{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:03:56Z","timestamp":1780632236572,"version":"3.54.1"},"reference-count":54,"publisher":"Elsevier BV","issue":"24","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":166,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer-Aided Civil and Infrastructure Engineering"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1111\/mice.13451","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T15:04:58Z","timestamp":1741187098000},"page":"3997-4013","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"title":["Generative adversarial network based on domain adaptation for crack segmentation in shadow environments"],"prefix":"10.1016","volume":"40","author":[{"given":"Yingchao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1111\/mice.13451_bb0010","doi-asserted-by":"crossref","first-page":"8675","DOI":"10.1007\/s00521-019-04359-7","article-title":"A dynamic ensemble learning algorithm for neural networks","volume":"32","author":"Alam","year":"2020","journal-title":"Neural Computing and Applications"},{"issue":"14","key":"10.1111\/mice.13451_bb0015","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1111\/mice.12844","article-title":"A sigmoid\u2010optimized encoder\u2013decoder network for crack segmentation with copy\u2010edit\u2010paste transfer learning","volume":"37","author":"\u00c7elik","year":"2022","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"8","key":"10.1111\/mice.13451_bb0020","doi-asserted-by":"crossref","first-page":"9339","DOI":"10.1109\/TPAMI.2023.3248294","article-title":"ADPL: Adaptive dual path learning for domain adaptation of semantic segmentation","volume":"45","author":"Cheng","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"17","key":"10.1111\/mice.13451_bb0025","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1111\/mice.13315","article-title":"Self\u2010training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation","volume":"39","author":"Chun","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.13451_bb0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.103606","article-title":"Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning","volume":"125","author":"Dais","year":"2021","journal-title":"Automation in Construction"},{"issue":"10","key":"10.1111\/mice.13451_bb0035","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500521","article-title":"Few\u2010shot pixel\u2010precise document layout segmentation via dynamic instance generation and local thresholding","volume":"33","author":"Nardin","year":"2023","journal-title":"International Journal of Neural Systems"},{"issue":"11","key":"10.1111\/mice.13451_bb0040","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065724500576","article-title":"Semi\u2010supervised semantic image segmentation by deep diffusion models and generative adversarial networks","volume":"34","author":"D\u00edaz\u2010Franc\u00e9s","year":"2024","journal-title":"International Journal of Neural Systems"},{"issue":"7","key":"10.1111\/mice.13451_bb0045","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/JAS.2023.123447","article-title":"Pavement cracks coupled with shadows: A new shadow\u2010crack dataset and a shadow\u2010removal\u2010oriented crack detection approach","volume":"10","author":"Fan","year":"2023","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1111\/mice.13451_bb0050","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neucom.2022.07.036","article-title":"An underwater dam crack image segmentation method based on multi\u2010level adversarial transfer learning","volume":"505","author":"Fan","year":"2022","journal-title":"Neurocomputing"},{"issue":"6","key":"10.1111\/mice.13451_bb0055","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065724500321","article-title":"Multitask adversarial networks based on extensive nonlinear spiking neuron models","volume":"34","author":"Fu","year":"2024","journal-title":"International Journal of Neural Systems"},{"issue":"9","key":"10.1111\/mice.13451_bb0060","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1111\/mice.12741","article-title":"Balanced semisupervised generative adversarial network for damage assessment from low\u2010data imbalanced\u2010class regime","volume":"36","author":"Gao","year":"2021","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"3","key":"10.1111\/mice.13451_bb0065","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3233\/ICA-230700","article-title":"Optimized instance segmentation by super\u2010resolution and maximal clique generation","volume":"30","author":"Garc\u00eda\u2010Aguilar","year":"2023","journal-title":"Integrated Computer\u2010Aided Engineering"},{"issue":"11","key":"10.1111\/mice.13451_bb0070","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1111\/mice.13451_bb0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105014","article-title":"UAV\u2010based road crack object\u2010detection algorithm","volume":"154","author":"He","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.13451_bb0080","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"574","author":"Ho","year":"2020","journal-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems"},{"key":"10.1111\/mice.13451_bb0085","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s00366-018-0611-9","article-title":"A novel method for asphalt pavement crack classification based on image processing and machine learning","volume":"35","author":"Hoang","year":"2019","journal-title":"Engineering with Computers"},{"issue":"12","key":"10.1111\/mice.13451_bb0090","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500600","article-title":"Enhancing robustness of medical image segmentation model with neural memory ordinary differential equation","volume":"33","author":"Hu","year":"2023","journal-title":"International Journal of Neural Systems"},{"issue":"12","key":"10.1111\/mice.13451_bb0095","doi-asserted-by":"crossref","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","article-title":"A style\u2010based generator architecture for generative adversarial networks","volume":"43","author":"Karras","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1111\/mice.13451_bb0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2023.107105","article-title":"Deep learning for crack detection on masonry fa\u00e7ades using limited data and transfer learning","volume":"76","author":"Katsigiannis","year":"2023","journal-title":"Journal of Building Engineering"},{"issue":"1","key":"10.1111\/mice.13451_bb0105","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500673","article-title":"Multilevel laser\u2010induced pain measurement with Wasserstein generative adversarial network\u2010gradient penalty model","volume":"34","author":"Leng","year":"2024","journal-title":"International Journal of Neural Systems"},{"issue":"11","key":"10.1111\/mice.13451_bb0110","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1111\/mice.12674","article-title":"Cross\u2010scene pavement distress detection by a novel transfer learning framework","volume":"36","author":"Li","year":"2021","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.13451_bb0115","doi-asserted-by":"crossref","unstructured":"Li, Y., Yuan, L., & Vasconcelos, N. (2019). Bidirectional learning for domain adaptation of semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA (pp. 6936\u20136945).","DOI":"10.1109\/CVPR.2019.00710"},{"issue":"11","key":"10.1111\/mice.13451_bb0120","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1111\/mice.12622","article-title":"Automated pavement crack detection and segmentation based on two\u2010step convolutional neural network","volume":"35","author":"Liu","year":"2020","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.13451_bb0125","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","article-title":"DeepCrack: A deep hierarchical feature learning architecture for crack segmentation","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"issue":"9","key":"10.1111\/mice.13451_bb0130","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.1109\/TII.2022.3233654","article-title":"Shape\u2010consistent one\u2010shot unsupervised domain adaptation for rail surface defect segmentation","volume":"19","author":"Ma","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"1","key":"10.1111\/mice.13451_bb0135","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/mice.12561","article-title":"Generative adversarial network for road damage detection","volume":"36","author":"Maeda","year":"2021","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"7","key":"10.1111\/mice.13451_bb0140","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/mice.12918","article-title":"Real\u2010time automatic crack detection method based on drone","volume":"38","author":"Meng","year":"2023","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"23","key":"10.1111\/mice.13451_bb0145","doi-asserted-by":"crossref","first-page":"18383","DOI":"10.1007\/s00500-023-09103-x","article-title":"Automated hyperparameter tuning for crack image classification with deep learning","volume":"27","author":"Ottoni","year":"2023","journal-title":"Soft Computing"},{"key":"10.1111\/mice.13451_bb0150","doi-asserted-by":"crossref","first-page":"6393","DOI":"10.1007\/s00521-019-04146-4","article-title":"FEMA: A finite element machine for fast learning","volume":"32","author":"Pereira","year":"2020","journal-title":"Neural Computing and Applications"},{"issue":"12","key":"10.1111\/mice.13451_bb0155","doi-asserted-by":"crossref","first-page":"3074","DOI":"10.1109\/TNNLS.2017.2682102","article-title":"A new neural dynamic classification algorithm","volume":"28","author":"Rafiei","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"10.1111\/mice.13451_bb0160","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TNNLS.2022.3190448","article-title":"Self\u2010supervised learning for electroencephalography","volume":"35","author":"Rafiei","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10.1111\/mice.13451_bb0165","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10916-024-02122-7","article-title":"Self\u2010supervised learning for near\u2010wild cognitive workload estimation","volume":"48","author":"Rafiei","year":"2024","journal-title":"Journal of Medical Systems"},{"issue":"12","key":"10.1111\/mice.13451_bb0170","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 Transactions on Intelligent Transportation Systems"},{"issue":"7","key":"10.1111\/mice.13451_bb0175","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1111\/mice.13119","article-title":"Self\u2010training approach for crack detection using synthesized crack images based on conditional generative adversarial network","volume":"39","author":"Shim","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"10","key":"10.1111\/mice.13451_bb0180","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high\u2010resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"10.1111\/mice.13451_bb0185","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065722500599","article-title":"Mixture 2D convolutions for 3D medical image segmentation","volume":"33","author":"Wang","year":"2023","journal-title":"International Journal of Neural Systems"},{"issue":"5","key":"10.1111\/mice.13451_bb0190","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500260","article-title":"A method based on evolutionary algorithms and channel attention mechanism to enhance cycle generative adversarial network performance for image translation","volume":"33","author":"Xue","year":"2023","journal-title":"International Journal of Neural Systems"},{"key":"10.1111\/mice.13451_bb0195","doi-asserted-by":"crossref","unstructured":"Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., & Zuo, W. (2017). Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI (pp. 2272\u20132281).","DOI":"10.1109\/CVPR.2017.107"},{"issue":"4","key":"10.1111\/mice.13451_bb0200","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","article-title":"Feature pyramid and hierarchical boosting network for pavement crack detection","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"9","key":"10.1111\/mice.13451_bb0205","doi-asserted-by":"crossref","first-page":"4535","DOI":"10.1109\/TAI.2024.3386149","article-title":"A novel applicable shadow resistant neural network model for high efficiency grid level pavement crack detection","volume":"5","author":"Yang","year":"2024","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"10.1111\/mice.13451_bb0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2020.103199","article-title":"Deep convolution neural network\u2010based transfer learning method for civil infrastructure crack detection","volume":"116","author":"Yang","year":"2020","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.13451_bb0215","doi-asserted-by":"crossref","unstructured":"Zhang, F., Tian, M., Li, Z., Xu, B., Lu, Q., Gao, C., & Sang, N. (2024). Lookup table meets local Laplacian filter: Pyramid reconstruction network for tone mapping. Proceedings of the 37th Conference on Neural Information Processing Systems, New Orleans, LA (pp. 57558\u201357569).","DOI":"10.52202\/075280-2510"},{"issue":"12","key":"10.1111\/mice.13451_bb0220","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1111\/mice.13171","article-title":"A controllable generative model for generating pavement crack images in complex scenes","volume":"39","author":"Zhang","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"4","key":"10.1111\/mice.13451_bb0225","doi-asserted-by":"crossref","first-page":"4474","DOI":"10.1109\/TITS.2023.3236247","article-title":"Integrated APC\u2010GAN and AttUNet framework for automated pavement crack pixel\u2010level segmentation: A new solution to small training datasets","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1111\/mice.13451_bb0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105646","article-title":"Crack segmentation using discrete cosine transform in shadow environments","volume":"166","author":"Zhang","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.13451_bb0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105375","article-title":"Network for robust and high\u2010accuracy pavement crack segmentation","volume":"162","author":"Zhang","year":"2024","journal-title":"Automation in Construction"},{"issue":"11","key":"10.1111\/mice.13451_bb0240","doi-asserted-by":"crossref","first-page":"18954","DOI":"10.1109\/TITS.2024.3416508","article-title":"Real\u2010time pavement damage detection with damage shape adaptation","volume":"25","author":"Zhang","year":"2024","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"3","key":"10.1111\/mice.13451_bb0245","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/JSEN.2021.3135388","article-title":"Road surface defects detection based on IMU sensor","volume":"22","author":"Zhang","year":"2021","journal-title":"IEEE Sensors Journal"},{"issue":"22","key":"10.1111\/mice.13451_bb0250","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1111\/mice.13231","article-title":"A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks","volume":"39","author":"Zhang","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.13451_bb0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104613","article-title":"Road damage detection using UAV images based on multi\u2010level attention mechanism","volume":"144","author":"Zhang","year":"2022","journal-title":"Automation in Construction"},{"issue":"7","key":"10.1111\/mice.13451_bb0260","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065724500333","article-title":"A stage\u2010wise residual attention generation adversarial network for mandibular defect repairing and reconstruction","volume":"34","author":"Zhong","year":"2024","journal-title":"International Journal of Neural Systems"},{"issue":"12","key":"10.1111\/mice.13451_bb0265","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/mice.13103","article-title":"A lightweight encoder\u2013decoder network for automatic pavement crack detection","volume":"39","author":"Zhu","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.13451_bb0270","doi-asserted-by":"crossref","unstructured":"Zhu, J.\u2010Y., Park, T., Isola, P., & Efros, A.A. (2017). Unpaired image\u2010to\u2010image translation using cycle\u2010consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy (pp. 2242\u20132251). https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"issue":"3","key":"10.1111\/mice.13451_bb0275","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TII.2022.3172995","article-title":"A bidirectional self\u2010rectifying network with Bayesian modeling for vision\u2010based crack detection","volume":"19","author":"Zhu","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"}],"container-title":["Computer-Aided Civil and Infrastructure Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1093968726012685?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1093968726012685?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/mice.13451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T09:20:42Z","timestamp":1773652842000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1093968726012685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":54,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["S1093968726012685"],"URL":"https:\/\/doi.org\/10.1111\/mice.13451","relation":{},"ISSN":["1093-9687"],"issn-type":[{"value":"1093-9687","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Generative adversarial network based on domain adaptation for crack segmentation in shadow environments","name":"articletitle","label":"Article Title"},{"value":"Computer-Aided Civil and Infrastructure Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1111\/mice.13451","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2025 The Author(s). Computer\u2010Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}