A novel asphalt pavement crack detection algorithm based on multi-feature test of cross-section image

2018 ◽  
Vol 35 (3-4) ◽  
pp. 289-302
Author(s):  
Lili HE ◽  
Han ZHU ◽  
Zhanxu GAO
Keyword(s):  
Cross Section ◽  
Crack Detection ◽  
Asphalt Pavement ◽  
Detection Algorithm ◽  
Pavement Crack Detection

A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background

2020 ◽  
Vol 57 (14) ◽  
pp. 141031
Author(s):  
李刚 Li Gang ◽  
刘强伟 Liu Qiangwei ◽  
万健 Wan Jian ◽  
马彪 Ma Biao ◽  
李莹 Li Ying
Keyword(s):  
Crack Detection ◽  
Detection Algorithm ◽  
Low Rank ◽  
Deep Network ◽  
Complex Background ◽  
Pavement Crack Detection ◽  
Rank Group

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

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Li ◽  
Ranran Deng ◽  
Yingjie Zhang ◽  
Zhaoyun Sun ◽  
Xueli Hao ◽  
...  
Keyword(s):  
Crack Detection ◽  
Clustering Algorithm ◽  
Asphalt Pavement ◽  
Fruit Fly ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Density Peak ◽  
3D Data ◽  
Density Peak Clustering ◽  
Pavement Crack Detection

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.

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