{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:32:54Z","timestamp":1773909174158,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>There are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7\u20134.9% based on different backbone networks, and the detection performance is better than other advanced algorithms.<\/jats:p>","DOI":"10.1007\/s40747-023-01240-y","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T09:02:44Z","timestamp":1700211764000},"page":"2387-2400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel twin branch network based on mutual training strategy for ship detection in SAR images"],"prefix":"10.1007","volume":"10","author":[{"given":"Yilong","family":"Lv","sequence":"first","affiliation":[]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yujie","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"issue":"1","key":"1240_CR1","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1109\/TGRS.2019.2937175","volume":"58","author":"L Du","year":"2019","unstructured":"Du L et al (2019) Target discrimination based on weakly supervised learning for high-resolution SAR images in complex scenes. IEEE Trans Geosci Remote Sens 58(1):461\u2013472","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1240_CR2","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TGRS.2018.2864716","volume":"57","author":"M Shahzad","year":"2018","unstructured":"Shahzad M et al (2018) Buildings detection in VHR SAR images using fully convolution neural networks. IEEE Trans Geosci Remote Sens 57(2):1100\u20131116","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"1240_CR3","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1109\/JSTARS.2017.2755672","volume":"11","author":"L Huang","year":"2017","unstructured":"Huang L et al (2017) OpenSARShip: a dataset dedicated to Sentinel-1 ship interpretation. IEEE J Sel Top Appl Earth Observ Remote Sens 11(1):195\u2013208","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"12","key":"1240_CR4","doi-asserted-by":"publisher","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","volume":"55","author":"Z Zhang","year":"2017","unstructured":"Zhang Z et al (2017) Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Trans Geosci Remote Sens 55(12):7177\u20137188","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"1240_CR5","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/LGRS.2018.2879969","volume":"16","author":"G Yang","year":"2018","unstructured":"Yang G et al (2018) Unsupervised change detection of SAR images based on variational multivariate Gaussian mixture model and Shannon entropy. IEEE Geosci Remote Sens Lett 16(5):826\u2013830","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"4","key":"1240_CR6","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/TGRS.2018.2870716","volume":"57","author":"CH Gierull","year":"2018","unstructured":"Gierull CH (2018) Demystifying the capability of sublook correlation techniques for vessel detection in SAR imagery. IEEE Trans Geosci Remote Sens 57(4):2031\u20132042","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"1240_CR7","doi-asserted-by":"publisher","first-page":"3616","DOI":"10.1109\/JSTARS.2017.2692820","volume":"10","author":"P Iervolino","year":"2017","unstructured":"Iervolino P, Guida R (2017) A novel ship detector based on the generalized-likelihood ratio test for SAR imagery. IEEE J Sel Top Appl Earth Observ Remote Sens 10(8):3616\u20133630","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"7","key":"1240_CR8","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1109\/JSTARS.2015.2417756","volume":"8","author":"CP Schwegmann","year":"2015","unstructured":"Schwegmann CP, Kleynhans W, Salmon BP (2015) Manifold adaptation for constant false alarm rate ship detection in South African oceans. IEEE J Sel Top Appl Earth Observ Remote Sens 8(7):3329\u20133337","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"3","key":"1240_CR9","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1109\/TGRS.2017.2763089","volume":"56","author":"CH Gierull","year":"2017","unstructured":"Gierull CH, Sikaneta I (2017) A compound-plus-noise model for improved vessel detection in non-Gaussian SAR imagery. IEEE Trans Geosci Remote Sens 56(3):1444\u20131453","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1240_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/7.937460","volume":"37","author":"LM Kaplan","year":"2001","unstructured":"Kaplan LM (2001) Improved SAR target detection via extended fractal features. IEEE Trans Aerosp Electron Syst 37(2):436\u2013451","journal-title":"IEEE Trans Aerosp Electron Syst"},{"issue":"2","key":"1240_CR11","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/LGRS.2005.845033","volume":"2","author":"M Tello","year":"2005","unstructured":"Tello M, L\u00f3pez-Mart\u00ednez C, Mallorqui JJ (2005) A novel algorithm for ship detection in SAR imagery based on the wavelet transform. IEEE Geosci Remote Sens Lett 2(2):201\u2013205","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"1240_CR12","first-page":"319","volume":"13","author":"S Song","year":"2016","unstructured":"Song S et al (2016) Ship detection in SAR imagery via variational Bayesian inference. IEEE Geosci Remote Sens Lett 13(3):319\u2013323","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"1240_CR13","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.1109\/LGRS.2019.2905714","volume":"16","author":"E Ferrentino","year":"2019","unstructured":"Ferrentino E et al (2019) Detection of wind turbines in intertidal areas using SAR polarimetry. IEEE Geosci Remote Sens Lett 16(10):1516\u20131520","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"8","key":"1240_CR14","doi-asserted-by":"publisher","first-page":"3616","DOI":"10.1109\/JSTARS.2017.2692820","volume":"10","author":"P Iervolino","year":"2017","unstructured":"Iervolino P, Guida R (2017) A novel ship detector based on the generalized-likelihood ratio test for SAR imagery. IEEE J Sel Top Appl Earth Observ Remote Sens 10(8):3616\u20133630","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"8","key":"1240_CR15","doi-asserted-by":"publisher","first-page":"5407","DOI":"10.1109\/TGRS.2019.2899337","volume":"57","author":"H Lang","year":"2019","unstructured":"Lang H, Xi Y, Zhang X (2019) Ship detection in high-resolution SAR images by clustering spatially enhanced pixel descriptor. IEEE Trans Geosci Remote Sens 57(8):5407\u20135423","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"1240_CR16","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.1109\/JSTARS.2018.2820078","volume":"11","author":"X Leng","year":"2018","unstructured":"Leng X et al (2018) Area ratio invariant feature group for ship detection in SAR imagery. IEEE J Sel Top Appl Earth Observ Remote Sens 11(7):2376\u20132388","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"9","key":"1240_CR17","doi-asserted-by":"publisher","first-page":"6731","DOI":"10.1109\/TGRS.2020.2979252","volume":"58","author":"T Liu","year":"2020","unstructured":"Liu T et al (2020) Robust CFAR detector based on truncated statistics for polarimetric synthetic aperture radar. IEEE Trans Geosci Remote Sens 58(9):6731\u20136747","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"1240_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/JSTARS.2017.2764506","volume":"11","author":"T Li","year":"2017","unstructured":"Li T et al (2017) An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens 11(1):184\u2013194","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"2","key":"1240_CR19","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/JSTARS.2017.2787573","volume":"11","author":"W Ao","year":"2018","unstructured":"Ao W et al (2018) Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens 11(2):536\u2013550","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"1240_CR20","doi-asserted-by":"crossref","unstructured":"Zhu M et al (2020) Rapid ship detection in SAR images based on YOLOv3. In: 2020 5th international conference on communication, image and signal processing (CCISP). IEEE","DOI":"10.1109\/CCISP51026.2020.9273476"},{"key":"1240_CR21","doi-asserted-by":"crossref","unstructured":"Chen Y, Yu J, Xu Y (2020) SAR ship target detection for SSDv2 under complex backgrounds. In: 2020 International conference on computer vision, image and deep learning (CVIDL). IEEE","DOI":"10.1109\/CVIDL51233.2020.00-27"},{"key":"1240_CR22","doi-asserted-by":"crossref","unstructured":"Zhang T et al (2020) Balanced feature pyramid network for ship detection in synthetic aperture radar images. In: 2020 IEEE radar conference (RadarConf20). IEEE","DOI":"10.1109\/RadarConf2043947.2020.9266519"},{"issue":"6","key":"1240_CR23","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.1109\/TGRS.2018.2889353","volume":"57","author":"Z Deng","year":"2019","unstructured":"Deng Z et al (2019) Learning deep ship detector in SAR images from scratch. IEEE Trans Geosci Remote Sens 57(6):4021\u20134039","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1240_CR24","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. ArXiv preprint arXiv:2004.10934"},{"key":"1240_CR25","unstructured":"Ren S et al (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28"},{"key":"1240_CR26","doi-asserted-by":"crossref","unstructured":"Redmon J et al (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.91"},{"key":"1240_CR27","doi-asserted-by":"crossref","unstructured":"Liu W et al (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1240_CR28","doi-asserted-by":"crossref","unstructured":"Lin T-Y, et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2017.324"},{"key":"1240_CR29","doi-asserted-by":"crossref","unstructured":"Tian Z et al (2019) Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00972"},{"key":"1240_CR30","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong T et al (2020) Foveabox: beyond anchor-based object detection. IEEE Trans Image Process 29:7389\u20137398","journal-title":"IEEE Trans Image Process"},{"key":"1240_CR31","doi-asserted-by":"crossref","unstructured":"Jiang B et al (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"1240_CR32","unstructured":"Zhu B et al (2020) Autoassign: differentiable label assignment for dense object detection. arXiv preprint arXiv:2007.03496"},{"key":"1240_CR33","doi-asserted-by":"crossref","unstructured":"Qiu H et al (2020) Borderdet: border feature for dense object detection. In: European conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-030-58452-8_32"},{"key":"1240_CR34","doi-asserted-by":"crossref","unstructured":"Zhang S et al (2020) \u2018Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"1240_CR35","doi-asserted-by":"crossref","unstructured":"Choi J et al (2019) Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00059"},{"key":"1240_CR36","doi-asserted-by":"crossref","unstructured":"He Y et al (2019) Bounding box regression with uncertainty for accurate object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00300"},{"issue":"18","key":"1240_CR37","doi-asserted-by":"publisher","first-page":"3690","DOI":"10.3390\/rs13183690","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang T et al (2021) Sar ship detection dataset (ssdd): official release and comprehensive data analysis. Remote Sens 13(18):3690","journal-title":"Remote Sens"},{"key":"1240_CR38","doi-asserted-by":"publisher","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","volume":"8","author":"S Wei","year":"2020","unstructured":"Wei S et al (2020) HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation. IEEE Access 8:120234\u2013120254","journal-title":"IEEE Access"},{"issue":"2","key":"1240_CR39","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M et al (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2):303\u2013338","journal-title":"Int J Comput Vision"},{"key":"1240_CR40","doi-asserted-by":"crossref","unstructured":"Lin T-Y et al (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1240_CR41","unstructured":"Chen K et al (2019) MMDetection: open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155"},{"key":"1240_CR42","doi-asserted-by":"crossref","unstructured":"Wang N et al (2020) Nas-fcos: fast neural architecture search for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.01196"},{"key":"1240_CR43","doi-asserted-by":"crossref","unstructured":"Kim K, Lee HS (2020) Probabilistic anchor assignment with iou prediction for object detection. In: European conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"1240_CR44","first-page":"21002","volume":"33","author":"X Li","year":"2020","unstructured":"Li X et al (2020) Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. Adv Neural Inf Process Syst 33:21002\u201321012","journal-title":"Adv Neural Inf Process Syst"},{"key":"1240_CR45","doi-asserted-by":"crossref","unstructured":"Cao Y et al (2020) Prime sample attention in object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11583\u201311591","DOI":"10.1109\/CVPR42600.2020.01160"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01240-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01240-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01240-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T15:29:01Z","timestamp":1711812541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01240-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1240"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01240-y","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,17]]},"assertion":[{"value":"28 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}