{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:56:31Z","timestamp":1778324191569,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["No. D5000210737"],"award-info":[{"award-number":["No. D5000210737"]}]},{"name":"the Key Research and Development Program of Shaanxi Province","award":["No. 2023-ZDLGY-53"],"award-info":[{"award-number":["No. 2023-ZDLGY-53"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62102320"],"award-info":[{"award-number":["No. 62102320"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19691-x","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T05:01:51Z","timestamp":1719464511000},"page":"16281-16299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation"],"prefix":"10.1007","volume":"84","author":[{"given":"Hui","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1453-2468","authenticated-orcid":false,"given":"Huanjie","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Qianyue","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Zhenwu","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yishi","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"issue":"8","key":"19691_CR1","doi-asserted-by":"publisher","first-page":"11710","DOI":"10.1109\/TITS.2021.3106647","volume":"23","author":"Z Qu","year":"2022","unstructured":"Qu Z, Chen W, Wang SY et al (2022) A crack detection algorithm for concrete pavement based on attention mechanism and multi-features fusion. IEEE Trans Intell Transp Syst 23(8):11710\u201311719","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19691_CR2","doi-asserted-by":"publisher","first-page":"104894","DOI":"10.1016\/j.autcon.2023.104894","volume":"152","author":"C Xiang","year":"2023","unstructured":"Xiang C, Guo J, Cao R et al (2023) A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios. Autom Constr 152:104894\u2013104908","journal-title":"Autom Constr"},{"key":"19691_CR3","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 et al (2023) Extended efficient convolutional neural network for concrete crack detection with illustrated merits. Autom Constr 156:105098\u2013105120","journal-title":"Autom Constr"},{"key":"19691_CR4","doi-asserted-by":"publisher","unstructured":"Yuan J, Wang N, Cai S et al. (2023) A multi-scale re-parameterization enhanced bilateral lightweight crack detection model for low-quality environments. Multimed Tools Appl 1-20. https:\/\/doi.org\/10.1007\/s11042-023-17664-0","DOI":"10.1007\/s11042-023-17664-0"},{"issue":"20","key":"19691_CR5","doi-asserted-by":"publisher","first-page":"59519","DOI":"10.1007\/s11042-023-17846-w","volume":"83","author":"M Sun","year":"2024","unstructured":"Sun M, Zhao H, Liu P et al (2024) A multi-task mean teacher with two stage decoder for semi-supervised crack detection. Multimed Tools Appl 83(20):59519\u201359536. https:\/\/doi.org\/10.1007\/s11042-023-17846-w","journal-title":"Multimed Tools Appl"},{"key":"19691_CR6","doi-asserted-by":"publisher","first-page":"3783","DOI":"10.1109\/ICCV48922.2021.00376","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"H Liu","year":"2021","unstructured":"Liu H, Miao X, Mertz C et al (2021) Crackformer: Transformer network for fine-grained crack detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3783\u20133792. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00376"},{"issue":"10","key":"19691_CR7","doi-asserted-by":"publisher","first-page":"18736","DOI":"10.1109\/TITS.2022.3154746","volume":"23","author":"Q Zhou","year":"2022","unstructured":"Zhou Q, Qu Z, Wang S et al (2022) A method of potentially promising network for crack detection with enhanced convolution and dynamic feature fusion. IEEE Trans Intell Transp Syst 23(10):18736\u201318745","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"19691_CR8","doi-asserted-by":"publisher","first-page":"16038","DOI":"10.1109\/TITS.2022.3147669","volume":"23","author":"Z Qu","year":"2022","unstructured":"Qu Z, Wang C, Wang S et al (2022) A method of hierarchical feature fusion and connected attention architecture for pavement crack detection. IEEE Trans Intell Transp Syst 23(9):16038\u201316047","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"27","key":"19691_CR9","doi-asserted-by":"publisher","first-page":"42465","DOI":"10.1007\/s11042-023-15201-7","volume":"82","author":"N Xu","year":"2023","unstructured":"Xu N, He L, Li Q (2023) Crack-Att Net: crack detection based on improved U-Net with parallel attention. Multimed Tools Appl 82(27):42465\u201342484. https:\/\/doi.org\/10.1007\/s11042-023-15201-7","journal-title":"Multimed Tools Appl"},{"key":"19691_CR10","doi-asserted-by":"publisher","first-page":"120291","DOI":"10.1016\/j.conbuildmat.2020.120291","volume":"258","author":"Z Dong","year":"2020","unstructured":"Dong Z, Wang J, Cui B et al (2020) Patch-based weakly supervised semantic segmentation network for crack detection. Constr Build Mater 258:120291\u2013120304","journal-title":"Constr Build Mater"},{"key":"19691_CR11","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/ICSIP55141.2022.9886030","volume-title":"Proceedings of the 7th international conference on signal and image processing","author":"J Quan","year":"2022","unstructured":"Quan J, Ge B, Wang M (2022) Weakly-supervised crack segmentation via scribble annotations. In: Proceedings of the 7th international conference on signal and image processing, pp 446\u2013451. https:\/\/doi.org\/10.1109\/ICSIP55141.2022.9886030"},{"issue":"12","key":"19691_CR12","doi-asserted-by":"publisher","first-page":"24083","DOI":"10.1109\/TITS.2022.3204853","volume":"23","author":"J K\u00f6nig","year":"2022","unstructured":"K\u00f6nig J, Jenkins MD, Mannion M et al (2022) Weakly-supervised surface crack segmentation by generating pseudo-labels using localization with a classifier and thresholding. IEEE Trans Intell Transp Syst 23(12):24083\u201324094","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19691_CR13","doi-asserted-by":"publisher","first-page":"103921","DOI":"10.1016\/j.compind.2023.103921","volume":"149","author":"Y Liu","year":"2023","unstructured":"Liu Y, Chen J, Hou J (2023) Learning position information from attention: end-to-end weakly supervised crack segmentation with GANs. Comput Ind 149:103921\u2013103936","journal-title":"Comput Ind"},{"key":"19691_CR14","doi-asserted-by":"publisher","first-page":"103545","DOI":"10.1016\/j.compind.2021.103545","volume":"133","author":"H Wang","year":"2021","unstructured":"Wang H, Li Y, Dang L et al (2021) Pixel-level tunnel crack segmentation using a weakly supervised annotation approach. Comput Ind 133:103545\u2013103555","journal-title":"Comput Ind"},{"key":"19691_CR15","doi-asserted-by":"publisher","first-page":"134134","DOI":"10.1016\/j.conbuildmat.2023.134134","volume":"411","author":"Z Wang","year":"2024","unstructured":"Wang Z, Leng Z, Zhang Z (2024) A weakly-supervised transformer-based hybrid network with multi-attention for pavement crack detection. Constr Build Mater 411:134134","journal-title":"Constr Build Mater"},{"issue":"11","key":"19691_CR16","doi-asserted-by":"publisher","first-page":"14527","DOI":"10.1007\/s10489-022-04212-w","volume":"53","author":"Z Al-Huda","year":"2022","unstructured":"Al-Huda Z, Peng B, Algburi RNA et al (2022) Weakly supervised pavement crack semantic segmentation based on multi-scale object localization and incremental annotation refinement. Appl Intell 53(11):14527\u201314546. https:\/\/doi.org\/10.1007\/s10489-022-04212-w","journal-title":"Appl Intell"},{"key":"19691_CR17","doi-asserted-by":"publisher","first-page":"134668","DOI":"10.1016\/j.conbuildmat.2023.134668","volume":"411","author":"T He","year":"2024","unstructured":"He T, Li H, Qian Z et al (2024) Research on weakly supervised pavement crack segmentation based on defect location by generative adversarial network and target re-optimization. Constr Build Mater 411:134668","journal-title":"Constr Build Mater"},{"key":"19691_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2024\/4863177","volume":"2024","author":"F Jiang","year":"2024","unstructured":"Jiang F, Ding Y, Song Y et al (2024) Weakly supervised fatigue crack detection in steel bridge girders using a proposed two-stage network training with a segmentation refinement module. Struct Control Health Monit 2024:1\u201321","journal-title":"Struct Control Health Monit"},{"key":"19691_CR19","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1109\/CVPR.2016.319","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"B Zhou","year":"2016","unstructured":"Zhou B, Khosla A, Lapedriza A et al (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2921\u20132929. https:\/\/doi.org\/10.1109\/CVPR.2016.319"},{"issue":"2","key":"19691_CR20","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1111\/mice.12481","volume":"35","author":"M Wang","year":"2020","unstructured":"Wang M, Cheng JCP (2020) A unified convolutional neural network integrated with conditional random field for pipe defect segmentation. Comput-Aided Civil Infrastruct Eng 35(2):162\u2013177","journal-title":"Comput-Aided Civil Infrastruct Eng"},{"key":"19691_CR21","doi-asserted-by":"publisher","first-page":"12275","DOI":"10.1109\/CVPR42600.2020.01229","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang J, Kan M et al (2020) Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12275\u201312284. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01229"},{"issue":"1","key":"19691_CR22","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.1038\/s41598-023-29665-y","volume":"13","author":"S Syed","year":"2023","unstructured":"Syed S, Anderssen KE, Stormo SK et al (2023) Weakly supervised semantic segmentation for MRI: exploring the advantages and disadvantages of class activation maps for biological image segmentation with soft boundaries. Sci Rep 13(1):2574\u20132586","journal-title":"Sci Rep"},{"key":"19691_CR23","doi-asserted-by":"publisher","first-page":"6813","DOI":"10.1145\/3503161.3547848","volume-title":"Proceedings of the 30th ACM international conference on multimedia","author":"H Zhao","year":"2022","unstructured":"Zhao H, Gu Z, Zheng B et al (2022) TransCNN-HAE: Transformer-CNN hybrid autoencoder for blind image inpainting. In: Proceedings of the 30th ACM international conference on multimedia, pp 6813\u20136821. https:\/\/doi.org\/10.1145\/3503161.3547848"},{"issue":"11","key":"19691_CR24","doi-asserted-by":"publisher","first-page":"7653","DOI":"10.1109\/TII.2022.3146142","volume":"18","author":"H Tao","year":"2022","unstructured":"Tao H, Lu M, Hu Z et al (2022) Attention-aggregated attribute-aware network with redundancy reduction convolution for video-based industrial smoke emission recognition. IEEE Trans Industr Inf 18(11):7653\u20137664","journal-title":"IEEE Trans Industr Inf"},{"key":"19691_CR25","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.patcog.2023.109761","volume":"143","author":"H Tao","year":"2023","unstructured":"Tao H, Duan Q, Lu M et al (2023) Learning discriminative feature representation with pixel-level supervision for forest smoke recognition. Pattern Recognit 143:109761\u2013109775. https:\/\/doi.org\/10.1016\/j.patcog.2023.109761","journal-title":"Pattern Recognit"},{"key":"19691_CR26","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.neunet.2023.11.033","volume":"170","author":"H Tao","year":"2024","unstructured":"Tao H, Duan Q (2024) Hierarchical attention network with progressive feature fusion for facial expression recognition. Neural Netw 170:337\u2013348","journal-title":"Neural Netw"},{"key":"19691_CR27","doi-asserted-by":"publisher","first-page":"12175","DOI":"10.1109\/CVPR52688.2022.01186","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"J Guo","year":"2022","unstructured":"Guo J, Han K, Wu H et al (2022) CMT: Convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12175\u201312185. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01186"},{"key":"19691_CR28","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.1109\/ICCV.2017.244","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"JY Zhu","year":"2017","unstructured":"Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2242\u20132251. https:\/\/doi.org\/10.1109\/ICCV.2017.244"},{"key":"19691_CR29","doi-asserted-by":"publisher","first-page":"129117","DOI":"10.1016\/j.conbuildmat.2022.129117","volume":"358","author":"H Zhang","year":"2022","unstructured":"Zhang H, Qian Z, Tan Y et al (2022) Investigation of pavement crack detection based on deep learning method using weakly supervised instance segmentation framework. Constr Build Mater 358:129117","journal-title":"Constr Build Mater"},{"key":"19691_CR30","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1109\/ICCV.2017.74","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"RR Selvaraju","year":"2017","unstructured":"Selvaraju RR, Cogswell M, Das A et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 618\u2013626. https:\/\/doi.org\/10.1109\/ICCV.2017.74"},{"key":"19691_CR31","first-page":"6000","volume-title":"Proceedings of the international conference on neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Noam S, Niki P et al (2017) Attention is all you need. In: Proceedings of the international conference on neural information processing systems, pp 6000\u20136010. https:\/\/dl.acm.org\/doi\/10.5555\/3295222.3295349"},{"key":"19691_CR32","first-page":"1","volume-title":"Proceedings of the international conference on learning representations","author":"A Dosovitskiy","year":"2021","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the international conference on learning representations, pp 1\u201321. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"19691_CR33","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/ICIVC52351.2021.9526968","volume-title":"Proceedings of the 6th international conference on image, vision and computing","author":"L Fan","year":"2021","unstructured":"Fan L, Wang Q, Wang Y (2021) Long-range comprehensive modeling for fine-grained visual classification. In: Proceedings of the 6th international conference on image, vision and computing, pp 196\u2013201. https:\/\/doi.org\/10.1109\/ICIVC52351.2021.9526968"},{"key":"19691_CR34","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/CACRE54574.2022.9834158","volume-title":"Proceedings of the 7th international conference on automation, control and robotics engineering","author":"H Duan","year":"2022","unstructured":"Duan H, Liu Y, Yan H et al (2022) Fourier ViT: A multi-scale vision transformer with Fourier transform for histopathological image classification. In: Proceedings of the 7th international conference on automation, control and robotics engineering, pp 189\u2013193. https:\/\/doi.org\/10.1109\/CACRE54574.2022.9834158"},{"key":"19691_CR35","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Proceedings of the European conference on computer vision","author":"N Carion","year":"2020","unstructured":"Carion N, Massa F, Synnaeve G et al (2020) End-to-end object detection with transformers. In: Proceedings of the European conference on computer vision, pp 213\u2013229. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"19691_CR36","doi-asserted-by":"publisher","first-page":"9992","DOI":"10.1109\/ICCV48922.2021.00986","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"Z Liu","year":"2021","unstructured":"Liu Z, Lin Y, Cao Y et al (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986"},{"key":"19691_CR37","doi-asserted-by":"publisher","first-page":"12114","DOI":"10.1109\/CVPR52688.2022.01181","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"X Dong","year":"2022","unstructured":"Dong X, Bao J, Chen D et al (2022) Cswin transformer: A general vision Transformer backbone with cross-shaped windows. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12114\u201312124. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01181"},{"key":"19691_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/MMSP55362.2022.9949177","volume-title":"Proceedings of the IEEE 24th international workshop on multimedia signal processing","author":"W Shi","year":"2022","unstructured":"Shi W, Xu J, Gao P (2022) SSformer: A lightweight Transformer for semantic segmentation. In: Proceedings of the IEEE 24th international workshop on multimedia signal processing, pp 1\u20135. https:\/\/doi.org\/10.1109\/MMSP55362.2022.9949177"},{"key":"19691_CR39","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1007\/978-3-030-58595-2_45","volume-title":"Proceedings of the European conference on computer vision","author":"Y Wang","year":"2020","unstructured":"Wang Y, Chen Y, Tao X et al (2020) Vcnet: A robust approach to blind image inpainting. In: Proceedings of the European conference on computer vision, pp 752\u2013768. https:\/\/doi.org\/10.1007\/978-3-030-58595-2_45"},{"key":"19691_CR40","doi-asserted-by":"publisher","first-page":"7516","DOI":"10.1109\/ICCV48922.2021.00744","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"Y Ma","year":"2021","unstructured":"Ma Y, Hua Y, Deng H et al (2021) Self-supervised vessel segmentation via adversarial learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7516\u20137525. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00744"},{"issue":"4","key":"19691_CR41","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","volume":"21","author":"F Yang","year":"2019","unstructured":"Yang F, Zhang L, Yu S et al (2019) Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans Intell Transp Syst 21(4):1525\u20131535","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19691_CR42","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Proceedings of the European conference on computer vision","author":"J Johnson","year":"2016","unstructured":"Johnson J, Alahi A, Li F (2016) Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European conference on computer vision, pp 694\u2013711. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"19691_CR43","doi-asserted-by":"publisher","first-page":"2414","DOI":"10.1109\/CVPR.2016.265","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"LA Gatys","year":"2016","unstructured":"Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2414\u20132423. https:\/\/doi.org\/10.1109\/CVPR.2016.265"},{"key":"19691_CR44","doi-asserted-by":"publisher","first-page":"1398","DOI":"10.1109\/ACSSC.2003.1292216","volume-title":"Proceedings of the thrity-seventh Asilomar conference on signals, systems & computers","author":"Z Wang","year":"2003","unstructured":"Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: Proceedings of the thrity-seventh Asilomar conference on signals, systems & computers, vol 2, pp 1398\u20131402. https:\/\/doi.org\/10.1109\/ACSSC.2003.1292216"},{"issue":"12","key":"19691_CR45","doi-asserted-by":"publisher","first-page":"5404","DOI":"10.1109\/TNNLS.2021.3072883","volume":"32","author":"C Zhang","year":"2021","unstructured":"Zhang C, Tang Y, Zhao C et al (2021) Multitask GANs for semantic segmentation and depth completion with cycle consistency. IEEE Trans Neural Netw Learn Syst 32(12):5404\u20135415","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"14","key":"19691_CR46","doi-asserted-by":"publisher","first-page":"5141","DOI":"10.3390\/s22145141","volume":"22","author":"X Li","year":"2022","unstructured":"Li X, Zheng Y, Chen B et al (2022) Dual attention-based industrial surface defect detection with consistency loss. Sensors 22(14):5141\u20135158","journal-title":"Sensors"},{"issue":"12","key":"19691_CR47","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","volume":"17","author":"Y Shi","year":"2016","unstructured":"Shi Y, Cui L, Qi Z et al (2016) Automatic road crack detection using random structured forests. IEEE Trans Intell Transp Syst 17(12):3434\u20133445","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19691_CR48","doi-asserted-by":"publisher","unstructured":"Chambon S, Moliard JM (2011) Automatic road pavement assessment with image processing: review and comparison. Int J Geophys, pp 1\u201320. https:\/\/doi.org\/10.1155\/2011\/989354","DOI":"10.1155\/2011\/989354"},{"key":"19691_CR49","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","volume":"338","author":"Y Liu","year":"2019","unstructured":"Liu Y, Yao J, Lu X et al (2019) DeepCrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338:139\u2013153","journal-title":"Neurocomputing"},{"key":"19691_CR50","doi-asserted-by":"publisher","first-page":"102907","DOI":"10.1016\/j.dsp.2020.102907","volume":"108","author":"K Jacob","year":"2021","unstructured":"Jacob K, Mark D, Mike M et al (2021) Optimized deep encoder-decoder methods for crack segmentation. Digit Signal Process 108:102907\u2013102918","journal-title":"Digit Signal Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19691-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19691-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19691-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T03:17:12Z","timestamp":1748056632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19691-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,27]]},"references-count":50,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19691"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19691-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,27]]},"assertion":[{"value":"2 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 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":"Conflicts of interest"}}]}}