{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:33:49Z","timestamp":1743057229699,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819786916"},{"type":"electronic","value":"9789819786923"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-981-97-8692-3_31","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"437-451","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DBMF-Net: A Dual-Branch Multimodal Fusion Network for\u00a0Multi-label Sewer Defect Classification"],"prefix":"10.1007","author":[{"given":"Ziyang","family":"Chen","sequence":"first","affiliation":[]},{"given":"Lin","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Chen, K., Hu, H., Chen, C., Chen, L., He, C.: An intelligent sewer defect detection method based on convolutional neural network. In: 2018 IEEE International Conference on Information and Automation (ICIA), pp. 1301\u20131306. IEEE (2018)","DOI":"10.1109\/ICInfA.2018.8812445"},{"issue":"9","key":"31_CR2","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1139\/cjce-2016-0592","volume":"44","author":"M Elmasry","year":"2017","unstructured":"Elmasry, M., Hawari, A., Zayed, T.: Defect based deterioration model for sewer pipelines using Bayesian belief networks. Can. J. Civ. Eng. 44(9), 675\u2013690 (2017)","journal-title":"Can. J. Civ. Eng."},{"issue":"2","key":"31_CR3","doi-asserted-by":"publisher","first-page":"04013014","DOI":"10.1061\/(ASCE)IS.1943-555X.0000161","volume":"20","author":"MR Halfawy","year":"2014","unstructured":"Halfawy, M.R., Hengmeechai, J.: Efficient algorithm for crack detection in sewer images from closed-circuit television inspections. J. Infrastruct. Syst. 20(2), 04013014 (2014)","journal-title":"J. Infrastruct. Syst."},{"issue":"2","key":"31_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/335191.335372","volume":"29","author":"J Han","year":"2000","unstructured":"Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29(2), 1\u201312 (2000)","journal-title":"ACM SIGMOD Rec."},{"key":"31_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.102849","volume":"106","author":"SI Hassan","year":"2019","unstructured":"Hassan, S.I., Dang, L.M., Mehmood, I., Im, S., Choi, C., Kang, J., Park, Y.S., Moon, H.: Underground sewer pipe condition assessment based on convolutional neural networks. Autom. Constr. 106, 102849 (2019)","journal-title":"Autom. Constr."},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Haurum, J.B., Moeslund, T.B.: Sewer-ml: A multi-label sewer defect classification dataset and benchmark. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13456\u201313467 (2021)","DOI":"10.1109\/CVPR46437.2021.01325"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR8","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1007\/s12205-019-0980-7","volume":"23","author":"G Heo","year":"2019","unstructured":"Heo, G., Jeon, J., Son, B.: Crack automatic detection of CCTV video of sewer inspection with low resolution. KSCE J. Civ. Eng. 23, 1219\u20131227 (2019)","journal-title":"KSCE J. Civ. Eng."},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"31_CR10","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5 mb model size (2016). arXiv:1602.07360"},{"issue":"6","key":"31_CR11","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1111\/j.1467-8667.2006.00445.x","volume":"21","author":"S Iyer","year":"2006","unstructured":"Iyer, S., Sinha, S.K.: Segmentation of pipe images for crack detection in buried sewers. Comput.-Aided Civ. Infrastruct. Eng. 21(6), 395\u2013410 (2006)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"issue":"11","key":"31_CR12","doi-asserted-by":"publisher","DOI":"10.1289\/EHP2048","volume":"125","author":"JS Jagai","year":"2017","unstructured":"Jagai, J.S., DeFlorio-Barker, S., Lin, C.J., Hilborn, E.D., Wade, T.J.: Sanitary sewer overflows and emergency room visits for gastrointestinal illness: analysis of massachusetts data, 2006\u20132007. Environ. Health Perspect. 125(11), 117007 (2017)","journal-title":"Environ. Health Perspect."},{"issue":"6","key":"31_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"issue":"2","key":"31_CR14","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s13349-022-00660-7","volume":"13","author":"M Li","year":"2023","unstructured":"Li, M., Li, M., Ren, Q., Liu, H., Liu, C.: Intelligent identification and classification of sewer pipeline network defects based on improved regnety network. J. Civ. Struct. Heal. Monit. 13(2), 547\u2013560 (2023)","journal-title":"J. Civ. Struct. Heal. Monit."},{"key":"31_CR15","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.proeng.2016.07.416","volume":"154","author":"J Myrans","year":"2016","unstructured":"Myrans, J., Kapelan, Z., Everson, R.: Automated detection of faults in wastewater pipes from CCTV footage by using random forests. Procedia Eng. 154, 36\u201341 (2016)","journal-title":"Procedia Eng."},{"issue":"9","key":"31_CR16","doi-asserted-by":"publisher","first-page":"2184","DOI":"10.2166\/wst.2018.131","volume":"77","author":"J Myrans","year":"2018","unstructured":"Myrans, J., Kapelan, Z., Everson, R.: Combining classifiers to detect faults in wastewater networks. Water Sci. Technol. 77(9), 2184\u20132189 (2018)","journal-title":"Water Sci. Technol."},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., Huang, Z.: Efficient multi-scale attention module with cross-spatial learning. In: ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Ridnik, T., Lawen, H., Noy, A., Ben\u00a0Baruch, E., Sharir, G., Friedman, I.: Tresnet: high performance GPU-dedicated architecture. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1400\u20131409 (2021)","DOI":"10.1109\/WACV48630.2021.00144"},{"issue":"5","key":"31_CR19","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.3390\/app11052263","volume":"11","author":"BJ Son","year":"2021","unstructured":"Son, B.J., Cho, T.: Modified crack detection of sewer conduit with low-resolution images. Appl. Sci. 11(5), 2263 (2021)","journal-title":"Appl. Sci."},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Tao, M., Wan, L., Wang, H., Su, T.: Cafen: a correlation-aware feature enhancement network for sewer defect identification. In: 2022 21st International Symposium on Communications and Information Technologies (ISCIT), pp. 204\u2013209. IEEE (2022)","DOI":"10.1109\/ISCIT55906.2022.9931233"},{"issue":"20","key":"31_CR22","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10\u201348550 (2017)","journal-title":"Stat"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., Wen, S.: Multi-label classification with label graph superimposing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 12265\u201312272 (2020)","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"31_CR24","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11211. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"4","key":"31_CR25","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1109\/TASE.2019.2900170","volume":"16","author":"Q Xie","year":"2019","unstructured":"Xie, Q., Li, D., Xu, J., Yu, Z., Wang, J.: Automatic detection and classification of sewer defects via hierarchical deep learning. IEEE Trans. Autom. Sci. Eng. 16(4), 1836\u20131847 (2019)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"31_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11783-019-1102-y","volume":"13","author":"X Ye","year":"2019","unstructured":"Ye, X., Zuo, J., Li, R., Wang, Y., Gan, L., Yu, Z., Hu, X.: Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city. Front. Environ. Sci. Eng. 13, 1\u201313 (2019)","journal-title":"Front. Environ. Sci. Eng."},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, J., Liu, W., Ge, J., Liu, B., Cao, J.: Scene-aware label graph learning for multi-label image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1473\u20131482 (2023)","DOI":"10.1109\/ICCV51070.2023.00142"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8692-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:27:50Z","timestamp":1730384870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8692-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819786916","9789819786923"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8692-3_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}