基于卷积神经网络与区域生长法的建筑裂缝识别

Building Crack Identification Based on Convolutional Neural Network and Regional Growth Method

基于卷积神经网络(CNN)的建筑裂缝识别结果大多为含有裂缝的面元图像,而并非裂缝本身.本文将CNN与区域生长法结合,提出一种两阶段方法用于提取像素级别的裂缝特征.利用数据扩充法建立裂缝图像数据库,选择包括Alexnet、Vgg16、Vgg19、Inception-V3和ResNet50的5种典型CNN用于裂缝识别.综合考虑样本图像的整体准确率,单张图像的裂缝识别精确度及背景图像的置信度,确定精度最高的CNN,得到裂缝面元图像;利用区域生长法对CNN识别的裂缝面元图像进行裂缝特征提取,得到像素级别的裂缝图像.研究表明,Inception-V3网络在裂缝识别中具有较高的识别精度;通过区域生长法进行裂缝特征提取,可以得到精度较高的像素级别裂缝特征图像.该研究提供了一种高精度的建筑裂缝识别方法.

The results of building crack recognition based on convolutional neural network(CNN)are bin images with crack,not the crack itself.A two-stage approach,which combined CNN and regional growth method,is proposed to extract a pixel-level crack feature.Data expansion method is used to establish crack image database.Five convolutional neural networks,Alexnet、Vgg16、Vgg19、Inception-V3 and ResNet50 are selected for crack identification.Comprehensively considered the test accuracy of image samples,the crack recognition accuracy of the single image and the confidence of background image,the optimal CNN are selected for crack identification,and the bin images with crack patch are obtained.The regional growth method is used to extract the crack feature from the crack images identified by CNN,and the pixel-level crack images are acquired.The research shows that the Inception-V3 network has higher recognition accuracy.By using the regional growth method to extract the crack feature,a high-precision pixel-level crack characteristic image can be obtained.The research provides a high-precision method for identifying building crack.