{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:20:11Z","timestamp":1742962811187,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":8,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819629107"},{"type":"electronic","value":"9789819629114"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2911-4_1","type":"book-chapter","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:44:04Z","timestamp":1741599844000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmentation of Crack Disaster Images Based on Deep Learning Neural Network Method"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8074-3431","authenticated-orcid":false,"given":"Gengkun","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0640-301X","authenticated-orcid":false,"given":"Letian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tossou","family":"Akpedje","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. F.","family":"Ingrid Hermilda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengwei","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"issue":"12","key":"1_CR1","doi-asserted-by":"publisher","first-page":"25212","DOI":"10.1109\/TITS.2022.3221169","volume":"23","author":"H Lu","year":"2022","unstructured":"Lu, H., Guizani, M., Ho, P.H.: Editorial introduction to responsible artificial intelligence for autonomous driving. IEEE Trans. Intell. Transp. Syst. 23(12), 25212\u201325215 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"1_CR2","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1109\/TITS.2020.2991766","volume":"22","author":"H Lu","year":"2020","unstructured":"Lu, H., Zhang, Y., Li, Y., Jiang, C., Abbas, H.: User-oriented virtual mobile network resource management for vehicle communications. IEEE Trans. Intell. Transp. Syst. 22(6), 3521\u20133532 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.future.2018.01.001","volume":"82","author":"H Lu","year":"2018","unstructured":"Lu, H., Li, Y., Uemura, T., Kim, H., Serikawa, S.: Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur. Gener. Comput. Syst. 82, 142\u2013148 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/TFUZZ.2020.2984991","volume":"29","author":"H Lu","year":"2020","unstructured":"Lu, H., Zhang, M., Xu, X., Li, Y., Shen, H.T.: Deep fuzzy hashing network for efficient image retrieval. IEEE Trans. Fuzzy Syst. 29(1), 166\u2013176 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.104853","volume":"150","author":"L Yang","year":"2023","unstructured":"Yang, L., Bai, S., Liu, Y., Yu, H.: Multi-scale triple-attention network for pixelwise crack segmentation. Autom. Constr. 150, 104853 (2023)","journal-title":"Autom. Constr."},{"issue":"3","key":"1_CR6","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.3390\/app12031374","volume":"12","author":"Y Hamishebahar","year":"2022","unstructured":"Hamishebahar, Y., Guan, H., So, S., Jo, J.: A comprehensive review of deep learning-based crack detection approaches. Appl. Sci. 12(3), 1374 (2022)","journal-title":"Appl. Sci."},{"issue":"4","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1007\/s12205-022-0518-2","volume":"26","author":"G Chen","year":"2022","unstructured":"Chen, G., Teng, S., Lin, M., Yang, X., Sun, X.: Crack detection based on generative adversarial networks and deep learning. KSCE J. Civ. Eng. 26(4), 1803\u20131816 (2022)","journal-title":"KSCE J. Civ. Eng."},{"key":"1_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2021.104969","volume":"158","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Wang, J., Yang, K., Wang, L., Su, F., Chen, X.: Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+. Comput. Geosci.Geosci. 158, 104969 (2022)","journal-title":"Comput. Geosci.Geosci."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2911-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:46:41Z","timestamp":1741600001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2911-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819629107","9789819629114"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2911-4_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"11 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISAIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Artificial Intelligence and Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isair2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/isair.site\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}