Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data...

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Main Authors: Wei Li, Ranran Deng, Yingjie Zhang, Zhaoyun Sun, Xueli Hao, Ju Huyan
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2019/4302805
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spelling doaj-c6851719271a4cb587468cf51f9c043c2020-11-25T01:34:37ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/43028054302805Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak ClusteringWei Li0Ranran Deng1Yingjie Zhang2Zhaoyun Sun3Xueli Hao4Ju Huyan5School of Information Engineering, Chang’An University, Xi’an, Shaanxi 710064, ChinaSchool of Information Engineering, Chang’An University, Xi’an, Shaanxi 710064, ChinaSchool of Information Engineering, Chang’An University, Xi’an, Shaanxi 710064, ChinaSchool of Information Engineering, Chang’An University, Xi’an, Shaanxi 710064, ChinaSchool of Information Engineering, Chang’An University, Xi’an, Shaanxi 710064, ChinaCentre for Pavement and Transportation Technology (CPATT), Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, CanadaComplex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.http://dx.doi.org/10.1155/2019/4302805
collection DOAJ
language English
format Article
sources DOAJ
author Wei Li
Ranran Deng
Yingjie Zhang
Zhaoyun Sun
Xueli Hao
Ju Huyan
spellingShingle Wei Li
Ranran Deng
Yingjie Zhang
Zhaoyun Sun
Xueli Hao
Ju Huyan
Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
Mathematical Problems in Engineering
author_facet Wei Li
Ranran Deng
Yingjie Zhang
Zhaoyun Sun
Xueli Hao
Ju Huyan
author_sort Wei Li
title Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
title_short Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
title_full Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
title_fullStr Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
title_full_unstemmed Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering
title_sort three-dimensional asphalt pavement crack detection based on fruit fly optimisation density peak clustering
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2019-01-01
description Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.
url http://dx.doi.org/10.1155/2019/4302805
work_keys_str_mv AT weili threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
AT ranrandeng threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
AT yingjiezhang threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
AT zhaoyunsun threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
AT xuelihao threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
AT juhuyan threedimensionalasphaltpavementcrackdetectionbasedonfruitflyoptimisationdensitypeakclustering
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