{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:32:54Z","timestamp":1770064374513,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20208-9","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T05:01:53Z","timestamp":1727067713000},"page":"27925-27947","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CiC-NET: a real-time semantic segmentation network for dam surface crack detection"],"prefix":"10.1007","volume":"84","author":[{"given":"Linjing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rashid","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"issue":"9","key":"20208_CR1","first-page":"35","volume":"40","author":"Q Huang","year":"2021","unstructured":"Huang Q, Liu D, Wei X et al (2021) Analysis of the most reasons for the number of dams built in China in the world. J Hydroelectric Eng 40(9):35\u201345","journal-title":"J Hydroelectric Eng"},{"key":"20208_CR2","doi-asserted-by":"publisher","unstructured":"Feng C, Zhang H, Wang S, et al (2021) Research on intelligent detection method for apparent crack damage of hydropower station overflow dam. Autom Inst 36(6):55\u201360. https:\/\/doi.org\/10.19557\/j.cnki.1001-9944.2021.06.012","DOI":"10.19557\/j.cnki.1001-9944.2021.06.012"},{"issue":"6","key":"20208_CR3","first-page":"7","volume":"41","author":"R Chen","year":"2021","unstructured":"Chen R, Wang H, Wang S et al (2021) Intelligent detection method of dam surface crack based on unmanned aerial vehicle. Prog Sci Technol Water Conservancy and Hydropower 41(6):7\u201312","journal-title":"Prog Sci Technol Water Conservancy and Hydropower"},{"key":"20208_CR4","doi-asserted-by":"crossref","unstructured":"Valen\u00e7a J, Julio, E (2018) MCrack-Dam: The scale-up of a method to assess cracks on concrete dams by image processing. The case study of Itaipu Dam, at the Brazil\u2013Paraguay border. J Civ Struct Health Monit 8(5):857\u2013866","DOI":"10.1007\/s13349-018-0309-0"},{"key":"20208_CR5","doi-asserted-by":"crossref","unstructured":"Wang N, Zhu H, Zhang X (2020) Multi-scale crack detection based on keypoint detection and minimal path technique. In: Intelligent robotics and applications: 13th international conference, ICIRA 2020, Kuala Lumpur, Malaysia, November 5\u20137, 2020, Proceedings 13 (pp 429-441) Springer International Publishing","DOI":"10.1007\/978-3-030-66645-3_36"},{"key":"20208_CR6","doi-asserted-by":"crossref","unstructured":"Zhou X, Xu L, Wang J (2019) Road crack edge detection based on wavelet transform. IOP Conference Series: Earth and Environmental Science 237(3)032132. IOP Publishing","DOI":"10.1088\/1755-1315\/237\/3\/032132"},{"key":"20208_CR7","doi-asserted-by":"publisher","unstructured":"Huang ZJ, Yang, XY, Xia J (2015) Inspection and treatment of water cracks on the upstream face of the dam of Danjiangkou Initial Project. People\u2019s Yangtze River 46(6):45\u201348+74. https:\/\/doi.org\/10.16232\/j.cnki.1001-4179.2015.06.014","DOI":"10.16232\/j.cnki.1001-4179.2015.06.014"},{"key":"20208_CR8","doi-asserted-by":"crossref","unstructured":"Li L, Tian Y, Deng X, Guo M, Le J, Zhang H (2022) Segmentation of schlieren images of flow field in combustor of scramjet based on improved fully convolutional network. Phys Fluids 34(11)","DOI":"10.1063\/5.0127589"},{"issue":"11","key":"20208_CR9","doi-asserted-by":"publisher","first-page":"5809","DOI":"10.1007\/s00371-022-02697-7","volume":"39","author":"A Roy","year":"2023","unstructured":"Roy A, Sharma LD, Shukla AK (2023) Multiclass CNN-based adaptive optimized filter for removal of impulse noise from digital images. Vis Comput 39(11):5809\u20135822","journal-title":"Vis Comput"},{"issue":"3","key":"20208_CR10","doi-asserted-by":"publisher","first-page":"035012","DOI":"10.1088\/2057-1976\/acbd53","volume":"9","author":"MK Chaitanya","year":"2023","unstructured":"Chaitanya MK, Sharma LD, Rahul J, Sharma D, Roy A (2023) Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders. Biomed PhysEng Express 9(3):035012","journal-title":"Biomed PhysEng Express"},{"key":"20208_CR11","doi-asserted-by":"crossref","unstructured":"Li L, Liu R, Ali R, Chen B, Lin H, Li Y, Zhang H (2024) DFP-Net: a crack segmentation method based on a feature pyramid network. Appl Sci 14(2:651","DOI":"10.3390\/app14020651"},{"issue":"7","key":"20208_CR12","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: A survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523\u20133542","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"20208_CR13","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1007\/s11063-019-10129-2","volume":"51","author":"Y Chen","year":"2020","unstructured":"Chen Y, Hu H (2020) Multi-layer adaptive feature fusion for semantic segmentation. Neural Process Lett 51(2):1081\u20131092","journal-title":"Neural Process Lett"},{"issue":"3","key":"20208_CR14","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1007\/s11063-021-10501-1","volume":"53","author":"F Qi","year":"2021","unstructured":"Qi F, Xie Z, Tang Z, Chen H (2021) Related study based on otsu watershed algorithm and new squeeze-and-excitation networks for segmentation and level classification of tea buds. Neural Process Lett 53(3):2261\u20132275","journal-title":"Neural Process Lett"},{"issue":"1","key":"20208_CR15","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11063-021-10629-0","volume":"54","author":"S Song","year":"2022","unstructured":"Song S, Bai T, Zhao Y, Zhang W, Yang C, Meng J, Su J (2022) A new convolutional neural network architecture for automatic segmentation of overlapping human chromosomes. Neural Process Lett 54(1):285\u2013301","journal-title":"Neural Process Lett"},{"key":"20208_CR16","doi-asserted-by":"publisher","first-page":"114892","DOI":"10.1109\/ACCESS.2020.3003638","volume":"8","author":"SL Lau","year":"2020","unstructured":"Lau SL, Chong EK, Yang X, Wang X (2020) Automated pavement crack segmentation using u-net-based convolutional neural network. Ieee Access 8:114892\u2013114899","journal-title":"Ieee Access"},{"key":"20208_CR17","doi-asserted-by":"publisher","first-page":"51446","DOI":"10.1109\/ACCESS.2020.2980086","volume":"8","author":"G Li","year":"2020","unstructured":"Li G, Wan J, He S, Liu Q, Ma B (2020) Semi-supervised semantic segmentation using adversarial learning for pavement crack detection. IEEE Access 8:51446\u201351459","journal-title":"IEEE Access"},{"key":"20208_CR18","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1007\/s12205-020-1896-y","volume":"24","author":"J Pang","year":"2020","unstructured":"Pang J, Zhang H, Feng C, Li L (2020) Research on crack segmentation method of hydro-junction project based on target detection network. KSCE J Civil Eng 24:2731\u20132741","journal-title":"KSCE J Civil Eng"},{"key":"20208_CR19","doi-asserted-by":"crossref","unstructured":"Li L, Zhang H, Pang J, Huang J (2019) Dam surface crack detection based on deep learning. In: Proceedings of the 2019 international conference on robotics, intelligent control and artificial intelligence (pp 738\u2013743)","DOI":"10.1145\/3366194.3366327"},{"issue":"18","key":"20208_CR20","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.3390\/app9183781","volume":"9","author":"Y Li","year":"2019","unstructured":"Li Y, Han Z, Xu H, Liu L, Li X, Zhang K (2019) YOLOv3-lite: A lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Appl Sci 9(18):3781","journal-title":"Appl Sci"},{"key":"20208_CR21","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767"},{"key":"20208_CR22","doi-asserted-by":"publisher","first-page":"105014","DOI":"10.1016\/j.autcon.2023.105014","volume":"154","author":"X He","year":"2023","unstructured":"He X, Tang Z, Deng Y, Zhou G, Wang Y, Li L (2023) UAV-based road crack object-detection algorithm. Autom Constr 154:105014","journal-title":"Autom Constr"},{"key":"20208_CR23","doi-asserted-by":"publisher","first-page":"105098","DOI":"10.1016\/j.autcon.2023.105098","volume":"156","author":"R Fu","year":"2023","unstructured":"Fu R, Cao M, Nov\u00e1k D, Qian X, Alkayem NF (2023) Extended efficient convolutional neural network for concrete crack detection with illustrated merits. Autom Constr 156:105098","journal-title":"Autom Constr"},{"key":"20208_CR24","doi-asserted-by":"crossref","unstructured":"Hu H, Li Z, He Z, Wang L, Cao S, Du W (2024) Road surface crack detection method based on improved YOLOv5 and vehicle-mounted images. Measurement pp 114443","DOI":"10.1016\/j.measurement.2024.114443"},{"issue":"1","key":"20208_CR25","doi-asserted-by":"publisher","first-page":"257","DOI":"10.3390\/s24010257","volume":"24","author":"M Sohaib","year":"2024","unstructured":"Sohaib M, Jamil S, Kim JM (2024) An ensemble approach for robust automated crack detection and segmentation in concrete structures. Sensors 24(1):257","journal-title":"Sensors"},{"issue":"3","key":"20208_CR26","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","volume":"15","author":"W Sun","year":"2018","unstructured":"Sun W, Wang R (2018) Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geosci Remote Sens Lett 15(3):474\u2013478","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"20208_CR27","first-page":"100144","volume":"18","author":"T Chen","year":"2020","unstructured":"Chen T, Cai Z, Zhao X, Chen C, Liang X, Zou T, Wang P (2020) Pavement crack detection and recognition using the architecture of segNet. J Ind Inf Integr 18:100144","journal-title":"J Ind Inf Integr"},{"issue":"6","key":"20208_CR28","doi-asserted-by":"publisher","first-page":"671","DOI":"10.3390\/jmse9060671","volume":"9","author":"H Fu","year":"2021","unstructured":"Fu H, Meng D, Li W, Wang Y (2021) Bridge crack semantic segmentation based on improved Deeplabv3+. J Mar Sci Eng 9(6):671","journal-title":"J Mar Sci Eng"},{"issue":"5","key":"20208_CR29","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.1109\/TIE.2017.2764844","volume":"65","author":"F-C Chen","year":"2018","unstructured":"Chen F-C, Jahanshahi MR (2018) NB-CNN: deep learning-based crack detection using convolutional neural network and Na\u00efve Bayes Data Fusion. IEEE Trans Ind Electron 65(5):4392\u20134400","journal-title":"IEEE Trans Ind Electron"},{"key":"20208_CR30","doi-asserted-by":"crossref","unstructured":"Akagic A, Buza E, Omanovic S, Karabegovic A (2018, May) Pavement crack detection using Otsu thresholding for image segmentation. In: 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO) (pp 1092-1097) IEEE","DOI":"10.23919\/MIPRO.2018.8400199"},{"key":"20208_CR31","doi-asserted-by":"crossref","unstructured":"Tang J, Mao, Y, Wang J, Wang L (2019) Multi-task enhanced dam crack image detection based on faster R-CNN. In 2019 IEEE 4th international conference on image, vision and computing (ICIVC) (pp 336-340) IEEE","DOI":"10.1109\/ICIVC47709.2019.8981093"},{"key":"20208_CR32","doi-asserted-by":"publisher","first-page":"104712","DOI":"10.1016\/j.autcon.2022.104712","volume":"147","author":"E Zhang","year":"2023","unstructured":"Zhang E, Shao L, Wang Y (2023) Unifying transformer and convolution for dam crack detection. Autom Constr 147:104712","journal-title":"Autom Constr"},{"issue":"7","key":"20208_CR33","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.3390\/s20072069","volume":"20","author":"C Feng","year":"2020","unstructured":"Feng C, Zhang H, Wang H, Wang S, Li Y (2020) Automatic pixel-level crack detection on dam surface using deep convolutional network. Sensors 20(7):2069","journal-title":"Sensors"},{"key":"20208_CR34","doi-asserted-by":"publisher","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","volume":"234","author":"Y Ren","year":"2020","unstructured":"Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, Shen X (2020) Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr Build Mater 234:117367","journal-title":"Constr Build Mater"},{"key":"20208_CR35","doi-asserted-by":"publisher","first-page":"108698","DOI":"10.1016\/j.measurement.2020.108698","volume":"170","author":"Y Wang","year":"2021","unstructured":"Wang Y, Song K, Liu J, Dong H, Yan Y, Jiang P (2021) RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks. Measurement 170:108698","journal-title":"Measurement"},{"key":"20208_CR36","unstructured":"Lin G, Xie A, Yang Y, Wang W, Xiong S (2021) Segmentation of Concrete Surface Crack Based on Improved U-Net. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) 33(4):645\u2013652"},{"issue":"13","key":"20208_CR37","doi-asserted-by":"publisher","first-page":"8117","DOI":"10.3390\/su14138117","volume":"14","author":"VP Golding","year":"2022","unstructured":"Golding VP, Gharineiat Z, Munawar HS, Ullah F (2022) Crack detection in concrete structures using deep learning. Sustainability 14(13):8117","journal-title":"Sustainability"},{"issue":"6","key":"20208_CR38","doi-asserted-by":"publisher","first-page":"065402","DOI":"10.1088\/1361-6501\/ac4b8d","volume":"33","author":"B Chen","year":"2022","unstructured":"Chen B, Zhang H, Li Y, Wang S, Zhou H, Lin H (2022) Quantify pixel-level detection of dam surface crack using deep learning. Measur Sci Technol 33(6):065402","journal-title":"Measur Sci Technol"},{"issue":"7","key":"20208_CR39","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.3390\/s20072069","volume":"20","author":"C Feng","year":"2020","unstructured":"Feng C, Zhang H, Wang H, Wang S, Li Y (2020) Automatic pixel-level crack detection on dam surface using deep convolutional network. Sensors 20(7):2069","journal-title":"Sensors"},{"key":"20208_CR40","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/ACCESS.2022.3233072","volume":"11","author":"P Jing","year":"2022","unstructured":"Jing P, Yu H, Hua Z, Xie S, Song C (2022) Road crack detection using deep neural network based on attention mechanism and residual structure. IEEE Access 11:919\u2013929","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20208-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20208-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20208-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T22:38:37Z","timestamp":1757111917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20208-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":40,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20208"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20208-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,23]]},"assertion":[{"value":"9 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2024","order":4,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing of interest"}},{"value":"No Human subject or animals are involved in the research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"All authors have mutually consented to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"All the authors have consented the Journal to publish this paper.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}}]}}