基于全卷积神经网络的单幅隧道图像裂纹提取算法

Crack extraction from single tunnel image based on fully convolutional neural network

提出一种基于全卷积神经网络的单幅隧道图像裂纹提取算法,能够有效避免复杂背景下的伪裂纹噪声点干扰,实现对隧道裂纹的精确分割。首先,构建深度残差网络模型提取裂纹特征;其次,使用改进的全卷积神经网络中的反卷积操作恢复裂纹特征图的尺寸和裂纹细节;为了提升裂缝提取的精细程度,提出一个细节修复模块来保持裂缝的完整性与边缘细节;最后,公开一个裂纹数据集NUAACrack-2000,包含2 000幅隧道裂纹图像与精准标注标签。实验表明,提出的算法在避免噪声点干扰方面优于传统图像分割算法;在保留提取裂纹的整体性以及边缘细节处理方面优于基于机器学习的主流裂纹提取算法。

A single tunnel image crack extraction algorithm based on fully convolutional neural network is proposed,which can effectively avoid the interference of pseudo-crack noise points in complex backgrounds and achieve accurate segmentation of tunnel cracks.First,a deep residual network model is constructed to extract crack features.Second,in order to recover the size of the crack feature map as well as the crack details,the feature map size is recovered using a deconvolution operation in an improved fully convolutional neural network.In order to improve the fineness of crack extraction,a detail repair module is proposed,which can maintain the integrity and edge details of cracks.Finally,a crack dataset NUAACrack-2000 is published,which contains 2000 tunnel crack images and accurately labeled labels.Extensive experiments show that the proposed algorithm outperforms traditional image segmentation algorithms in avoiding noise point interference.It is superior to the mainstream crack extraction algorithms based on machine learning in preserving the integrity of the extracted cracks and processing edge details.