Automatic Tunnel Crack Detection Based on Unet and a Convolutional Neural Network with Alternately Updated Clique Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is timeconsuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on UNet and a convolutional neural network with alternately updated clique (CliqueNet), called UCliqueNet, to separate cracks from background in the tunnel images. A consumergrade DSCWX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into subimages with a resolution of 496 xc3x97 496 pixels. A total of 60,000 subimages were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the UCliqueNet with fully convolutional networks (FCN), Unet, Encoderxe2x80x93decoder network (SegNet) and the multiscale fusion crack detection (MFCD) algorithm using hypothesis testing, and itxe2x80x99s proved that the MIoU predicted by UCliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from xe2x88x9211.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections Automatic crack detection and segmentation plays a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and the performance of it is compared with other state of the art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmarkdataset on the internet for crack detection and segmentation, which is opensourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmarkdataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed for the crack inspection tasks of various concrete structures.