNet-50 as basic classification networks are 97.336% and 95.772%, respectively, on the validation set. When the types of distresses are distinguished, the PA values of models using the two classification networks are 66.103% and 44.953%, respectively. For the model using ResNet-34, the category pixel accuracy (CPA) and intersection over union (IoU) of the identification of areas with no distress are 99.276% and 99.059%, respectively. For areas featuring distresses in the images, the CPA and IoU of the model are the highest for the identification of patches, at 82.774% and 73.778%, and are the lowest for alligator cracks, at 14.077% and 12.581%, respectively.
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institution Kabale University
issn 1687-8434
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spelling doaj-art-4c9176f51a174c1f846f088681b240ea2025-02-03T01:28:28ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/55866155586615Pixel-Level Recognition of Pavement Distresses Based on U-NetDeru Li0Zhongdong Duan1Xiaoyang Hu2Dongchang Zhang3School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaChina Merchants Roadway Information Technology (Chongqing) Co., Ltd., Chongqing 400067, ChinaChina Merchants Roadway Information Technology (Chongqing) Co., Ltd., Chongqing 400067, ChinaThis study develops and tests an automatic pixel-level image recognition model to reduce the amount of manual labor required to collect data for road maintenance. Firstly, images of six kinds of pavement distresses, namely, transverse cracks, longitudinal cracks, alligator cracks, block cracks, potholes, and patches, are collected from four asphalt highways in three provinces in China to build a labeled pixel-level dataset containing 10,097 images. Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. Data augmentation, batch normalization, momentum, transfer learning, and discriminative learning rates are used to train the model. Thirdly, the trained models are validated on the test dataset, and the results of experiments show the following: if the types of pavement distresses are not distinguished, the pixel accuracy (PA) values of the recognition models using ResNet-34 and ResNet-50 as basic classification networks are 97.336% and 95.772%, respectively, on the validation set. When the types of distresses are distinguished, the PA values of models using the two classification networks are 66.103% and 44.953%, respectively. For the model using ResNet-34, the category pixel accuracy (CPA) and intersection over union (IoU) of the identification of areas with no distress are 99.276% and 99.059%, respectively. For areas featuring distresses in the images, the CPA and IoU of the model are the highest for the identification of patches, at 82.774% and 73.778%, and are the lowest for alligator cracks, at 14.077% and 12.581%, respectively.http://dx.doi.org/10.1155/2021/5586615
spellingShingle Deru Li
Zhongdong Duan
Xiaoyang Hu
Dongchang Zhang
Pixel-Level Recognition of Pavement Distresses Based on U-Net
Advances in Materials Science and Engineering
title Pixel-Level Recognition of Pavement Distresses Based on U-Net
title_full Pixel-Level Recognition of Pavement Distresses Based on U-Net
title_fullStr Pixel-Level Recognition of Pavement Distresses Based on U-Net
title_full_unstemmed Pixel-Level Recognition of Pavement Distresses Based on U-Net
title_short Pixel-Level Recognition of Pavement Distresses Based on U-Net
title_sort pixel level recognition of pavement distresses based on u net
url http://dx.doi.org/10.1155/2021/5586615
work_keys_str_mv AT deruli pixellevelrecognitionofpavementdistressesbasedonunet
AT zhongdongduan pixellevelrecognitionofpavementdistressesbasedonunet
AT xiaoyanghu pixellevelrecognitionofpavementdistressesbasedonunet
AT dongchangzhang pixellevelrecognitionofpavementdistressesbasedonunet