{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:39:12Z","timestamp":1776883152158,"version":"3.51.2"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031479687","type":"print"},{"value":"9783031479694","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47969-4_16","type":"book-chapter","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T20:02:06Z","timestamp":1701374526000},"page":"199-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9339-1546","authenticated-orcid":false,"given":"Iason","family":"Katsamenis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-0024","authenticated-orcid":false,"given":"Eftychios","family":"Protopapadakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3106-4758","authenticated-orcid":false,"given":"Nikolaos","family":"Bakalos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5331-3514","authenticated-orcid":false,"given":"Andreas","family":"Varvarigos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0612-5889","authenticated-orcid":false,"given":"Anastasios","family":"Doulamis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4064-8990","authenticated-orcid":false,"given":"Nikolaos","family":"Doulamis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0632-9769","authenticated-orcid":false,"given":"Athanasios","family":"Voulodimos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, T.U., et al.: An integrated CNN-RNN framework to assess road crack. In: 2019 22nd International Conference on Computer and Information Technology (ICCIT), pp. 1\u20136 (2019)","DOI":"10.1109\/ICCIT48885.2019.9038607"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., et al.: Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv preprint:1802.06955 (2018)","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-030-33720-9_46","volume-title":"Advances in Visual Computing","author":"UH Billah","year":"2019","unstructured":"Billah, U.H., Tavakkoli, A., La, H.M.: Concrete crack pixel classification using an encoder decoder based deep learning architecture. In: Bebis, G., et al. (eds.) ISVC 2019. LNCS, vol. 11844, pp. 593\u2013604. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33720-9_46"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F.I., et al.: ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote. Sens. 162, 94\u2013114 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"16_CR6","unstructured":"Jenkins, M.D., et al.: A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. In: 2018 26th European Signal Processing Conference (EUSIPCO), pp. 2120\u20132124. IEEE (2018)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Katsamenis, I., et al.: Robotic maintenance of road infrastructures: the heron project. In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 628\u2013635. PETRA 2022, Association for Computing Machinery, New York, NY, USA (2022)","DOI":"10.1145\/3529190.3534746"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"104182","DOI":"10.1016\/j.autcon.2022.104182","volume":"137","author":"I Katsamenis","year":"2022","unstructured":"Katsamenis, I., et al.: Simultaneous precise localization and classification of metal rust defects for robotic-driven maintenance and prefabrication using residual attention u-net. Autom. Constr. 137, 104182 (2022)","journal-title":"Autom. Constr."},{"key":"16_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-030-64556-4_13","volume-title":"Advances in Visual Computing","author":"I Katsamenis","year":"2020","unstructured":"Katsamenis, I., Protopapadakis, E., Doulamis, A., Doulamis, N., Voulodimos, A.: Pixel-level corrosion detection on metal constructions by fusion of deep learning semantic and contour segmentation. In: Bebis, G., et al. (eds.) ISVC 2020. LNCS, vol. 12509, pp. 160\u2013169. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64556-4_13"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Katsamenis, I., et al.: Deep transformer networks for precise pothole segmentation tasks. In: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 596\u2013602 (2023)","DOI":"10.1145\/3594806.3596560"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"K\u00f6nig, J., et al.: A convolutional neural network for pavement surface crack segmentation using residual connections and attention gating. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1460\u20131464 (2019)","DOI":"10.1109\/ICIP.2019.8803060"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"114892","DOI":"10.1109\/ACCESS.2020.3003638","volume":"8","author":"SL Lau","year":"2020","unstructured":"Lau, S.L., et al.: Automated pavement crack segmentation using U-Net-based convolutional neural network. IEEE Access 8, 114892\u2013114899 (2020)","journal-title":"IEEE Access"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3367\u20133375 (2015)","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Ogawa, N., et al.: Distress level classification of road infrastructures via CNN generating attention map. In: 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 97\u201398 (2020)","DOI":"10.1109\/LifeTech48969.2020.1570619126"},{"key":"16_CR17","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint:1804.03999 (2018)"},{"key":"16_CR18","unstructured":"Oliveira, H., Correia, P.L.: Automatic road crack segmentation using entropy and image dynamic thresholding. In: 2009 17th European Signal Processing Conference, pp. 622\u2013626 (2009)"},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"107725","DOI":"10.1016\/j.compeleceng.2022.107725","volume":"99","author":"AK Pandey","year":"2022","unstructured":"Pandey, A.K., et al.: Convolution neural networks for pothole detection of critical road infrastructure. Comp. Electr. Eng. 99, 107725 (2022)","journal-title":"Comp. Electr. Eng."},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Protopapadakis, E., Katsamenis, I., Doulamis, A.: Multi-label deep learning models for continuous monitoring of road infrastructures. In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1\u20137 (2020)","DOI":"10.1145\/3389189.3397997"},{"key":"16_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"16_CR22","first-page":"17","volume":"98","author":"N Tanaka","year":"1998","unstructured":"Tanaka, N., Uematsu, K.: A crack detection method in road surface images using morphology. MVA 98, 17\u201319 (1998)","journal-title":"MVA"},{"issue":"1","key":"16_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3141\/2523-07","volume":"2523","author":"C Torres-Mach\u00ed","year":"2015","unstructured":"Torres-Mach\u00ed, C., et al.: Sustainable pavement management: integrating economic, technical, and environmental aspects in decision making. Transp. Res. Rec. 2523(1), 56\u201363 (2015)","journal-title":"Transp. Res. Rec."},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., et al.: A few-shot U-Net deep learning model for COVID-19 infected area segmentation in CT images. Sensors 21(6) (2021)","DOI":"10.3390\/s21062215"},{"key":"16_CR25","doi-asserted-by":"publisher","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York (1992). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_16","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"16_CR26","unstructured":"Zaloshnja, E., Miller, T.R.: Cost of crashes related to road conditions, united states, 2006. In: Annals of Advances in Automotive Medicine\/Annual Scientific Conference, vol. 53, p. 141. Association for the Advancement of Automotive Medicine (2009)"},{"issue":"5","key":"16_CR27","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. IEEE Geosci. Remote Sens. Lett. 15(5), 749\u2013753 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qin, G., Wang, X.: Improvement of canny algorithm based on pavement edge detection. In: 2010 3rd International Congress on Image and Signal Processing, vol. 2, pp. 964\u2013967 (2010)","DOI":"10.1109\/CISP.2010.5646923"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47969-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T14:59:27Z","timestamp":1730732367000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47969-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031479687","9783031479694"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47969-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lake Tahoe, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"232% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43 (oral), 15 (poster), 25 (special tracks) out of 34 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}