{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:35:30Z","timestamp":1769740530957,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819789627","type":"print"},{"value":"9789819789634","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"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\/978-981-97-8963-4_27","type":"book-chapter","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T22:59:51Z","timestamp":1735081191000},"page":"282-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of\u00a0DeepLab-MDA Semantic Segmentation Network in\u00a0Electric Power Scenarios"],"prefix":"10.1007","author":[{"given":"Baigen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuying","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanfang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,25]]},"reference":[{"issue":"11","key":"27_CR1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4065613","volume":"16","author":"E Pearson","year":"2024","unstructured":"Pearson, E., et al.: Robust autonomous mobile manipulation for substation inspection. ASME. J. Mech. Robot. 16(11), 115001 (2024)","journal-title":"ASME. J. Mech. Robot."},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Wang, X., Guan, C., Lin, S., Cao, H.: Application of intelligent robot inspection system in power transmission project. In: 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Xi\u2019an, China, pp. 49\u201352 (2022). https:\/\/doi.org\/10.1109\/ICBAIE56435.2022.9985821","DOI":"10.1109\/ICBAIE56435.2022.9985821"},{"key":"27_CR3","first-page":"123","volume":"39","author":"EA Zanaty","year":"2017","unstructured":"Zanaty, E.A., El-Zoghdy, S.F.: A novel approach for color image segmentation based on region growing. Int. J. Comput. Appl. 39, 123\u2013139 (2017)","journal-title":"Int. J. Comput. Appl."},{"key":"27_CR4","doi-asserted-by":"publisher","unstructured":"Gupta, S., Singh, H., Singh, Y.J.: Comprehensive study on edge detection. In: Singh, S.N., Mahanta, S., Singh, Y.J. (eds.) Proceedings of the NIELIT\u2019s International Conference on Communication, Electronics and Digital Technology. NICE-DT 2023. LNNS, vol. 676, pp. 49-52. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-1699-3_30","DOI":"10.1007\/978-981-99-1699-3_30"},{"key":"27_CR5","unstructured":"Roberts, L.G.: Machine perception of three-dimensional solids. Massachusetts Institute of Technology (1963)"},{"issue":"1","key":"27_CR6","first-page":"15","volume":"10","author":"JM Prewitt","year":"1970","unstructured":"Prewitt, J.M., et al.: Object enhancement and extraction. Picture Process. Psychopictorics 10(1), 15\u201319 (1970)","journal-title":"Picture Process. Psychopictorics"},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"13635","DOI":"10.1038\/s41598-022-17818-4","volume":"12","author":"F Xiong","year":"2022","unstructured":"Xiong, F., Zhang, Z., Ling, Y., et al.: Image thresholding segmentation based on weighted Parzen-window and linear programming techniques. Sci. Rep. 12, 13635 (2022)","journal-title":"Sci. Rep."},{"key":"27_CR8","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"},{"issue":"12","key":"27_CR9","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":"27_CR10","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":"2","key":"27_CR11","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3390\/e21020168","volume":"21","author":"C Wang","year":"2019","unstructured":"Wang, C., Zhao, Z., Ren, Q., et al.: Dense u-net based on patch-based learning for retinal vessel segmentation. Entropy 21(2), 168 (2019)","journal-title":"Entropy"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Alom, M. Z., Yakopcic, C., Taha, T. M., et al.: Nuclei segmentation with recurrent residual convolutional neural networks based u-net (r2u-net). In: NAECON 2018-IEEE National Aerospace and Electronics Conference, pp. 228\u2013233. IEEE (2018)","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"5","key":"27_CR13","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1109\/JBHI.2020.3024188","volume":"25","author":"E Thomas","year":"2020","unstructured":"Thomas, E., Pawan, S., Kumar, S., et al.: Multi-res-attention unet: a CNN model for the segmentation of focal cortical dysplasia lesions from magnetic resonance images. IEEE J. Biomed. Health Inform. 25(5), 1724\u20131734 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"27_CR14","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR15","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. CoRR abs\/1706.05587 (2017)"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Chen, L. C., Zhu, Y., Papandreou, G., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"27_CR17","unstructured":"Dosovitskiy, A., et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR abs\/2010.11929 (2020)"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"27_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"27_CR21","unstructured":"Yuan, Y., Wang, J.: OCNet: Object Context Network for Scene Parsing. CoRR abs\/1809.00916 (2018)"}],"container-title":["Lecture Notes in Computer Science","Social Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8963-4_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T23:03:45Z","timestamp":1735081425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8963-4_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,25]]},"ISBN":["9789819789627","9789819789634"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8963-4_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,25]]},"assertion":[{"value":"25 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSR + BioMed","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Social Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"17 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socrob2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/robicon2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}