Research on morphological wavelet operator for crack detection of asphalt pavement

2016 ◽  
Author(s):  
Guifang Wu ◽  
Xiuming Sun ◽  
Lipeng Zhou ◽  
Haitao Zhang ◽  
Jiexin Pu
Keyword(s):  
Crack Detection ◽  
Asphalt Pavement ◽  
Wavelet Operator

Detection of Crack Growth in Asphalt Pavement Through Use of Infrared Imaging

2017 ◽  
Vol 2645 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Yuchuan Du ◽  
Xiaoming Zhang ◽  
Feng Li ◽  
Lijun Sun
Keyword(s):  
Crack Growth ◽  
Temperature Difference ◽  
Crack Detection ◽  
Infrared Imaging ◽  
Asphalt Pavement ◽  
Atmospheric Temperature ◽  
Support Vector ◽  
Road Maintenance ◽  
Pavement Maintenance ◽  
Detection Model

The degree of crack growth in asphalt pavement is an important decision-making factor in road maintenance management. Automatic crack detection is based mainly on digital images; this factor makes effective detection of the degree of crack growth difficult. Infrared thermography was used, and a detection method for the degree of crack growth on the basis of infrared imaging was proposed. Infrared images included gray-level information on cracks and temperature information; the latter provided one additional dimension of information over ordinary images. Temperature information was used to detect the degree of crack growth. Atmospheric temperature was found to be the main factor that affected the temperature difference between a crack and the road surface. This temperature difference varied significantly for different extents of crack growth, and therefore this difference can be used to detect the degree of crack growth. Two classification functions that divided the degree of crack growth into three grades were obtained by classifying data through the use of a support vector machine. A suitable environmental condition for using the detection model was proposed. The experimental results showed that the average model error was 15.4%, which indicated a good application prospect and an improvement in economic benefit for pavement maintenance.

scholarly journals Deep convolution neural network for crack detection on asphalt pavement

2019 ◽  
Vol 1349 ◽  
pp. 012020 ◽  
Author(s):  
N A M Yusof ◽  
A Ibrahim ◽  
M H M Noor ◽  
N M Tahir ◽  
N M Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Asphalt Pavement ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

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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