基于卷积神经网络的轻量型裂缝分割方法

Method for lightweight crack segmentation based on convolutional neural network

针对通用分割模型作用在坝面混凝土表观裂缝时出现网络深度不断增加,导致模型参数过大,有效裂缝特征丢失,提出一种基于卷积神经网络的轻量型裂缝分割方法,以减小网络内存占用和特征丢失问题。网络采用编码-解码结构,利用深度可分离卷积和轻量特征提取模块构建级联编码器,解码器则融合编码器第二阶段的跨尺度信息,重构特征提取中丢失的像素级几何信息,以提高网络分割精度。试验结果表明:网络在自制坝面混凝土裂缝数据集上训练得到的模型大小为10.8 MB,相较于U-Net减小了90.8%,验证集测试下交并比为73.30%,像素精准率为85.36%,数据结果验证了网络在坝面裂缝分割方面的可行性,为提高坝面检测效率及坝面结构后期维护提供有力支撑。

When the general segmentation model is applied to the apparent cracks in the dam face concrete,the network suffers the problem of depth increasing that leads to excessive model parameters and certain loss of effective crack features.To reduce network memory occupation and feature loss,this paper develops a lightweight crack segmentation method based on a convolutional neural network.The network adopts an encoding-decoding structure,and uses a depth-separable convolution module and a lightweight feature extraction module to construct a cascade encoder;it is equipped with a decoder to fuse cross-scale information in the second stage of the encoder and to reconstruct the pixel-level geometric information lost in feature extraction to improve the accuracy of network segmentation.The experimental results show the model size of the network trained on the crack dataset of dam face concrete is 10.8 MB or a size reduction of 90.8% from U-Net,with its PA of 73.3% and IoU of 85.4%.The results verify the network is feasible in dam face crack segmentation and useful for improving the efficiency of dam face detection and maintenance.