A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images  

作　　者：Shuai Zhao Guokai Zhang Dongming Zhang Daoyuan Tan Hongwei Huang 

机构地区：[1]Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,999077,Hong Kong,China [2]School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai,200093,China [3]Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education,Tongji University,Shanghai,200092,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2023年第12期3105-3117,共13页岩石力学与岩土工程学报（英文版）

基　　金：support from the Ministry of Science and Tech-nology of the:People's Republic of China(Grant No.2021 YFB2600804);the Open Research Project Programme of the State Key Labor atory of Interet of Things for Smart City(University of Macao)(Grant No.SKL-IoTSC(UM)-2021-2023/ORPF/A19/2022);the General Research Fund(GRF)project(Grant No.15214722)from Research Grants Council(RGC)of Hong Kong Special Administrative Re gion Government of China are gratefully acknowledged.

摘　　要：This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

关 键 词：Crack segmentation Crack disjoint problem U-net Channel attention Position attention 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] TU45[建筑科学—岩土工程]