{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:28:11Z","timestamp":1793377691881,"version":"4.5.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434112","type":"print"},{"value":"9783031434129","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\/978-3-031-43412-9_23","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T20:28:38Z","timestamp":1694896118000},"page":"389-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Dynamic Thresholding for Accurate Crack Segmentation Using Multi-objective Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1340-5969","authenticated-orcid":false,"given":"Qin","family":"Lei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9780-0984","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangmei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"issue":"9","key":"23_CR1","doi-asserted-by":"publisher","first-page":"2557","DOI":"10.3390\/s20092557","volume":"20","author":"R Augustauskas","year":"2020","unstructured":"Augustauskas, R., Lipnickas, A.: Improved pixel-level pavement-defect segmentation using a deep autoencoder. Sensors 20(9), 2557 (2020)","journal-title":"Sensors"},{"issue":"12","key":"23_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, J., Xiong, W., Chen, W., Gu, Y., Li, Y.: Pixel-level crack detection using U-Net. In: TENCON 2018\u20132018 IEEE Region 10 Conference, pp. 0462\u20130466. IEEE (2018)","DOI":"10.1109\/TENCON.2018.8650059"},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","volume":"99","author":"CV Dung","year":"2019","unstructured":"Dung, C.V., et al.: Autonomous concrete crack detection using deep fully convolutional neural network. Autom. Constr. 99, 52\u201358 (2019)","journal-title":"Autom. Constr."},{"key":"23_CR6","unstructured":"Fan, Z., Wu, Y., Lu, J., Li, W.: Automatic pavement crack detection based on structured prediction with the convolutional neural network. arXiv preprint arXiv:1802.02208 (2018)"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"issue":"2","key":"23_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1080\/15732479.2020.1838559","volume":"18","author":"H Huang","year":"2022","unstructured":"Huang, H., Zhao, S., Zhang, D., Chen, J.: Deep learning-based instance segmentation of cracks from shield tunnel lining images. Struct. Infrastruct. Eng. 18(2), 183\u2013196 (2022)","journal-title":"Struct. Infrastruct. Eng."},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Inoue, Y., Nagayoshi, H.: Deployment conscious automatic surface crack detection. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 686\u2013694. IEEE (2019)","DOI":"10.1109\/WACV.2019.00078"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Inoue, Y., Nagayoshi, H.: Crack detection as a weakly-supervised problem: towards achieving less annotation-intensive crack detectors. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 65\u201372. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412041"},{"key":"23_CR11","unstructured":"Jenkins, M.D., Carr, T.A., Iglesias, M.I., Buggy, T., Morison, G.: 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":"23_CR12","doi-asserted-by":"publisher","first-page":"103291","DOI":"10.1016\/j.autcon.2020.103291","volume":"118","author":"D Kang","year":"2020","unstructured":"Kang, D., Benipal, S.S., Gopal, D.L., Cha, Y.J.: Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Autom. Constr. 118, 103291 (2020)","journal-title":"Autom. Constr."},{"key":"23_CR13","unstructured":"K\u00f6nig, J., Jenkins, M., Mannion, M., Barrie, P., Morison, G.: What\u2019s cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification. arXiv preprint arXiv:2202.03714 (2022)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"K\u00f6nig, J., Jenkins, M.D., Barrie, P., Mannion, M., Morison, G.: 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. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803060"},{"key":"23_CR15","doi-asserted-by":"publisher","first-page":"110727","DOI":"10.1016\/j.measurement.2022.110727","volume":"190","author":"Y Li","year":"2022","unstructured":"Li, Y., Wang, H., Dang, L.M., Piran, M.J., Moon, H.: A robust instance segmentation framework for underground sewer defect detection. Measurement 190, 110727 (2022)","journal-title":"Measurement"},{"key":"23_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"23_CR17","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., Xie, R., Li, L.: DeepCrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338, 139\u2013153 (2019)","journal-title":"Neurocomputing"},{"key":"23_CR18","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":"23_CR19","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"},{"issue":"1","key":"23_CR20","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s42947-021-00006-4","volume":"15","author":"N Safaei","year":"2022","unstructured":"Safaei, N., Smadi, O., Masoud, A., Safaei, B.: An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification. Int. J. Pavement Res. Technol. 15(1), 159\u2013172 (2022)","journal-title":"Int. J. Pavement Res. Technol."},{"issue":"3","key":"23_CR21","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","volume":"31","author":"D Tabernik","year":"2020","unstructured":"Tabernik, D., \u0160ela, S., Skvar\u010d, J., Sko\u010daj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759\u2013776 (2020)","journal-title":"J. Intell. Manuf."},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Z., Lu, T., Zhang, Y., Wang, B., Zhao, X.: Crack detecting by recursive attention U-Net. In: 2020 3rd International Conference on Robotics, Control and Automation Engineering (RCAE), pp. 103\u2013107. IEEE (2020)","DOI":"10.1109\/RCAE51546.2020.9294343"},{"issue":"4","key":"23_CR23","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., Prokhorov, D., Mei, X., Ling, H.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525\u20131535 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"103934","DOI":"10.1016\/j.autcon.2021.103934","volume":"132","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Zhang, D., Xue, Y., Zhou, M., Huang, H.: A deep learning-based approach for refined crack evaluation from shield tunnel lining images. Autom. Constr. 132, 103934 (2021)","journal-title":"Autom. Constr."},{"key":"23_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS-2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"issue":"3","key":"23_CR26","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TIP.2018.2878966","volume":"28","author":"Q Zou","year":"2018","unstructured":"Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S.: DeepCrack: learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 28(3), 1498\u20131512 (2018)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43412-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T20:32:56Z","timestamp":1694896376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43412-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434112","9783031434129"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43412-9_23","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":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This study followed the ethical guidelines of the National Natural Science Foundation of China and the ethical review standards of the Chongqing Artificial Intelligence Innovation Center. This study did not involve any human or animal participants, nor did it use any sensitive or private data. The purpose of this study was to promote scientific advancement and social welfare in the field of artificial intelligence, and it did not cause any harm or adverse effects to any individual or group. All authors of this study are responsible for the content of this paper, and declare that they have no conflicts of interest or academic misconduct.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}