|Table of Contents|

Pavement crack identification method based on deepconvolutional neural network fusion model(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2020年4期
Page:
1-13
Research Field:
道路工程
Publishing date:

Info

Title:
Pavement crack identification method based on deepconvolutional neural network fusion model
Author(s):
SUN Zhaoyun;  MA Zhidan;  LI Wei;  HAO Xueli;  SHEN Hao
(School of Information Engineering, Changan University, Xian 710064, Shaanxi, China)
Keywords:
road engineering;  pavement crack identification;  deep convolutional neural network;  multitarget detection model;  crack segmentation;  model fusion
PACS:
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DOI:
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Abstract:
Aimed at the existing methods of using deep learning technology for road surface crack recognition, most of the methods were still limited to the processing technology based on active feature extraction, and the source of the road image was targeted, resulting in the algorithm didnt have the ability to generalize the identification algorithm of the network still has specific requirements for the equipment, and the problem of the accuracy of the location of the road cracks was not high. A method based on deep convolutional neural network fusion model for pavement crack identification was proposed. Firstly, the multitarget SSD convolutional neural network model was applied to classify and detect pavement cracks. Then the depth residual network was used to improve the feature extraction structure of the SSD model, and the hyper parameters in the model were optimized according to the convergence degree of the loss function, which improves the accuracy of classification and location of pavement cracks. Secondly, the classification of cracks detection model was deviations from the pavement crack location. The UNet model based pavement crack segmentation method was proposed, and the feature extraction network of the model was improved. The crack segmentation accuracy was improved and the precise crack segmentation was realized. Finally, the crack was obtained. The classification detection model was merged with the segmentation model, two models were loaded, and the optimal weights obtained by the above training were imported, and the road surface image was judged according to the crack classification network to determine whether there was a crack, and if there was a crack, a specific category and confidence were given, and these were the information and the original crack image were input into the UNet segmentation network, the length and width of the linear crack and the area of the mesh crack were calculated according to the segmentation result. The results show that the recognition accuracy of the pavement crack identification method for transverse cracks, longitudinal cracks and map cracks are 86.6%, 87.2% and 85.3%, respectively. This method can not only give the category information of pavement cracks, but also give accurate positioning and geometric parameter information, and can be directly used for pavement condition evaluation. 5 tabs, 19 figs, 25 refs.

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Last Update: 2020-07-31