{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:59:51Z","timestamp":1776329991250,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["62001156"],"award-info":[{"award-number":["62001156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013058","name":"jiangsu provincial key research and development program","doi-asserted-by":"publisher","award":["BE 2019036"],"award-info":[{"award-number":["BE 2019036"]}],"id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11042-022-14168-1","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T20:16:08Z","timestamp":1668456968000},"page":"20899-20923","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["ROV-based binocular vision system for underwater structure crack detection and width measurement"],"prefix":"10.1007","volume":"82","author":[{"given":"Yunpeng","family":"Ma","sequence":"first","affiliation":[]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Qingwu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yaqin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Dabing","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"14168_CR1","doi-asserted-by":"publisher","first-page":"04019040","DOI":"10.1061\/(asce)cp.1943-5487.0000854","volume":"33","author":"M Alipour","year":"2019","unstructured":"Alipour M, Harris DK, Miller GR (2019) Robust pixel-level crack detection using deep fully convolutional neural networks. J Comput Civ Eng 33:04019040. https:\/\/doi.org\/10.1061\/(asce)cp.1943-5487.0000854","journal-title":"J Comput Civ Eng"},{"key":"14168_CR2","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.autcon.2016.06.008","volume":"71","author":"YJ Cha","year":"2016","unstructured":"Cha YJ, You K, Choi W (2016) Vision-based detection of loosened bolts using the Hough transform and support vector machines. Autom Constr 71:181\u2013188. https:\/\/doi.org\/10.1016\/j.autcon.2016.06.008","journal-title":"Autom Constr"},{"key":"14168_CR3","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"YJ Cha","year":"2017","unstructured":"Cha YJ, Choi W, B\u00fcy\u00fck\u00f6zt\u00fcrk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Civ Infrastruct Eng 32:361\u2013378. https:\/\/doi.org\/10.1111\/mice.12263","journal-title":"Comput Civ Infrastruct Eng"},{"key":"14168_CR4","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.3390\/s17081784","volume":"17","author":"Z Chen","year":"2017","unstructured":"Chen Z, Zhang Z, Dai F, Bu Y, Wang H (2017) Monocular vision-based underwater object detection. Sensors (Switzerland) 17:1784. https:\/\/doi.org\/10.3390\/s17081784","journal-title":"Sensors (Switzerland)"},{"key":"14168_CR5","doi-asserted-by":"publisher","first-page":"60100","DOI":"10.1109\/ACCESS.2018.2875889","volume":"6","author":"H Cho","year":"2018","unstructured":"Cho H, Yoon HJ, Jung JY (2018) Image-based crack detection using crack width transform (CWT) algorithm. IEEE Access 6:60100\u201360114. https:\/\/doi.org\/10.1109\/ACCESS.2018.2875889","journal-title":"IEEE Access"},{"key":"14168_CR6","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/JOE.2017.2735598","volume":"43","author":"JK Choi","year":"2018","unstructured":"Choi JK, Yokobiki T, Kawaguchi K (2018) ROV-based automated cable-laying system: application to DONET2 installation. IEEE J Ocean Eng 43:665\u2013676. https:\/\/doi.org\/10.1109\/JOE.2017.2735598","journal-title":"IEEE J Ocean Eng"},{"key":"14168_CR7","doi-asserted-by":"publisher","first-page":"2443","DOI":"10.3390\/RS12152443","volume":"12","author":"S Collings","year":"2020","unstructured":"Collings S, Martin TJ, Hernandez E, Edwards S, Filisetti A, Catt G, Marouchos A, Boyd M, Embry C (2020) Findings from a combined subsea LiDAR and multibeam survey at Kingston reef, Western Australia. Remote Sens 12:2443. https:\/\/doi.org\/10.3390\/RS12152443","journal-title":"Remote Sens"},{"key":"14168_CR8","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 (Switzerland) 20:2069. https:\/\/doi.org\/10.3390\/s20072069","journal-title":"Sensors (Switzerland)"},{"key":"14168_CR9","doi-asserted-by":"publisher","first-page":"15857","DOI":"10.1109\/TVT.2020.3036350","volume":"69","author":"Z Gong","year":"2020","unstructured":"Gong Z, Li C, Jiang F (2020) A machine learning-based approach for auto-detection and localization of targets in underwater acoustic Array networks. IEEE Trans Veh Technol 69:15857\u201315866. https:\/\/doi.org\/10.1109\/TVT.2020.3036350","journal-title":"IEEE Trans Veh Technol"},{"key":"14168_CR10","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/J.ENGSTRUCT.2019.03.089","volume":"189","author":"E Gotts\u00e4ter","year":"2019","unstructured":"Gotts\u00e4ter E, Johansson M, Plos M, Larsson Ivanov O (2019) Crack widths in base restrained walls subjected to restraint loading. Eng Struct 189:272\u2013285. https:\/\/doi.org\/10.1016\/J.ENGSTRUCT.2019.03.089","journal-title":"Eng Struct"},{"key":"14168_CR11","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.oceaneng.2019.03.044","volume":"181","author":"S Hachicha","year":"2019","unstructured":"Hachicha S, Zaoui C, Dallagi H, Nejim S, Maalej A (2019) Innovative design of an underwater cleaning robot with a two arm manipulator for hull cleaning. Ocean Eng 181:303\u2013313. https:\/\/doi.org\/10.1016\/j.oceaneng.2019.03.044","journal-title":"Ocean Eng"},{"key":"14168_CR12","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1007\/S11042-020-09752-2","volume":"80","author":"N Hassan","year":"2021","unstructured":"Hassan N, Ullah S, Bhatti N, Mahmood H, Zia M (2021) The Retinex based improved underwater image enhancement. Multimed Tools Appl 80:1839\u20131857. https:\/\/doi.org\/10.1007\/S11042-020-09752-2","journal-title":"Multimed Tools Appl"},{"key":"14168_CR13","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/S12555-019-0646-8","volume":"18","author":"S Hong","year":"2020","unstructured":"Hong S, Kim J (2020) Three-dimensional visual mapping of underwater ship Hull surface using piecewise-planar SLAM. Int J Control Autom Syst 18:564\u2013574. https:\/\/doi.org\/10.1007\/S12555-019-0646-8","journal-title":"Int J Control Autom Syst"},{"key":"14168_CR14","doi-asserted-by":"publisher","unstructured":"Huang D, Wang Y, Song W et al (2018) Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. In: lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer Verlag, pp 453\u2013465. https:\/\/doi.org\/10.1007\/978-3-319-73603-7_37","DOI":"10.1007\/978-3-319-73603-7_37"},{"key":"14168_CR15","doi-asserted-by":"publisher","unstructured":"Huang Z, Wan L, Sheng M et al (2019) An underwater image enhancement method for simultaneous localization and mapping of autonomous underwater vehicle. In: proceedings of 2019 3rd IEEE international conference on robotics and automation sciences, ICRAS 2019. Institute of Electrical and Electronics Engineers Inc., pp 137\u2013142. https:\/\/doi.org\/10.1109\/ICRAS.2019.8809014","DOI":"10.1109\/ICRAS.2019.8809014"},{"key":"14168_CR16","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.1109\/LRA.2020.2974710","volume":"5","author":"MJ Islam","year":"2020","unstructured":"Islam MJ, Xia Y, Sattar J (2020) Fast underwater image enhancement for improved visual perception. IEEE Robot Autom Lett 5:3227\u20133234. https:\/\/doi.org\/10.1109\/LRA.2020.2974710","journal-title":"IEEE Robot Autom Lett"},{"key":"14168_CR17","doi-asserted-by":"publisher","first-page":"103019","DOI":"10.1016\/j.autcon.2019.103019","volume":"110","author":"S Jin","year":"2020","unstructured":"Jin S, Lee SE, Hong JW (2020) A vision-based approach for autonomous crack width measurement with flexible kernel. Autom Constr 110:103019. https:\/\/doi.org\/10.1016\/j.autcon.2019.103019","journal-title":"Autom Constr"},{"key":"14168_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/APP11062750","volume":"11","author":"P Kot","year":"2021","unstructured":"Kot P, Muradov M, Gkantou M, Kamaris GS, Hashim K, Yeboah D (2021) Recent advancements in non-destructive testing techniques for structural health monitoring. Appl Sci 11:1\u201328. https:\/\/doi.org\/10.3390\/APP11062750","journal-title":"Appl Sci"},{"key":"14168_CR19","doi-asserted-by":"publisher","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","volume":"25","author":"CY Li","year":"2016","unstructured":"Li CY, Guo JC, Cong RM, Pang YW, Wang B (2016) Underwater image enhancement by Dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25:5664\u20135677. https:\/\/doi.org\/10.1109\/TIP.2016.2612882","journal-title":"IEEE Trans Image Process"},{"key":"14168_CR20","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.3390\/APP9173573","volume":"9","author":"S Li","year":"2019","unstructured":"Li S, Yang W, Xu L, Li C (2019) An environmental perception framework for robotic fish formation based on machine learning methods. Appl Sci 9:3573. https:\/\/doi.org\/10.3390\/APP9173573","journal-title":"Appl Sci"},{"key":"14168_CR21","doi-asserted-by":"publisher","unstructured":"Li L, Zhang H, Pang J, Huang J (2019) Dam surface crack detection based on deep learning. In: ACM International Conference Proceeding Series. Association for Computing Machinery, pp 738\u2013743. https:\/\/doi.org\/10.1145\/3366194.3366327","DOI":"10.1145\/3366194.3366327"},{"key":"14168_CR22","doi-asserted-by":"publisher","unstructured":"Lu P, Liu Q, Guo J (2016) Camera calibration implementation based on zhang zhengyou plane method. In: Lecture Notes in Electrical Engineering https:\/\/doi.org\/10.1007\/978-3-662-48386-2_4","DOI":"10.1007\/978-3-662-48386-2_4"},{"key":"14168_CR23","doi-asserted-by":"publisher","unstructured":"Marques TP, Branzan Albu A (2020) L2UWE: a framework for the efficient enhancement of low-light underwater images using local contrast and multi-scale fusion. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00277","DOI":"10.1109\/CVPRW50498.2020.00277"},{"key":"14168_CR24","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1007\/s10514-018-9797-3","volume":"43","author":"N Muhammad","year":"2019","unstructured":"Muhammad N, Fuentes-Perez JF, Tuhtan JA, Toming G, Musall M, Kruusmaa M (2019) Map-based localization and loop-closure detection from a moving underwater platform using flow features. Auton Robot 43:1419\u20131434. https:\/\/doi.org\/10.1007\/s10514-018-9797-3","journal-title":"Auton Robot"},{"key":"14168_CR25","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.3390\/APP11062797","volume":"11","author":"F Mu\u00f1oz","year":"2021","unstructured":"Mu\u00f1oz F, Cervantes-Rojas JS, Valdovinos JM, Sandre-Hern\u00e1ndez O, Salazar S, Romero H (2021) Dynamic neural network-based adaptive tracking control for an autonomous underwater vehicle subject to modeling and parametric uncertainties. Appl Sci 11:2797. https:\/\/doi.org\/10.3390\/APP11062797","journal-title":"Appl Sci"},{"key":"14168_CR26","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1080\/15732479.2019.1670215","volume":"16","author":"A Nasr","year":"2020","unstructured":"Nasr A, Kjellstr\u00f6m E, Bj\u00f6rnsson I, Honfi D, Ivanov OL, Johansson J (2020) Bridges in a changing climate: a study of the potential impacts of climate change on bridges and their possible adaptations. Struct Infrastruct Eng 16:738\u2013749. https:\/\/doi.org\/10.1080\/15732479.2019.1670215","journal-title":"Struct Infrastruct Eng"},{"key":"14168_CR27","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.autcon.2018.07.008","volume":"94","author":"H Nhat-Duc","year":"2018","unstructured":"Nhat-Duc H, Nguyen QL, Tran VD (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Autom Constr 94:203\u2013213. https:\/\/doi.org\/10.1016\/j.autcon.2018.07.008","journal-title":"Autom Constr"},{"key":"14168_CR28","doi-asserted-by":"publisher","unstructured":"Nowald N, Ratmeyer V, Wefer G (2016) MARUM-Squid - A powerful, yet compact 2000 m ROV system designed for marine research operations from smaller vessels. OCEANS 2016 MTS\/IEEE Monterey 1\u20134. https:\/\/doi.org\/10.1109\/OCEANS.2016.7761353.","DOI":"10.1109\/OCEANS.2016.7761353"},{"key":"14168_CR29","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1002\/rob.21907","volume":"36","author":"A Palomer","year":"2019","unstructured":"Palomer A, Ridao P, Ribas D (2019) Inspection of an underwater structure using point-cloud SLAM with an AUV and a laser scanner. J F Robot 36:1333\u20131344. https:\/\/doi.org\/10.1002\/rob.21907","journal-title":"J F Robot"},{"key":"14168_CR30","doi-asserted-by":"publisher","first-page":"123896","DOI":"10.1016\/j.conbuildmat.2021.123896","volume":"299","author":"X Peng","year":"2021","unstructured":"Peng X, Zhong X, Zhao C, Chen A, Zhang T (2021) A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning. Constr Build Mater 299:123896. https:\/\/doi.org\/10.1016\/j.conbuildmat.2021.123896","journal-title":"Constr Build Mater"},{"key":"14168_CR31","doi-asserted-by":"publisher","unstructured":"Protasiuk R, Bibi A, Ghanem B (2019) Local color mapping combined with color transfer for underwater image enhancement. In: proceedings - 2019 IEEE winter conference on applications of computer vision, WACV 2019. Inst Electric Electron Eng Inc, pp 1433\u20131439. https:\/\/doi.org\/10.1109\/WACV.2019.00157","DOI":"10.1109\/WACV.2019.00157"},{"key":"14168_CR32","doi-asserted-by":"publisher","first-page":"04016056","DOI":"10.1061\/(asce)cp.1943-5487.0000627","volume":"31","author":"S Qiu","year":"2017","unstructured":"Qiu S, Wang W, Wang S, Wang KCP (2017) Methodology for accurate AASHTO PP67-10-based cracking quantification using 1-mm 3D pavement images. J Comput Civ Eng 31:04016056. https:\/\/doi.org\/10.1061\/(asce)cp.1943-5487.0000627","journal-title":"J Comput Civ Eng"},{"key":"14168_CR33","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) A YOLOv3: An Incremental Improvement. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"14168_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5772\/60526","volume":"12","author":"DL Rizzini","year":"2015","unstructured":"Rizzini DL, Kallasi F, Oleari F, Caselli S (2015) Investigation of vision-based underwater object detection with multiple datasets. Int J Adv Robot Syst 12:1\u201313. https:\/\/doi.org\/10.5772\/60526","journal-title":"Int J Adv Robot Syst"},{"key":"14168_CR35","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/JOE.2017.2774318","volume":"44","author":"T Salumae","year":"2019","unstructured":"Salumae T, Chemori A, Kruusmaa M (2019) Motion control of a hovering biomimetic four-fin underwater robot. IEEE J Ocean Eng 44:54\u201371. https:\/\/doi.org\/10.1109\/JOE.2017.2774318","journal-title":"IEEE J Ocean Eng"},{"key":"14168_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S11042-020-10187-Y","volume":"2021","author":"P Shi","year":"2021","unstructured":"Shi P, Lu L, Fan X, Xin Y, Ni J (2021) A novel underwater sonar image enhancement algorithm based on approximation spaces of random sets. Multimed Tools Appl 2021:1\u201316. https:\/\/doi.org\/10.1007\/S11042-020-10187-Y","journal-title":"Multimed Tools Appl"},{"key":"14168_CR37","doi-asserted-by":"publisher","first-page":"30810","DOI":"10.3390\/s151229831","volume":"2015","author":"M Shortis","year":"2015","unstructured":"Shortis M (2015) Calibration techniques for accurate measurements by underwater camera systems. Sensors (Switzerland) 2015:30810\u201330827","journal-title":"Sensors (Switzerland)"},{"key":"14168_CR38","doi-asserted-by":"publisher","unstructured":"Song W, Wang Y, Huang D, Tjondronegoro D (2018) A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer Verlag, pp 678\u2013688. https:\/\/doi.org\/10.1007\/978-3-030-00776-8_62","DOI":"10.1007\/978-3-030-00776-8_62"},{"key":"14168_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs12244106","volume":"12","author":"J Villa","year":"2020","unstructured":"Villa J, Aaltonen J, Virta S, Koskinen KT (2020) A co-operative autonomous offshore system for target detection using multi-sensor technology. Remote Sens 12:1\u201324. https:\/\/doi.org\/10.3390\/rs12244106","journal-title":"Remote Sens"},{"key":"14168_CR40","doi-asserted-by":"publisher","unstructured":"Wang C, Zhang Q, Lin S, et al (2019) Research and experiment of an underwater stereo vision system. OCEANS 2019-Marseille 2019:1-5. https:\/\/doi.org\/10.1109\/OCEANSE.2019.8867236.","DOI":"10.1109\/OCEANSE.2019.8867236"},{"key":"14168_CR41","doi-asserted-by":"publisher","first-page":"7895","DOI":"10.1109\/TVT.2020.2993715","volume":"69","author":"Y Wang","year":"2020","unstructured":"Wang Y, Ma X, Wang J, Wang H (2020) Pseudo-3D vision-inertia based underwater self-localization for AUVs. IEEE Trans Veh Technol 69:7895\u20137907. https:\/\/doi.org\/10.1109\/TVT.2020.2993715","journal-title":"IEEE Trans Veh Technol"},{"key":"14168_CR42","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1364\/prj.3.000275","volume":"3","author":"X Xue","year":"2015","unstructured":"Xue X, Pan D, Zhang X, Luo B, Chen J, Guo H (2015) Faraday anomalous dispersion optical filter at ^133Cs weak 459 nm transition. Photonics Res 3:275. https:\/\/doi.org\/10.1364\/prj.3.000275","journal-title":"Photonics Res"},{"key":"14168_CR43","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1109\/LSP.2015.2409203","volume":"22","author":"Q Yang","year":"2015","unstructured":"Yang Q (2015) Local smoothness enforced cost volume regularization for fast stereo correspondence. IEEE Signal Process Lett 22:1429\u20131433. https:\/\/doi.org\/10.1109\/LSP.2015.2409203","journal-title":"IEEE Signal Process Lett"},{"key":"14168_CR44","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1111\/mice.12412","volume":"33","author":"X Yang","year":"2018","unstructured":"Yang X, Li H, Yu Y, Luo X, Huang T, Yang X (2018) Automatic pixel-level crack detection and measurement using fully convolutional network. Comput Civ Infrastruct Eng 33:1090\u20131109. https:\/\/doi.org\/10.1111\/mice.12412","journal-title":"Comput Civ Infrastruct Eng"},{"key":"14168_CR45","doi-asserted-by":"publisher","first-page":"2868","DOI":"10.3390\/APP11062868","volume":"11","author":"C Yang","year":"2021","unstructured":"Yang C, Chen J, Li Z, Huang Y (2021) Structural crack detection and recognition based on deep learning. Appl Sci 11:2868. https:\/\/doi.org\/10.3390\/APP11062868","journal-title":"Appl Sci"},{"key":"14168_CR46","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1111\/mice.12141","volume":"30","author":"CM Yeum","year":"2015","unstructured":"Yeum CM, Dyke SJ (2015) Vision-based automated crack detection for bridge inspection. Comput Civ Infrastruct Eng 30:759\u2013770. https:\/\/doi.org\/10.1111\/mice.12141","journal-title":"Comput Civ Infrastruct Eng"},{"key":"14168_CR47","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.imavis.2016.11.018","volume":"57","author":"D Zhang","year":"2017","unstructured":"Zhang D, Li Q, Chen Y, Cao M, He L, Zhang B (2017) An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection. Image Vis Comput 57:130\u2013146. https:\/\/doi.org\/10.1016\/j.imavis.2016.11.018","journal-title":"Image Vis Comput"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14168-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14168-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14168-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T09:19:43Z","timestamp":1685006383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14168-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":47,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["14168"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14168-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]},"assertion":[{"value":"4 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2022","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 there is no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}