{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:29:21Z","timestamp":1772749761237,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"China Construction Sixth Engineering Bureau Research and development project","award":["CSCEC6B-2022-Z-2"],"award-info":[{"award-number":["CSCEC6B-2022-Z-2"]}]},{"name":"Hebei Province Full-time Top-level Talents Introduction Project","award":["2020HBQZYC013"],"award-info":[{"award-number":["2020HBQZYC013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01503-y","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T23:03:22Z","timestamp":1722035002000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-time detection and geometric analysis algorithm for concrete cracks based on the improved U-net model"],"prefix":"10.1007","volume":"21","author":[{"given":"Qian","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Longxuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Liulu","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"issue":"20","key":"1503_CR1","doi-asserted-by":"publisher","first-page":"5950","DOI":"10.3390\/ma14205950","volume":"14","author":"Y Li","year":"2021","unstructured":"Li, Y., Zhang, J.H., Guan, Z.G., et al.: Experimental study on the correlation between crack width and crack depth of RC beams. Materials 14(20), 5950 (2021)","journal-title":"Materials"},{"key":"1503_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103484","volume":"124","author":"W Wang","year":"2021","unstructured":"Wang, W., Hu, W., Wang, W., et al.: Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network. Autom. Constr. 124, 103484 (2021)","journal-title":"Autom. Constr."},{"key":"1503_CR3","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/j.conbuildmat.2019.03.080","volume":"210","author":"Z Zeng","year":"2019","unstructured":"Zeng, Z., Wang, J., Shen, S., et al.: Experimental study on evolution of mechanical properties of CRTS III ballastless slab track under fatigue load. Constr. Build. Mater. 210, 639\u2013649 (2019)","journal-title":"Constr. Build. Mater."},{"issue":"2","key":"1503_CR4","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s12205-015-0461-6","volume":"20","author":"H Shanbao","year":"2016","unstructured":"Shanbao, H., Shijie, Z., Jinping, O.: A stereovision-based crack width detection approach for concrete surface assessment. KSCE J. Civ. Eng. 20(2), 803\u2013812 (2016)","journal-title":"KSCE J. Civ. Eng."},{"issue":"1","key":"1503_CR5","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.autcon.2013.06.011","volume":"39","author":"RS Adhikari","year":"2014","unstructured":"Adhikari, R.S., Moselhi, O., Bagchi, A.: Image-based retrieval of concrete crack properties for bridge inspection. Autom. Constr. 39(1), 180\u2013194 (2014)","journal-title":"Autom. Constr."},{"issue":"3","key":"1503_CR6","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/data3030028","volume":"3","author":"G Kasthurirangan","year":"2018","unstructured":"Kasthurirangan, G.: Deep learning in data-driven pavement image analysis and automated distress detection: a review. Data 3(3), 28 (2018)","journal-title":"Data"},{"key":"1503_CR7","first-page":"46","volume":"1","author":"B Peng","year":"2018","unstructured":"Peng, B., Cai, X., Li, S., et al.: Automatic crack detection algorithm based on 3D virtual pavement. J. Chongqing Jiaotong Univ. (Natural Science) 1, 46\u201353 (2018)","journal-title":"J. Chongqing Jiaotong Univ. (Natural Science)"},{"issue":"2","key":"1503_CR8","first-page":"560","volume":"51","author":"Z Guo","year":"2020","unstructured":"Guo, Z., Cai, B., Jiang, W.: A railway track detection method using LiDAR. J. Cent. S. Univ. (Science and Technology) 51(2), 560\u2013566 (2020)","journal-title":"J. Cent. S. Univ. (Science and Technology)"},{"issue":"2","key":"1503_CR9","first-page":"541","volume":"128","author":"JX Dong","year":"2021","unstructured":"Dong, J.X., Liu, J.H., Wang, N.N., et al.: Intelligent segmentation and measurement model for asphalt road cracks based on modified mask R-CNN algorithm. Comput. Model. Eng. Sci. 128(2), 541\u2013564 (2021)","journal-title":"Comput. Model. Eng. Sci."},{"issue":"4","key":"1503_CR10","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1177\/1475921720940068","volume":"20","author":"LX Zhang","year":"2020","unstructured":"Zhang, L.X., Shen, J.K., Zhu, B.J.: A research on an improved Unet-based concrete crack detection algorithm. Struct. Health Monit. 20(4), 1864\u20131879 (2020)","journal-title":"Struct. Health Monit."},{"key":"1503_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., et al.: Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3708\u20133712. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"1503_CR12","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","volume":"396","author":"XW Wu","year":"2020","unstructured":"Wu, X.W., Sahoo, D., Hoi, S.C.H.: Recent advances in deep learning for object detection. Neurocomputing 396, 39\u201364 (2020)","journal-title":"Neurocomputing"},{"issue":"6","key":"1503_CR13","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1007\/s11554-019-00925-3","volume":"17","author":"XF Li","year":"2019","unstructured":"Li, X.F., Wu, Y.R., Zhang, W., et al.: Deep learning methods in real-time image super-resolution: a survey. J. Real-Time Image Proc. 17(6), 1885\u20131909 (2019)","journal-title":"J. Real-Time Image Proc."},{"key":"1503_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2021.102913","volume":"43","author":"C Kaiwen","year":"2021","unstructured":"Kaiwen, C., Georg, R., Xin, X., et al.: Automated crack segmentation in close-range building fa\u00e7ade inspection images using deep learning techniques. J. Build. Eng. 43, 102913 (2021)","journal-title":"J. Build. Eng."},{"issue":"3","key":"1503_CR15","first-page":"332","volume":"16","author":"F Lili","year":"2021","unstructured":"Lili, F., Hongwei, Z., Ying, L., et al.: RAO-UNet: a residual attention and octave UNet for road crack detection via balance loss. IET Intell. Transport Syst. 16(3), 332\u2013343 (2021)","journal-title":"IET Intell. Transport Syst."},{"issue":"3","key":"1503_CR16","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1134\/S1062739122030176","volume":"58","author":"R Shunling","year":"2022","unstructured":"Shunling, R., Danyang, L., Qinghua, G., et al.: An intelligent detection method for open-pit slope fracture based on the improved mask R-CNN. J. Min. Sci. 58(3), 503\u2013518 (2022)","journal-title":"J. Min. Sci."},{"issue":"1","key":"1503_CR17","first-page":"1","volume":"2278","author":"Y Yalong","year":"2022","unstructured":"Yalong, Y., Zihao, Z., Liangliang, S., et al.: Research on pavement crack detection algorithm based on deep residual Unet neural network. J. Phys. Conf. Ser. 2278(1), 1\u201311 (2022)","journal-title":"J. Phys. Conf. Ser."},{"issue":"2","key":"1503_CR18","first-page":"1","volume":"29","author":"SP Babu","year":"2024","unstructured":"Babu, S.P., Pranjal, B., Kant, K.P.: Semantic segmentation of cracks on masonry surfaces using deep-learning techniques. Pract. Period. Struct. Des. Constr. 29(2), 1\u201318 (2024)","journal-title":"Pract. Period. Struct. Des. Constr."},{"key":"1503_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.istruc.2023.105780","volume":"59","author":"Y Jingyue","year":"2024","unstructured":"Jingyue, Y., Qiubing, R., Chao, J., et al.: Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment. Structures 59, 105780 (2024)","journal-title":"Structures"},{"issue":"1","key":"1503_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s11554-023-01398-1","volume":"21","author":"WQ Li","year":"2024","unstructured":"Li, W.Q., Mao, S.T., Mahoney, A.S., et al.: Deep learning models for bolus segmentation in videofluoroscopic swallow studies. J. Real-Time Image Proc. 21(1), 18 (2024)","journal-title":"J. Real-Time Image Proc."},{"issue":"6","key":"1503_CR21","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s11554-022-01249-5","volume":"19","author":"XX Wu","year":"2022","unstructured":"Wu, X.X., Zhang, Z.H., Guo, L.L., et al.: FAM: focal attention module for lesion segmentation of COVID-19 CT images. J. Real-Time Image Proc. 19(6), 1091\u20131104 (2022)","journal-title":"J. Real-Time Image Proc."},{"key":"1503_CR22","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cag.2021.04.014","volume":"97","author":"X Lin","year":"2021","unstructured":"Lin, X., Huang, O., Huang, W., et al.: Single image deraining via detail-guided efficient channel attention network. Comput. Graph. 97, 117\u2013125 (2021)","journal-title":"Comput. Graph."},{"key":"1503_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531\u201311539. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"2","key":"1503_CR24","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/JBHI.2019.2912935","volume":"24","author":"S Guan","year":"2020","unstructured":"Guan, S., Khan, A.A., Sikdar, S., et al.: Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal. IEEE J. Biomed. Health Inform. 24(2), 568\u2013576 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"S2","key":"1503_CR25","first-page":"316","volume":"37","author":"JH Xiang","year":"2020","unstructured":"Xiang, J.H., Xu, H.: Research on image semantic segmentation algorithm based on deep learning. Appl. Res. Comput. 37(S2), 316\u2013317 (2020)","journal-title":"Appl. Res. Comput."},{"key":"1503_CR26","doi-asserted-by":"publisher","first-page":"9872","DOI":"10.1109\/ACCESS.2018.2890127","volume":"7","author":"K Zhang","year":"2019","unstructured":"Zhang, K., Guo, Y.R., Wang, X.S., et al.: Multiple feature reweight densenet for image classification. IEEE Access 7, 9872\u20139880 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"1503_CR27","first-page":"1056","volume":"40","author":"XH Liang","year":"2020","unstructured":"Liang, X.H., Cheng, Y.Z., Zhang, R.J., et al.: Bridge crack classification and measurement method based on deep convolutional neural network. J. Comput. Appl. 40(4), 1056\u20131061 (2020)","journal-title":"J. Comput. Appl."},{"key":"1503_CR28","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.ijleo.2016.11.124","volume":"131","author":"M Naseri","year":"2016","unstructured":"Naseri, M., Heidari, S., Gheibi, R., et al.: A novel quantum binary images thinning algorithm: a quantum version of the Hilditch\u2019s algorithm. Optik Int. J. Light Electron Opt. 131, 678\u2013686 (2016)","journal-title":"Optik Int. J. Light Electron Opt."},{"key":"1503_CR29","unstructured":"Chen, G., Chen, N., Zeng, Y.: An improved OPTA fingerprint thinning algorithm based on neighborhood searching. In: International Conference on Computer Science and Information Processing, p. e31119. IEEE (2012)"},{"issue":"6","key":"1503_CR30","first-page":"967","volume":"51","author":"CQ Yang","year":"2021","unstructured":"Yang, C.Q., Li, S., Wang, B.K., et al.: High anti-noise extraction and identification method for concrete cracks based on dynamic threshold. J. Southeast Univ. (Natural Science Edition) 51(6), 967\u2013972 (2021)","journal-title":"J. Southeast Univ. (Natural Science Edition)"},{"key":"1503_CR31","doi-asserted-by":"crossref","unstructured":"Changlu, G., M\u00e1rton, S., Yangtao, H., et al.: Channel attention residual U-net for retinal vessel segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1185\u20131189. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414282"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01503-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01503-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01503-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T22:40:48Z","timestamp":1732488048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01503-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1503"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01503-y","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]},"assertion":[{"value":"21 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"139"}}