{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:36:49Z","timestamp":1780587409437,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T00:00:00Z","timestamp":1707004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/s24031005","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T03:28:29Z","timestamp":1707103709000},"page":"1005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic Segmentation of Surface Cracks in Urban Comprehensive Pipe Galleries Based on Global Attention"],"prefix":"10.3390","volume":"24","author":[{"given":"Yuan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Control and Mechanical, Tianjin Chengjian University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoubin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Control and Mechanical, Tianjin Chengjian University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guili","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Control and Mechanical, Tianjin Chengjian University, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Li","sequence":"additional","affiliation":[{"name":"STECOL Corporation, Power Construction Corporation of China, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Wang","sequence":"additional","affiliation":[{"name":"STECOL Corporation, Power Construction Corporation of China, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huailei","family":"Chang","sequence":"additional","affiliation":[{"name":"STECOL Corporation, Power Construction Corporation of China, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1111\/j.1467-8667.2010.00674.x","article-title":"Beamlet transform-based technique for pavement crack detection and classification","volume":"25","author":"Ying","year":"2010","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_2","unstructured":"Xu, B., and Huang, Y. (2003). Development of an Automatic Pavement Surface Distress Inspection System, Center for Transportation Research, The University of Texas at Austin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Salman, M., Mathavan, S., Kamal, K., and Rahman, M. (2013, January 6\u20139). Pavement crack detection using the Gabor filter. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728529"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijleo.2015.09.147","article-title":"Detection crack in image using Otsu method and multiple filtering in image processing techniques","volume":"127","author":"Talab","year":"2016","journal-title":"Optik"},{"key":"ref_6","unstructured":"Oliveira, H., and Correia, P. (2009, January 24\u201328). Automatic road crack segmentation using entropy and image dynamic thresholding. Proceedings of the 2009 17th European Signal Processing Conference, Glasgow, UK."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"04015021","DOI":"10.1061\/(ASCE)CP.1943-5487.0000488","article-title":"Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images","volume":"30","author":"Sun","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sari, Y., Prakoso, P., and Baskara, A. (2019, January 18\u201321). Road Crack Detection using Support Vector Machine and OTSU Algorithm. Proceedings of the 2019 6th International Conference on Electric Vehicular Technology, Bali, Indonesia.","DOI":"10.1109\/ICEVT48285.2019.8993969"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1049\/iet-ipr.2019.0973","article-title":"Pavement crack detection network based on pyramid structure and attention mechanism","volume":"14","author":"Xiang","year":"2020","journal-title":"IET Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108698","DOI":"10.1016\/j.measurement.2020.108698","article-title":"RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks","volume":"170","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1007\/s10489-018-01396-y","article-title":"Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing","volume":"49","author":"Protopapadakis","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1177\/14759217211053546","article-title":"Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm","volume":"21","author":"Yu","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103357","DOI":"10.1016\/j.autcon.2020.103357","article-title":"A spatial-channel hierarchical deep learning network for pixel-level automated crack detection","volume":"119","author":"Pan","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102907","DOI":"10.1016\/j.dsp.2020.102907","article-title":"Optimized deep encoder-decoder methods for crack segmentation","volume":"108","author":"Jenkins","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18392","DOI":"10.1109\/TITS.2022.3158670","article-title":"DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation","volume":"23","author":"Sun","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1177\/14759217211053776","article-title":"Efficient attention-based deep encoder and decoder for automatic crack segmentation","volume":"21","author":"Kang","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112107","DOI":"10.1016\/j.measurement.2022.112107","article-title":"CycleADC-Net: A crack segmentation method based on multi-scale feature fusion","volume":"204","author":"Yan","year":"2022","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4600916","DOI":"10.1109\/TGRS.2023.3327285","article-title":"AMFLW-YOLO: A Lightweight Network for Remote Sensing Image Detection Based on Attention Mechanism and Multi-scale Feature Fusion","volume":"61","author":"Peng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1111\/mice.12881","article-title":"Tiny-Crack-Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks","volume":"37","author":"Chu","year":"2022","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rehman, M., Ry, J., Nizami, I., and Chong, K. (2023). RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames. Comput. Biol. Med., 152.","DOI":"10.1016\/j.compbiomed.2022.106426"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TETCI.2023.3309626","article-title":"TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aghdam, M., Azad, R., Zarvani, M., and Merhof, D. (2023, January 17\u201321). Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation. Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia.","DOI":"10.1109\/ISBI53787.2023.10230337"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8227","DOI":"10.1109\/TPAMI.2023.3235826","article-title":"DAN: A Segmentation-Free Document Attention Network for Handwritten Document Recognition","volume":"45","author":"Coquenet","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_28","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_32","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/1005\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:54:43Z","timestamp":1760104483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/1005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,4]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24031005"],"URL":"https:\/\/doi.org\/10.3390\/s24031005","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,4]]}}}