{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:37:33Z","timestamp":1790311053200,"version":"4.1.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF2022R1A2C1011774"],"award-info":[{"award-number":["NRF2022R1A2C1011774"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-80239-y","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T16:39:26Z","timestamp":1732639166000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Ship detection using ensemble deep learning techniques from synthetic aperture radar imagery"],"prefix":"10.1038","volume":"14","author":[{"given":"Himanshu","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Om Prakash","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarun Kumar","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirdesh","family":"Varshney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wooguil","family":"Pak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"80239_CR1","doi-asserted-by":"publisher","first-page":"9424","DOI":"10.1109\/TGRS.2020.3042506","volume":"59","author":"X Wang","year":"2021","unstructured":"Wang, X., Li, G., Plaza, A. & He, Y. Ship detection in SAR images via enhanced nonnegative sparse locality-representation of fisher vectors. IEEE Trans. Geosci. Remote Sens. 59, 9424\u20139438 (2021).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"80239_CR2","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1109\/TGRS.2020.2995477","volume":"59","author":"L Li","year":"2021","unstructured":"Li, L., Zhou, Z., Wang, B., Miao, L. & Zong, H. A novel CNN-based method for accurate ship detection in HR optical remote sensing images via rotated bounding box. IEEE Trans. Geosci. Remote Sens. 59, 686\u2013699 (2021).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"80239_CR3","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.aqpro.2015.02.040","volume":"4","author":"GS Dwarakish","year":"2015","unstructured":"Dwarakish, G. S. & Salim, A. M. Review on the role of ports in the development of a nation. Aquat. Procedia 4, 295\u2013301 (2015).","journal-title":"Aquat. Procedia"},{"key":"80239_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, T. et al. SAR ship detection dataset (SSDD): official release and comprehensive data analysis. Remote Sens. 13, 3690 (2021).","DOI":"10.3390\/rs13183690"},{"key":"80239_CR5","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, S. & Wang, W. Q. A lightweight faster R-CNN for ship detection in SAR images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022).","DOI":"10.1109\/LGRS.2020.3038901"},{"key":"80239_CR6","doi-asserted-by":"publisher","first-page":"3412","DOI":"10.1080\/01431161.2022.2091966","volume":"43","author":"T Xie","year":"2022","unstructured":"Xie, T., Liu, M., Zhang, M., Qi, S. & Yang, J. Ship detection based on a superpixel-level CFAR detector for SAR imagery. Int. J. Remote Sens. 43, 3412\u20133428 (2022).","journal-title":"Int. J. Remote Sens."},{"key":"80239_CR7","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TGRS.2020.2997200","volume":"59","author":"Z Cui","year":"2021","unstructured":"Cui, Z., Wang, X., Liu, N., Cao, Z. & Yang, J. Ship detection in large-scale SAR images via spatial shuffle-group enhance attention. IEEE Trans. Geosci. Remote Sens. 59, 379\u2013391 (2021).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"80239_CR8","doi-asserted-by":"crossref","unstructured":"Kumar, S. et al. A Novel YOLOv3 algorithm-based deep learning approach for waste segregation: towards smart waste management. Electronics 10, 14 (2020).","DOI":"10.3390\/electronics10010014"},{"key":"80239_CR9","doi-asserted-by":"publisher","unstructured":"Gupta, H. & Verma, O. P. Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach. Multimedia Tools Appl. 2021, 1\u201321. https:\/\/doi.org\/10.1007\/s11042-021-11146-x (2021).","DOI":"10.1007\/s11042-021-11146-x"},{"key":"80239_CR10","doi-asserted-by":"publisher","unstructured":"Gupta, H., Jindal, P. & Verma, O. P. Automatic Vehicle Detection from Satellite Images Using Deep Learning Algorithm 551\u2013562 (Springer, Singapore, 2021). https:\/\/doi.org\/10.1007\/978-981-16-1696-9_52.","DOI":"10.1007\/978-981-16-1696-9_52"},{"key":"80239_CR11","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/MGRS.2020.3046356","volume":"9","author":"XX Zhu","year":"2021","unstructured":"Zhu, X. X. et al. Deep learning meets SAR: concepts, models, pitfalls, and perspectives. IEEE Geosci. Remote Sens. Mag. 9, 143\u2013172 (2021).","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"80239_CR12","doi-asserted-by":"crossref","unstructured":"Cozzolino, D., Di Martino, G., Poggi, G. & Verdoliva, L. A fully convolutional neural network for low-complexity single-stage ship detection in Sentinel-1 SAR images. In International Geoscience and Remote Sensing Symposium (IGARSS) 2017-July 886\u2013889 (2017).","DOI":"10.1109\/IGARSS.2017.8127094"},{"key":"80239_CR13","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"80239_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C. & Shao, J. Ship detection in SAR images based on an improved faster R-CNN. In Proceedings of 2017 SAR in Big Data Era: Models, Methods and Applications, BIGSARDATA 2017, 2017-January 1\u20136 (2017).","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"80239_CR15","doi-asserted-by":"publisher","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","volume":"6","author":"J Jiao","year":"2018","unstructured":"Jiao, J. et al. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection. IEEE Access 6, 20881\u201320892 (2018).","journal-title":"IEEE Access"},{"key":"80239_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition vols 2016-Decem 779\u2013788 (IEEE Computer Society, 2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"80239_CR17","doi-asserted-by":"crossref","unstructured":"Chang, Y. L. et al. Ship detection based on YOLOv2 for SAR imagery. Remote Sens. 11, 786 (2019).","DOI":"10.3390\/rs11070786"},{"key":"80239_CR18","doi-asserted-by":"publisher","unstructured":"Jiang, S. et al. Ship detection with sar based on Yolo. In International Geoscience and Remote Sensing Symposium (IGARSS) 1647\u20131650 (2020). https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9324538.","DOI":"10.1109\/IGARSS39084.2020.9324538"},{"key":"80239_CR19","doi-asserted-by":"publisher","unstructured":"Devadharshini, S., Kalaipriya, R., Rajmohan, R., Pavithra, M. & Ananthkumar, T. Performance investigation of hybrid YOLO-VGG16 based ship detection framework using SAR images. In 2020 International Conference on System, Computation, Automation and Networking, ICSCAN 2020. https:\/\/doi.org\/10.1109\/ICSCAN49426.2020.9262440 (2020).","DOI":"10.1109\/ICSCAN49426.2020.9262440"},{"key":"80239_CR20","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhuge, Y., Claramunt, C. & Men, S. N-YOLO: A SAR ship detection using noise-classifying and complete-target extraction. Remote Sens. 13, 871 (2021).","DOI":"10.3390\/rs13050871"},{"key":"80239_CR21","doi-asserted-by":"publisher","unstructured":"Zhu, H. et al. DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images. Sensors. https:\/\/doi.org\/10.3390\/s21238146 (2021).","DOI":"10.3390\/s21238146"},{"key":"80239_CR22","doi-asserted-by":"publisher","unstructured":"Wang, J., Lin, Y., Guo, J. & Zhuang, L. SSS-YOLO: towards more accurate detection for small ships in SAR image. Remote Sens. Lett. https:\/\/doi.org\/10.1080\/2150704X.2020.1837988 (2020).","DOI":"10.1080\/2150704X.2020.1837988"},{"key":"80239_CR23","doi-asserted-by":"publisher","first-page":"107787","DOI":"10.1016\/j.patcog.2020.107787","volume":"112","author":"H Guo","year":"2021","unstructured":"Guo, H., Yang, X., Wang, N. & Gao, X. A CenterNet++ model for ship detection in SAR images. Pattern Recogn. 112, 107787 (2021).","journal-title":"Pattern Recogn."},{"key":"80239_CR24","first-page":"850","volume":"3","author":"A Neubeck","year":"2006","unstructured":"Neubeck, A. & Van Gool, L. Efficient non-maximum suppression. Proc. Int. Conf. Pattern Recogn. 3, 850\u2013855 (2006).","journal-title":"Proc. Int. Conf. Pattern Recogn."},{"key":"80239_CR25","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R. & Davis, L. S. Soft-NMS\u2014improving object detection with one line of code. In Proceedings of the IEEE International Conference on Computer Vision 5562\u20135570 (2017).","DOI":"10.1109\/ICCV.2017.593"},{"key":"80239_CR26","doi-asserted-by":"publisher","unstructured":"Mehbodniya, A. et al. Fetal health classification from cardiotocographic data using machine learning. Expert Syst. 2021, 1\u201313. https:\/\/doi.org\/10.1111\/exsy.12899 (2021).","DOI":"10.1111\/exsy.12899"},{"key":"80239_CR27","doi-asserted-by":"publisher","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"R Solovyev","year":"2021","unstructured":"Solovyev, R., Wang, W. & Gabruseva, T. Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021).","journal-title":"Image Vis. Comput."},{"key":"80239_CR28","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H. et al. Generalized intersection over union: a metric and a loss for bounding box regression. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 658\u2013666 (2019).","DOI":"10.1109\/CVPR.2019.00075"},{"key":"80239_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, Z. et al. Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (Wiley, 2019).","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"80239_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3095305","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z. et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybern. https:\/\/doi.org\/10.1109\/TCYB.2021.3095305 (2021).","journal-title":"IEEE Trans. Cybern."},{"key":"80239_CR31","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y. & Wei, S. A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds (Springer, 2019). https:\/\/doi.org\/10.3390\/rs11070765.","DOI":"10.3390\/rs11070765"},{"key":"80239_CR32","unstructured":"Bochkovskiy, A., Wang, C.-Y. & Liao, H.-Y. M. YOLOv4: Optimal Speed and Accuracy of Object Detection (Wiley, 2020)."},{"key":"80239_CR33","doi-asserted-by":"publisher","first-page":"105742","DOI":"10.1016\/j.compag.2020.105742","volume":"178","author":"D Wu","year":"2020","unstructured":"Wu, D., Lv, S., Jiang, M. & Song, H. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agric. 178, 105742 (2020).","journal-title":"Comput. Electron. Agric."},{"key":"80239_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3040273","volume":"60","author":"Y Yu","year":"2022","unstructured":"Yu, Y., Yang, X., Li, J. & Gao, X. A cascade rotated anchor-aided detector for ship detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2020.3040273 (2022).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"80239_CR35","doi-asserted-by":"publisher","unstructured":"Zhao, Q., Wu, Y. & Yuan, Y. Ship target detection in optical remote sensing images based on E2YOLOX-VFL. Remote Sens. 16, 340. https:\/\/doi.org\/10.3390\/rs16020340 (2024).","DOI":"10.3390\/rs16020340"},{"key":"80239_CR36","doi-asserted-by":"crossref","unstructured":"Gao, F., Shi, W., Wang, J., Yang, E. & Zhou, H. Enhanced feature extraction for ship detection from multi-resolution and multi-scene synthetic aperture radar (SAR) images. Remote Sens. 11, 2694 (2019).","DOI":"10.3390\/rs11222694"},{"key":"80239_CR37","doi-asserted-by":"crossref","unstructured":"Yu, Y., Yang, X., Li, J. & Gao, X. A cascade rotated anchor-aided detector for ship detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2022).","DOI":"10.1109\/TGRS.2021.3128060"},{"key":"80239_CR38","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neucom.2022.04.127","volume":"497","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Su, D., Lauria, S. & Liu, X. Recent advances on loss functions in deep learning for computer vision. Neurocomputing 497, 129\u2013158 (2022).","journal-title":"Neurocomputing"},{"key":"80239_CR39","unstructured":"Pang, J. et al. Libra R-CNN: Towards Balanced Learning for Object Detection (Springer, 2024)"},{"key":"80239_CR40","doi-asserted-by":"publisher","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","volume":"57","author":"Z Cui","year":"2019","unstructured":"Cui, Z., Li, Q., Cao, Z. & Liu, N. Dense attention pyramid networks for multi-scale ship detection in SAR images. IEEE Trans. Geosci. Remote Sens. 57, 8983\u20138997 (2019).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"80239_CR41","doi-asserted-by":"publisher","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","volume":"7","author":"C Chen","year":"2019","unstructured":"Chen, C., He, C., Hu, C., Pei, H. & Jiao, L. A deep neural network based on an attention mechanism for SAR ship detection in multiscale and complex scenarios. IEEE Access 7, 104848\u2013104863 (2019).","journal-title":"IEEE Access"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-80239-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-80239-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-80239-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T17:06:40Z","timestamp":1732640800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-80239-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["80239"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-80239-y","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"15 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29397"}}