基于隧道快速检测车数据的公路隧道衬砌开裂识别模型研究

Research on Crack Identification of Highway Tunnel Linings Based on Data Obtained from the Testing Vehicle

随着公路隧道的不断建设,公路隧道的病害识别问题、养护问题日益增多,传统的隧道衬砌裂纹识别工作需要大量人工参与内业工作,效率不高。文章采用隧道快速检测车所采集的图像数据,利用卷积神经网络,研究得出了一套用于定向识别隧道检测车所拍摄的公路隧道衬砌开裂的模型。根据训练过程中发现的问题,针对裂纹数据集对神经网络识别效果的影响进行了对比研究,证明了按照不同裂纹走势分类图像所训练的神经网络识别能力较好,具有较好的适用性。

With the continuous construction of highway tunnels,the problems of disease identification and mainte⁃nance of highway tunnels are increasing.The traditional tunnel lining crack identification requires a lot of personnel to participate in the indoor work,resulting in low efficiency.Based on the image data collected by the rapid testing vehicle for highway tunnel condition,this paper uses the convolutional neural networks to train a set of models for recognizing the cracks in the highway tunnel linings.According to the problems found in the training process,a com⁃parative study on the effect of different crack data sets on the recognition effect of the neural network models is car⁃ried out,proving that the CNN model trained by images sorted by crack development trends has better recognition ability and better applicability.