基于递进式级联卷积神经网络的混凝土裂缝识别方法

卢佳祁; 姚志东

doi:10.13204/j.gyjzG20112504

基于递进式级联卷积神经网络的混凝土裂缝识别方法

doi: 10.13204/j.gyjzG20112504

卢佳祁^1,2,
姚志东^1,2

1. 中冶建筑研究总院(深圳)有限公司, 广东深圳 518055;
2. 深圳市建筑幕墙智能检测工程技术研究中心, 广东深圳 518055

基金项目:

国家重点研发计划项目（2019YFB1600700）；中冶建筑研究总院有限公司重大课题项目（YJA2018Kj01）。

详细信息

作者简介:
卢佳祁,男,1988年出生,硕士,工程师。电子信箱:15104573199@163.com

计量
- 文章访问数: 246
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-25
- 网络出版日期: 2021-09-16
- 刊出日期: 2021-09-16

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

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

摘要

摘要: 基于深度学习的卷积神经网络方法是目前图像裂缝识别鲁棒性较高的方法，主要分为滑动窗口法和图像分割法。滑动窗口法存在后期阈值分割裂缝精度不高的问题；全局图像分割法存在裂缝区域数据和背景区域数据严重不均衡问题，会对裂缝分割精度产生影响。采用了基于递进式级联卷积神经网络的方法对混凝土表面裂缝进行识别：首先采用全卷积神经网络一次性判断图像中所有密集重叠窗口区域内是否含有裂缝，然后将含有裂缝的窗口区块提取出来作为感兴趣区域，再基于轻量化的U-Net图像分割网络作用于感兴趣区域，将裂缝区域精确地提取出来。试验结果表明，所提出的基于递进式级联卷积神经网路的裂缝识别方法优于直接使用滑动窗口法和全局图像分割法，有着可靠的应用前景。
- 裂缝识别 /
- 递进式级联卷积神经网络 /
- 全卷积神经网络 /
- 感兴趣区域 /
- U-Net图像分割
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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