{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:25:35Z","timestamp":1783542335001,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819224869","type":"print"},{"value":"9789819224876","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2487-6_9","type":"book-chapter","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:40:16Z","timestamp":1783539616000},"page":"128-149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EAU-Net: Efficient Asymmetric U-Net for\u00a0Butterfly Ecological Image Segmentation"],"prefix":"10.1007","author":[{"given":"Jie","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinyuan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanying","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengquan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"issue":"1454","key":"9_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1098\/rstb.2004.1589","volume":"360","author":"ST Buckland","year":"2005","unstructured":"Buckland, S.T., Magurran, A.E., Green, R.E., Fewster, R.M.: Monitoring change in biodiversity through composite indices. Philos. Trans. Royal Soc. Biol. Sci. 360(1454), 243\u2013254 (2005)","journal-title":"Philos. Trans. Royal Soc. Biol. Sci."},{"issue":"4","key":"9_CR2","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., Murphy, K., Yuille, A.L.: 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":"9_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2777\u20132787 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Filali, I., et al.: Graph ranking based butterfly segmentation in ecological images. Eco. Inform. 68, 101553 (2022)","DOI":"10.1016\/j.ecoinf.2022.101553"},{"key":"9_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.103968","volume":"135","author":"I Filali","year":"2023","unstructured":"Filali, I., Achour, B., Ramdani, M.: Segmentation of butterflies from complex AGRO-ecological images using quantum mechanics and spatial refinement. Digit. Signal Process. 135, 103968 (2023)","journal-title":"Digit. Signal Process."},{"key":"9_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2024.109420","volume":"219","author":"I Filali","year":"2024","unstructured":"Filali, I., Ramdani, M., Achour, B.: Butterfly segmentation by multi scaled quantum cuts in AGRO-ecological environment. Signal Process. 219, 109420 (2024)","journal-title":"Signal Process."},{"key":"9_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102539","volume":"80","author":"Y Gao","year":"2024","unstructured":"Gao, Y., et al.: Application of machine learning in automatic image identification of insects: a review. Eco. Inform. 80, 102539 (2024)","journal-title":"Eco. Inform."},{"key":"9_CR9","unstructured":"Gao, Y., Du, J., Xu, S.: 7th china conference on data mining and 3rd china data mining competition: 1st international butterfly identification competition. https:\/\/ccdm2018.sdufe.edu.cn\/sjwjjs.htm. Accessed 30 Apr 2026"},{"key":"9_CR10","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":"9_CR11","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Kargar, A., Zorbas, D., Gaffney, M., O\u2019Flynn, B., Tedesco, S.: Y-Net: insect counting and segmentation using deep learning on embedded devices. In: Proceedings of the 2024 IEEE International Instrumentation and Measurement Technology Conference. IEEE (2024)","DOI":"10.1109\/I2MTC60896.2024.10561007"},{"key":"9_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110378","volume":"236","author":"A Kargar","year":"2025","unstructured":"Kargar, A., et al.: Tiny deep learning model for insect segmentation and counting on resource-constrained devices. Comput. Electron. Agric. 236, 110378 (2025)","journal-title":"Comput. Electron. Agric."},{"key":"9_CR14","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"key":"9_CR15","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":"9_CR16","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision, pp. 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"issue":"11","key":"9_CR17","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.3390\/electronics14112244","volume":"14","author":"Y Ma","year":"2025","unstructured":"Ma, Y., et al.: Lightweight semantic segmentation network with multi-level feature fusion and dual attention collaboration. Electronics 14(11), 2244 (2025)","journal-title":"Electronics"},{"issue":"2","key":"9_CR18","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.knosys.2006.11.012","volume":"20","author":"M Mayo","year":"2007","unstructured":"Mayo, M., Watson, A.T.: Automatic species identification of live moths. Knowl.-Based Syst. 20(2), 195\u2013202 (2007)","journal-title":"Knowl.-Based Syst."},{"issue":"6736","key":"9_CR19","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1038\/21181","volume":"399","author":"C Parmesan","year":"1999","unstructured":"Parmesan, C., et al.: Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399(6736), 579\u2013583 (1999)","journal-title":"Nature"},{"key":"9_CR20","unstructured":"Poudel, R.P.K., Liwicki, S., Cipolla, R.: Fast-SCNN: fast semantic segmentation network. In: Proceedings of the 30th British Machine Vision Conference. p.\u00a0289 (2019)"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI, pp. 234\u2013241. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105739","volume":"178","author":"H Tang","year":"2020","unstructured":"Tang, H., Wang, B., Chen, X.: Deep learning techniques for automatic butterfly segmentation in ecological images. Comput. Electron. Agric. 178, 105739 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.patcog.2017.10.002","volume":"76","author":"XS Wei","year":"2018","unstructured":"Wei, X.S., Xie, C.W., Wu, J., Shen, C.: Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recogn. 76, 704\u2013714 (2018)","journal-title":"Pattern Recogn."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhan, C., Lai, Y.K., Cheng, M.M., Yang, J.: IP102: a large-scale benchmark dataset for insect pest recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8787\u20138796 (2019)","DOI":"10.1109\/CVPR.2019.00899"},{"key":"9_CR25","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems. vol. 34, pp. 12077\u201312090 (2021)"},{"issue":"3","key":"9_CR26","first-page":"265","volume":"4","author":"J Xie","year":"2019","unstructured":"Xie, J., et al.: A dataset of butterfly ecological images for automatic species identification. China Sci. Data 4(3), 265 (2019)","journal-title":"China Sci. Data"},{"issue":"6","key":"9_CR27","first-page":"1","volume":"53","author":"J Xie","year":"2025","unstructured":"Xie, J., Lan, X., Xu, S.: SABM: an enhanced SAM model for segmenting any butterflies in ecological images. J. Shaanxi Norm. Univ. (Natural Sci. Edition) 53(6), 1\u201314 (2025)","journal-title":"J. Shaanxi Norm. Univ. (Natural Sci. Edition)"},{"issue":"5","key":"9_CR28","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.3390\/app10051681","volume":"10","author":"D Xin","year":"2020","unstructured":"Xin, D., Chen, Y.W., Li, J.: Fine-grained butterfly classification in ecological images using squeeze-and-excitation and spatial attention modules. Appl. Sci. 10(5), 1681 (2020)","journal-title":"Appl. Sci."},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., Bhattacharyya, S.P.: PIDNet: a real-time semantic segmentation network inspired by PID controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19529\u201319539 (2023)","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 325\u2013341 (2018)","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"9_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2025.105168","volume":"162","author":"K Zhang","year":"2025","unstructured":"Zhang, K., Chen, X., Wang, B.: MdeBEIA: multi-task deep learning for butterfly ecological image analysis. Digit. Signal Process. 162, 105168 (2025)","journal-title":"Digit. Signal Process."},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, S.C., et al.: Revisiting efficient semantic segmentation: learning offsets for better spatial and class feature alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22361\u201322371 (2025)","DOI":"10.1109\/ICCV51701.2025.02076"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: TopFormer: Token pyramid transformer for mobile semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12083\u201312093 (2022)","DOI":"10.1109\/CVPR52688.2022.01177"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: U-Net++: a nested U-Net architecture for medical image segmentation. In: International Workshop on Deep Learning in Medical Image Analysis, pp. 3\u201311. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Engineering for Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2487-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:40:19Z","timestamp":1783539619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2487-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"ISBN":["9789819224869","9789819224876"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2487-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"9 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FLINS-ISKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Knowledge Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iske2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.flins.cc","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}