基于全卷积神经网络的坝面裂纹检测方法研究

针对常规裂纹检测方法难适用于坝面裂纹检测的问题，提出一种基于全卷积神经网络的裂纹检测方法，主要解决混凝土坝面裂纹的定量化检测问题。该检测方法引入图像预处理与形态学后处理相结合的方式，分别对原始数据和预测结果进行优化，提升检测精度；并根据坝面数据特点对传统FCN（Fully Convolutional Network）网络进行改进，得到针对性更强的裂纹检测网络C-FCN（Crack Fully Convolutional Network），提升对裂纹检测的准确率；结合成像原理提取定量化信息，避免繁杂的相机标定工作，更加高效客观。利用该检测方法对实际工程进行实测，像素准确率、召回率和交并比分别达到75.13%、86.84%和60.15%，相比传统FCN网络，三项指标分别提升5.61%、16.56%、13.22%，同时定量化误差小于5%，裂纹平均宽度均不超过5 mm。该检测方法能够实现对坝面裂纹的精准识别和定量，为坝面后期风险评估和维护提供有力的数据支撑，具有显著的工程意义。

Study on detection method of dam surface cracks based on full convolution neural network

Aimed at the drawback of conventional crack detection methods not applicable to the detection of dam surface cracks, this paper presents a new method for quantitative detection based on a full convolution network (FCN). This method combines image preprocessing with morphological post-processing to achieve an improvement on detection accuracy through optimizing raw data and predictions. Oriented at dam surface data, it further improves the accuracy by modifying the traditional FCN network into a more targeted crack detection network, namely a crack full convolution network (C-FCN). And its quantitative information is extracted based on the imaging principle, which avoids complicated camera calibration and is more efficient and objective. We have applied it to in-situ measurements at a dam face and achieved a pixel accuracy of 75.13%, a recall rate of 86.84%, and an intersection ratio of 60.15%. These three indexes are improved by 5.61%, 16.56% and 13.22% respectively in comparison with the traditional FCN network. And the quantified error of detection is less than 5%, and the average opening of the cracks detected is less than 5 mm. Thus, our new detection method would provide a useful tool for dam surface risk assessment and maintenance of water dams.