CNN-Transformer hybrid network for concrete dam crack patrol inspection

Regular patrol inspection of concrete dams can detect cracks at an early stage. However, conventional crack segmentation models based on deep learning (DL) are difficult to be deployed in resource-constrained mobile devices due to the large number of parameters. This paper describes a lightweight semantic segmentation model, termed as CrackTrNet, for images of concrete dam cracks. CrackTrNet is a hybrid U-shaped model based on convolutional neural network (CNN) and Vision Transformer. The CNN is adopted to extract low-level visual features and the Transformer focuses on learning the global contextual information. The results demonstrate that its segmentation accuracy can reach 97.60%, while the model size is only 34.86 MB, which is 66.12%–87.85% lower than that of current mainstream DL-based models. To make the model more practical, a crack inspection mobile application (APP) is developed using Android Studio. The integration of lightweight CrackTrNet and APP can effectively assist the intelligent inspection of dam cracks to ensure structural safety. •A lightweight hybrid network, CrackTrNet, is presented for semantic segmentation of dam cracks.•The integration of CNN and Transformer maximizes the preservation of global and local features.•Transfer learning and depthwise separable convolution are introduced to optimize the model size.•The mobile application is developed to assist patrol inspection of cracks in concrete dams.