{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:18:50Z","timestamp":1777490330846,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Nature Science Foun- dation of China","award":["51804250"],"award-info":[{"award-number":["51804250"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11554-024-01603-9","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T07:59:40Z","timestamp":1737705580000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CFP-PSPNet: a lightweight unmanned vessel water segmentation algorithm"],"prefix":"10.1007","volume":"22","author":[{"given":"Xuecun","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijing","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lintao","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghua","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"1603_CR1","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos, N., Vasanthavada, N.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circuits 23, 358\u2013367 (1988)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"1603_CR2","doi-asserted-by":"crossref","unstructured":"Canny, J.A.: Computational approach to edge detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 679\u2013698 (1986)","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"1603_CR3","first-page":"5","volume":"26","author":"YU Jiajun","year":"2018","unstructured":"Jiajun, Y.U., Wentao, L., Feihong, X., Changyun, W.: River boundary recognition algorithm for intelligent float? Garbage ship. Electron. Des. Eng. 26, 5 (2018)","journal-title":"Electron. Des. Eng."},{"key":"1603_CR4","first-page":"857","volume":"41","author":"M Elias","year":"2016","unstructured":"Elias, M.: Automatic waterline extraction from smartphone images. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 41, 857\u2013863 (2016)","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"1603_CR5","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, S., Xie, J.: IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, pp. 3899\u20133902 (2019)","DOI":"10.1109\/IGARSS.2019.8899001"},{"key":"1603_CR6","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.3390\/app10072471","volume":"10","author":"J Yu","year":"2020","unstructured":"Yu, J., Lin, Y., Zhu, Y., Xu, W., Zhang, G.: Segmentation of river scenes based on water surface reflection mechanism. Appl. Sci. 10, 2471 (2020)","journal-title":"Appl. Sci."},{"key":"1603_CR7","doi-asserted-by":"publisher","first-page":"13093","DOI":"10.1007\/s00500-021-06171-9","volume":"25","author":"R Sravanthi","year":"2021","unstructured":"Sravanthi, R., Sarma, A.S.: Efficient image-based object detection for floating weed collection with low cost unmanned floating vehicles. Soft. Comput. 25, 13093\u201313101 (2021)","journal-title":"Soft. Comput."},{"issue":"5","key":"1603_CR8","first-page":"73","volume":"35","author":"G Ling","year":"2022","unstructured":"Ling, G., Li, Y., Zhou, W., Liu, Y., Xiang, J.: Water area segmentation model design and channel pruning acceleration method for unmanned ship. Ind. Control Comput. 35(5), 73\u20135 (2022)","journal-title":"Ind. Control Comput."},{"key":"1603_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: A Nested U-Net Architecture for Medical Image Segmentation, pp. 13\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"1","key":"1603_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s40747-022-00793-8","volume":"9","author":"Y Yin","year":"2022","unstructured":"Yin, Y., Guo, Y., Deng, L., Chai, B.: Improved PSPNet-based water shoreline detection in complex inland river scenarios. Complex Intell. Syst. 9(1), 233\u201345 (2022)","journal-title":"Complex Intell. Syst."},{"key":"1603_CR11","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Computer Society, pp. 2881\u20132890 (2016)","DOI":"10.1109\/CVPR.2017.660"},{"key":"1603_CR12","doi-asserted-by":"crossref","unstructured":"von Braun, M.-S., Frenzel, P., K\u00e4ding, C., Mirco, F.: Utilizing mask R-CNN for waterline detection in canoe sprint video analysis. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3826\u20133835 (2020)","DOI":"10.1109\/CVPRW50498.2020.00446"},{"key":"1603_CR13","unstructured":"Kaiming, H., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 2961\u20132969 (2017)"},{"key":"1603_CR14","unstructured":"Thales, A., Marcato Junior, J., Nunes Gon\u00e7alves, W., Ol\u00e3 Bressan, P., Eltner, A., Binder, F., Singer, T.: Deep learning applied to water segmentation. In: ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 1189\u20131193 (2020)"},{"key":"1603_CR15","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 2481\u20132495 (2017)","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"1603_CR16","doi-asserted-by":"publisher","first-page":"12661","DOI":"10.1109\/TCYB.2021.3085856","volume":"52","author":"B Bovcon","year":"2021","unstructured":"Bovcon, B., Kristan, M.: WaSRA water segmentation and refinement maritime obstacle detection network. IEEE Trans. Cybern. 52, 12661\u201312674 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"1603_CR17","doi-asserted-by":"crossref","unstructured":"Wenqiang, Z., Changshi, X., Yuan, H., Xiong, Z., Qianqian, C., Tiantian, Y.: (Invited) Domain Adaptation for the Semantic Segmentation of the Unmanned Surface Vehicle, ECS Meeting Abstracts, pp. 73\u201383 (2020)","DOI":"10.1149\/09813.0073ecst"},{"key":"1603_CR18","doi-asserted-by":"crossref","unstructured":"Steccanella, L., Bloisi, D., Blum, J., Farinelli, A.: Deep Learning Waterline Detection for Low-Cost Autonomous Boats: Proceedings of the 15th International Conference IAS-15, Intelligent Autonomous Systems, p. 15 (2019)","DOI":"10.1007\/978-3-030-01370-7_48"},{"key":"1603_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1603_CR20","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1603_CR21","doi-asserted-by":"crossref","unstructured":"Lou, A., Loew, M.H.: CFPNET: channel-wise feature pyramid for real-time semantic segmentation. In: 2021 IEEE international conference on image processing (ICIP), pp. 1894\u20131898 (2021)","DOI":"10.1109\/ICIP42928.2021.9506485"},{"key":"1603_CR22","unstructured":"Kaiming, H., Zhang, X., Shaoqing, R., Jian, S.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015)"},{"key":"1603_CR23","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"issue":"2","key":"1603_CR24","doi-asserted-by":"publisher","first-page":"3964","DOI":"10.1109\/LRA.2021.3067271","volume":"6","author":"Y Cheng","year":"2021","unstructured":"Cheng, Y., Jiang, M., Zhu, J., Liu, Y.: Are we ready for unmanned surface vehicles in inland waterways The USVInland multisensor dataset and benchmark. IEEE Robot. Autom. Lett. 6(2), 3964\u201370 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1603_CR25","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1177\/0278364917751842","volume":"37","author":"M Miller","year":"2018","unstructured":"Miller, M., Chung, S.J., Hutchinson, S.: The VisualCInertial Canoe Dataset. Int. J. Robot. Res. 37, 13\u201320 (2018)","journal-title":"Int. J. Robot. Res."},{"key":"1603_CR26","doi-asserted-by":"crossref","unstructured":"Liang-Chieh, C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1603_CR27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, pp. 234\u2013241 (2015). arXiv:1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1603_CR28","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, pp. 325\u2013341 (2018). arXiv:1808.00897","DOI":"10.1007\/978-3-030-01261-8_20"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01603-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01603-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01603-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T17:18:21Z","timestamp":1738603101000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01603-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1603"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01603-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]},"assertion":[{"value":"10 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"51"}}