{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:15:08Z","timestamp":1784232908073,"version":"3.55.0"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005064","name":"Hebei Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["24465001D"],"award-info":[{"award-number":["24465001D"]}],"id":[{"id":"10.13039\/501100005064","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2025210053"],"award-info":[{"award-number":["F2025210053"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2024210051"],"award-info":[{"award-number":["F2024210051"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"publisher","award":["HJYB202516"],"award-info":[{"award-number":["HJYB202516"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"publisher","award":["BJK2024128"],"award-info":[{"award-number":["BJK2024128"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A20246"],"award-info":[{"award-number":["U22A20246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52572481"],"award-info":[{"award-number":["52572481"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104915","type":"journal-article","created":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T03:15:24Z","timestamp":1780802124000},"page":"104915","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["CoCrack: A completeness-oriented framework toward effective and efficient pavement crack detection"],"prefix":"10.1016","volume":"76","author":[{"given":"Xuewei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ze","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3326-3723","authenticated-orcid":false,"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104915_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102388","article-title":"From global challenges to local solutions: a review of cross-country collaborations and winning strategies in road damage detection","volume":"60","author":"Arya","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104915_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101525","article-title":"Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network","volume":"51","author":"Li","year":"2022","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104915_b0015","article-title":"Real-time semantic segmentation for autonomous driving: a review of CNNs, transformers, and beyond","volume":"36","author":"Elhassan","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inform. Sci."},{"key":"10.1016\/j.aei.2026.104915_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122552","article-title":"Integrating crack causal augmentation framework and dynamic binary threshold for imbalanced crack instance segmentation","volume":"240","author":"Lei","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104915_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103262","article-title":"Advancing road safety: a lightweight feature fusion model for robust road crack segmentation","volume":"65","author":"Khan","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104915_b0030","series-title":"in: 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)","first-page":"1327","article-title":"Design and implementation of a manipulator system for roadway crack sealing","author":"Zhu","year":"2019"},{"key":"10.1016\/j.aei.2026.104915_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103610","article-title":"Automatic lightweight networks for real-time road crack detection with DPSO","volume":"68","author":"Zhu","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104915_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105367","article-title":"An average pooling designed transformer for robust crack segmentation","volume":"162","author":"Chen","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0045","article-title":"Pavement crack detection with hybrid-window attentive vision transformers","volume":"116","author":"Xiao","year":"2023","journal-title":"Int. J. Appl. Earth Observations and Geoinformation"},{"key":"10.1016\/j.aei.2026.104915_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103384","article-title":"GLoU-MiT: Lightweight global-local Mamba-guided U-mix Transformer for UAV-based pavement crack segmentation","volume":"65","author":"Shan","year":"2025","journal-title":"Adv. Eng. Inf."},{"issue":"9","key":"10.1016\/j.aei.2026.104915_b0055","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1177\/03611981211002203","article-title":"Efficient road crack detection based on an adaptive pixel-level segmentation algorithm","volume":"2675","author":"Safaei","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"10.1016\/j.aei.2026.104915_b0060","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.autcon.2018.07.008","article-title":"Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network","volume":"94","author":"Hoang","year":"2018","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2023.133788","article-title":"Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm","volume":"408","author":"Yuan","year":"2023","journal-title":"Constr. Build. Mater."},{"issue":"9","key":"10.1016\/j.aei.2026.104915_b0070","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1080\/10298436.2021.1888092","article-title":"Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM","volume":"23","author":"Chen","year":"2022","journal-title":"Int. J. Pavement Eng."},{"issue":"5","key":"10.1016\/j.aei.2026.104915_b0075","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/0885-9507.00113","article-title":"Effect of neural network topology on flexible pavement cracking prediction","volume":"13","author":"Owusu-Ababio","year":"2010","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"10.1016\/j.aei.2026.104915_b0080","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"},{"key":"10.1016\/j.aei.2026.104915_b0085","doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Boston, MA, USA, 2015: pp. 3431-3440.","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"10","key":"10.1016\/j.aei.2026.104915_b0090","doi-asserted-by":"crossref","first-page":"18394","DOI":"10.1109\/TITS.2022.3158670","article-title":"DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation","volume":"23","author":"Sun","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104915_b0095","doi-asserted-by":"crossref","unstructured":"L. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (Eds.), Proceedings of the European Conference on Computer Vision (ECCV), Springer International Publishing, Munich, Germany, 2018: pp. 801-818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.1016\/j.aei.2026.104915_b0100","doi-asserted-by":"crossref","unstructured":"H. Liu, X. Miao, C. Mertz, C. Xu, H. Kong, CrackFormer: Transformer network for fine-grained crack detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, Canada, 2021: pp. 3783-3792.","DOI":"10.1109\/ICCV48922.2021.00376"},{"issue":"12","key":"10.1016\/j.aei.2026.104915_b0105","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.aei.2026.104915_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105482","article-title":"CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation","volume":"164","author":"Liang","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110697","article-title":"Efficient crack segmentation with multi-decoder networks and enhanced feature fusion","volume":"152","author":"Okran","year":"2025","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104915_b0120","doi-asserted-by":"crossref","unstructured":"Z. Liu, H. Mao, C. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A ConvNet for the 2020s, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2022: pp. 11976-11986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.aei.2026.104915_b0125","doi-asserted-by":"crossref","unstructured":"A. Chaurasia, E. Culurciello, LinkNet: Exploiting encoder representations for efficient semantic segmentation, in: Proceedings of the IEEE Visual Communications and Image Processing (VCIP), IEEE, 2017: pp. 1-4.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"10.1016\/j.aei.2026.104915_b0130","series-title":"Medical image computing and computer-assisted intervention, MICCAI 2015","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.aei.2026.104915_b0135","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.autcon.2019.04.005","article-title":"Computer vision-based concrete crack detection using U-net fully convolutional networks","volume":"104","author":"Liu","year":"2019","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2021.126265","article-title":"UNet-based model for crack detection integrating visual explanations","volume":"322","author":"Liu","year":"2022","journal-title":"Constr. Build. Mater."},{"issue":"24","key":"10.1016\/j.aei.2026.104915_b0145","doi-asserted-by":"crossref","first-page":"12949","DOI":"10.1007\/s10489-024-05788-1","article-title":"EU-Net: a segmentation network based on semantic fusion and edge guidance for road crack images","volume":"54","author":"Gao","year":"2024","journal-title":"Appl. Intell."},{"issue":"9","key":"10.1016\/j.aei.2026.104915_b0150","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1109\/TNNLS.2021.3062070","article-title":"A deeply supervised convolutional neural network for pavement crack detection with multiscale feature fusion","volume":"33","author":"Qu","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"10","key":"10.1016\/j.aei.2026.104915_b0155","doi-asserted-by":"crossref","first-page":"13833","DOI":"10.1109\/TITS.2024.3405995","article-title":"OUR-Net: A multi-frequency network with octave max unpooling and octave convolutional residual block for pavement crack segmentation","volume":"25","author":"Li","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104915_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102787","article-title":"A pyramid auxiliary supervised U-Net model for road crack detection with dual-attention mechanism","volume":"84","author":"Lu","year":"2024","journal-title":"Displays"},{"key":"10.1016\/j.aei.2026.104915_b0165","doi-asserted-by":"crossref","first-page":"4076","DOI":"10.1111\/mice.13527","article-title":"Pavement crack detection and segmentation using nested U-Net with residual attention mechanism","volume":"40","author":"Zhao","year":"2025","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"10.1016\/j.aei.2026.104915_b0170","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2018)","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.aei.2026.104915_b0175","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"12","key":"10.1016\/j.aei.2026.104915_b0180","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/mice.13103","article-title":"A lightweight encoder-decoder network for automatic pavement crack detection","volume":"39","author":"Zhu","year":"2024","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"10.1016\/j.aei.2026.104915_b0185","series-title":"Transformers for image recognition at scale, in: International Conference on Learning Representations (ICLR)","article-title":"An image is worth 16\u00d716 words","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.aei.2026.104915_b0190","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE","first-page":"3783","article-title":"Crackformer: transformer network for fine-grained crack detection","author":"Liu","year":"2021"},{"issue":"9","key":"10.1016\/j.aei.2026.104915_b0195","doi-asserted-by":"crossref","first-page":"9240","DOI":"10.1109\/TITS.2023.3266776","article-title":"Crackformer network for pavement crack segmentation","volume":"24","author":"Liu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104915_b0200","series-title":"2023 IEEE International Conference on Image Processing (ICIP), IEEE","first-page":"86","article-title":"A convolutional-transformer network for crack segmentation with boundary awareness","author":"Tao","year":"2023"},{"key":"10.1016\/j.aei.2026.104915_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.104894","article-title":"A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios","volume":"152","author":"Xiang","year":"2023","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105217","article-title":"Dual-path network combining CNN and transformer for pavement crack segmentation","volume":"158","author":"Wang","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0215","first-page":"1","article-title":"Hybrid swin transformer-CNN model for pore\u2013crack structure identification","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.aei.2026.104915_b0220","series-title":"in: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"10012","article-title":"Swin transformer: hierarchical vision Transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.aei.2026.104915_b0225","series-title":"in: 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","first-page":"29406","article-title":"SCSegamba: Lightweight structure-aware vision mamba for crack segmentation in structures","author":"Liu","year":"2025"},{"key":"10.1016\/j.aei.2026.104915_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105770","article-title":"Enhancing pixel-level crack segmentation with visual mamba and convolutional networks","volume":"168","author":"Han","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111723","article-title":"Crack segmentation network via difference convolution-based encoder and hybrid CNN-Mamba multi-scale attention","volume":"167","author":"Zhang","year":"2025","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.aei.2026.104915_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110536","article-title":"Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and Mamba","volume":"150","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104915_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2025.143657","article-title":"REM-Net: An edge-enhanced network integrating Transformer and State Space Models for precise pavement crack segmentation","volume":"496","author":"Xiong","year":"2025","journal-title":"Constr. Build. Mater."},{"issue":"2","key":"10.1016\/j.aei.2026.104915_b0250","doi-asserted-by":"crossref","first-page":"152","DOI":"10.3390\/coatings10020152","article-title":"Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement","volume":"10","author":"Fan","year":"2020","journal-title":"Coatings"},{"key":"10.1016\/j.aei.2026.104915_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2020.119397","article-title":"A cost effective solution for pavement crack inspection using cameras and deep neural networks","volume":"256","author":"Mei","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.aei.2026.104915_b0260","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.aei.2026.104915_b0265","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2023.3325447","article-title":"MCL-CrackNet: a concrete crack segmentation network using multilevel contrastive learning","volume":"72","author":"Shi","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104915_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106570","article-title":"Image super-resolution reconstruction based on conditional diffusion model in crack detection and segmentation of shield tunnels","volume":"181","author":"Xia","year":"2026","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106142","article-title":"A hybrid deep learning pavement crack semantic segmentation","volume":"122","author":"Al-Huda","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104915_b0280","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"13733","article-title":"RepVGG: Making VGG-style ConvNets great again","author":"Ding","year":"2021"},{"key":"10.1016\/j.aei.2026.104915_b0285","first-page":"4898","article-title":"Understanding the effective receptive field in deep convolutional neural networks","author":"Luo","year":"2017","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"10.1016\/j.aei.2026.104915_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2025.141150","article-title":"Automatic detection, localization and quantification of structural cracks combining computer vision and crowd sensing technologies","volume":"476","author":"Jin","year":"2025","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.aei.2026.104915_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2024.104347","article-title":"RTCNet: a novel real-time triple branch network for pavement crack semantic segmentation","volume":"136","author":"Liu","year":"2025","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"10.1016\/j.aei.2026.104915_b0300","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","first-page":"4413","article-title":"The Lov\u00e1sz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks","author":"Berman","year":"2018"},{"key":"10.1016\/j.aei.2026.104915_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2019.103018","article-title":"Densely connected deep neural network considering connectivity of pixels for automatic crack detection","volume":"110","author":"Mei","year":"2020","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104915_b0310","doi-asserted-by":"crossref","unstructured":"S. Kulkarni, S. Singh, D. Balakrishnan, S. Sharma, S. Devunuri, S. Korlapati, CrackSeg9k: A collection and benchmark for crack segmentation datasets and frameworks, in: European Conference on Computer Vision (ECCV), Springer Nature Switzerland, Cham, 2022: pp. 179-195.","DOI":"10.1007\/978-3-031-25082-8_12"},{"key":"10.1016\/j.aei.2026.104915_b0315","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE","first-page":"4548","article-title":"Structure-measure: A new way to evaluate foreground maps","author":"Fan","year":"2017"},{"key":"10.1016\/j.aei.2026.104915_b0320","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"issue":"11","key":"10.1016\/j.aei.2026.104915_b0325","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.aei.2026.104915_b0330","doi-asserted-by":"crossref","unstructured":"B. Dinh, T. Nguyen, T. Tran, V. Pham, 1M parameters are enough? A lightweight CNN-based model for medical image segmentation, in: 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2023: pp. 1279-1284.","DOI":"10.1109\/APSIPAASC58517.2023.10317244"},{"issue":"9","key":"10.1016\/j.aei.2026.104915_b0335","doi-asserted-by":"crossref","first-page":"8016","DOI":"10.1109\/TIE.2019.2945265","article-title":"SDDNet: real-time crack segmentation","volume":"67","author":"Choi","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"12","key":"10.1016\/j.aei.2026.104915_b0340","doi-asserted-by":"crossref","first-page":"15105","DOI":"10.1109\/TITS.2023.3300312","article-title":"ECSNet: an accelerated real-time image segmentation CNN architecture for pavement crack detection","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104915_b0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114159","article-title":"An attention-based progressive fusion network for pixelwise pavement crack detection","volume":"226","author":"Ma","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104915_b0350","article-title":"Context-cracknet: A context-aware framework for precise segmentation of tiny cracks in pavement images","volume":"484","author":"Kyem","year":"2025","journal-title":"Constr. Build. Mater."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006075?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006075?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T19:55:15Z","timestamp":1784231715000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626006075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":70,"alternative-id":["S1474034626006075"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104915","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CoCrack: A completeness-oriented framework toward effective and efficient pavement crack detection","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104915","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104915"}}