A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK

LU Jiaqi; YAO Zhidong

doi:10.13204/j.gyjzG20112504

Volume 51 Issue 5

Sep. 2021

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LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

Citation:

LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

Citation:

LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

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A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK

doi: 10.13204/j.gyjzG20112504

LU Jiaqi^1,2,
YAO Zhidong^1,2

1. Central Research Institute of Building and Construction (Shenzhen) Co., Ltd., Shenzhen 518055, China;
2. Shenzhen Engineering Research Center of Intelligent Inspection for Curtain Wall, Shenzhen 518055, China

Received Date: 2020-11-25
Available Online: 2021-09-16
Publish Date: 2021-09-16

Abstract

Abstract

Convolution neural network method of deep learning is a high robust method for image crack recognition at present, which is mainly divided into sliding window method and image segmentation method. Sliding window method has the problems of low precision of later threshold segmentation of cracks; global image segmentation method has the problem of serious unbalanced sample distribution between crack region and background region,which will affect the accuracy of crack segmentation. The method based on progressive cascade convolution neural network was used to detect concrete surface cracks:firstly, the fully convolution neural network was used to judge whether there were cracks in all the dense overlapped window areas in the image only once, and then the window blocks with cracks were extracted as the region of interest, and then the light-weight U-Net image segmentation network was used to act on the region of interest to extract the crack area accurately. Experimental results showed that the proposed progressive cascade convolution neural network was superior to sliding window method and global image segmentation method, and had a reliable application prospect.
- crack recognition,
- progressive cascade convolution neural network,
- fully convolutional network,
- region of interest,
- U-Net image segmentation

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References(20)

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