{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:13:20Z","timestamp":1777418000663,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T00:00:00Z","timestamp":1729296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T00:00:00Z","timestamp":1729296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 52331012"],"award-info":[{"award-number":["No. 52331012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shanghai","award":["No. 21ZR1426500"],"award-info":[{"award-number":["No. 21ZR1426500"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06527-6","type":"journal-article","created":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T11:02:30Z","timestamp":1729335750000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["ADV-YOLO: improved SAR ship detection model based on YOLOv8"],"prefix":"10.1007","volume":"81","author":[{"given":"Yuqin","family":"Huang","sequence":"first","affiliation":[]},{"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Han","sequence":"additional","affiliation":[]},{"given":"Zhongdai","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,19]]},"reference":[{"issue":"4","key":"6527_CR1","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1080\/2150704X.2024.2334193","volume":"15","author":"W Yu","year":"2024","unstructured":"Yu W, Li J, Wang Z, Yu Z, Luo Y, Liu Y, Feng J (2024) Detecting rotated ships in SAR images using a streamlined ship detection network and gliding phases. Remote Sens Lett 15(4):413\u2013422. https:\/\/doi.org\/10.1080\/2150704X.2024.2334193","journal-title":"Remote Sens Lett"},{"key":"6527_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3202495","volume":"60","author":"M Zhu","year":"2022","unstructured":"Zhu M, Guoping H, Zhou H, Wang S (2022) Multiscale ship detection method in SAR images based on information compensation and feature enhancement. IEEE Trans Geosci Remote Sens 60:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2022.3202495","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6527_CR3","doi-asserted-by":"publisher","first-page":"4438","DOI":"10.3390\/rs14184438","volume":"14","author":"Y Guo","year":"2022","unstructured":"Guo Y, Zhou L (2022) MEA-Net: a lightweight SAR ship detection model for imbalanced datasets. Remote Sens 14:4438. https:\/\/doi.org\/10.3390\/rs14184438","journal-title":"Remote Sens"},{"issue":"4","key":"6527_CR4","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s11760-018-1408-4","volume":"13","author":"S Chen","year":"2019","unstructured":"Chen S, Li X (2019) A new CFAR algorithm based on variable window for ship target detection in SAR images. Signal Image V Process 13(4):779\u2013786. https:\/\/doi.org\/10.1007\/s11760-018-1408-4","journal-title":"Signal Image V Process"},{"issue":"6","key":"6527_CR5","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 (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"6527_CR6","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","volume":"16","author":"Z Lin","year":"2019","unstructured":"Lin Z, Ji K, Leng X, Kuang G (2019) Squeeze and excitation rank faster R-CNN for ship detection in SAR images. IEEE Geosci Remote Sens Lett 16(5):751\u2013755. https:\/\/doi.org\/10.1109\/LGRS.2018.2882551","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"14","key":"6527_CR7","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.3390\/rs13142771","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang T, Zhang X, Ke X (2021) Quad-FPN: a novel quad feature pyramid network for SAR ship detection. Remote Sens 13(14):2771. https:\/\/doi.org\/10.3390\/rs13142771","journal-title":"Remote Sens"},{"key":"6527_CR8","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.322"},{"key":"6527_CR9","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp 27\u201330","DOI":"10.1109\/CVPR.2016.91"},{"issue":"5","key":"6527_CR10","doi-asserted-by":"publisher","first-page":"871","DOI":"10.3390\/rs13050871","volume":"13","author":"G Tang","year":"2021","unstructured":"Tang G, Zhuge Y, Claramunt C et al (2021) N-YOLO: a SAR ship detection using noise-classifying and complete-target extraction[J]. Remote Sens 13(5):871. https:\/\/doi.org\/10.3390\/rs13050871","journal-title":"Remote Sens"},{"key":"6527_CR11","doi-asserted-by":"publisher","first-page":"5268","DOI":"10.3390\/rs14205268","volume":"14","author":"S Wang","year":"2022","unstructured":"Wang S, Gao S, Zhou L, Liu R, Zhang H, Liu J, Jia Y, Qian J (2022) YOLO-SD: small ship detection in SAR images by multi-scale convolution and feature transformer module. Remote Sens 14:5268. https:\/\/doi.org\/10.3390\/rs14205268","journal-title":"Remote Sens"},{"key":"6527_CR12","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11554-024-01445-5","volume":"21","author":"S Cai","year":"2024","unstructured":"Cai S, Meng H, Wu J (2024) FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement. J Real-Time Image Proc 21:61. https:\/\/doi.org\/10.1007\/s11554-024-01445-5","journal-title":"J Real-Time Image Proc"},{"key":"6527_CR13","doi-asserted-by":"publisher","first-page":"4851","DOI":"10.3390\/rs13234851","volume":"13","author":"M Kim","year":"2021","unstructured":"Kim M, Jeong J, Kim S (2021) ECAP-YOLO: efficient channel attention pyramid YOLO for small object detection in aerial image. Remote Sens 13:4851. https:\/\/doi.org\/10.3390\/rs13234851","journal-title":"Remote Sens"},{"key":"6527_CR14","doi-asserted-by":"publisher","first-page":"24554","DOI":"10.1109\/ACCESS.2024.3365777","volume":"12","author":"Z Liangjun","year":"2024","unstructured":"Liangjun Z, Feng N, Yubin X, Gang L, Zhongliang H, Yuanyang Z (2024) MSFA-YOLO: a multi-scale SAR ship detection algorithm based on fused attention. IEEE Access 12:24554\u201324568. https:\/\/doi.org\/10.1109\/ACCESS.2024.3365777","journal-title":"IEEE Access"},{"key":"6527_CR15","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.3390\/rs13112171","volume":"13","author":"Y Qing","year":"2021","unstructured":"Qing Y, Liu W, Feng L, Gao W (2021) Improved YOLO network for free-angle remote sensing target detection. Remote Sens 13:2171. https:\/\/doi.org\/10.3390\/rs13112171","journal-title":"Remote Sens"},{"key":"6527_CR16","doi-asserted-by":"publisher","first-page":"190","DOI":"10.3390\/jmse12010190","volume":"12","author":"Z Jiang","year":"2024","unstructured":"Jiang Z, Su L, Sun Y (2024) YOLOv7-ship: a lightweight algorithm for ship object detection in complex marine environments. J Mar Sci Eng 12:190. https:\/\/doi.org\/10.3390\/jmse12010190","journal-title":"J Mar Sci Eng"},{"issue":"1","key":"6527_CR17","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1080\/17445302.2022.2142362","volume":"19","author":"Y Ning","year":"2024","unstructured":"Ning Y, Zhao L, Zhang C, Yuan Z (2024) STD-Yolov5: a ship-type detection model based on improved Yolov5. Sh Offsh Struct 19(1):66\u201375. https:\/\/doi.org\/10.1080\/17445302.2022.2142362","journal-title":"Sh Offsh Struct"},{"key":"6527_CR18","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"6527_CR19","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Song X, ZangY, Wang W, Lu T, Yu G, Shen C (2019) Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea, pp 8440\u20138449","DOI":"10.1109\/ICCV.2019.00853"},{"key":"6527_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/ipr2.13073","volume":"00","author":"F Chen","year":"2024","unstructured":"Chen F, Deng M, Gao H, Yang X, Zhang D (2024) NHD-YOLO: improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation. IET Image Process 00:1\u201312. https:\/\/doi.org\/10.1049\/ipr2.13073","journal-title":"IET Image Process"},{"key":"6527_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3379090","volume":"73","author":"H Wang","year":"2024","unstructured":"Wang H, Liu C, Cai Y, Chen L, Li Y (2024) YOLOv8-QSD: an improved small object detection algorithm for autonomous vehicles based on YOLOv8. IEEE Trans Instrum Meas 73:1\u201316. https:\/\/doi.org\/10.1109\/TIM.2024.3379090","journal-title":"IEEE Trans Instrum Meas"},{"key":"6527_CR22","doi-asserted-by":"crossref","unstructured":"Sunkara R, Luo T (2022) No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Grenoble, France, pp 443\u2013459","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"6527_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-024-03003-9","author":"Y Sun","year":"2024","unstructured":"Sun Y, Zhang Y, Wang H et al (2024) SES-YOLOv8n: automatic driving object detection algorithm based on improved YOLOv8. SIViP. https:\/\/doi.org\/10.1007\/s11760-024-03003-9","journal-title":"SIViP"},{"key":"6527_CR24","unstructured":"Wei H, Liu X, Xu S, Dai Z, Dai Y, Xu X (2022) DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation"},{"key":"6527_CR25","doi-asserted-by":"publisher","first-page":"227288","DOI":"10.1109\/ACCESS.2020.3046515","volume":"8","author":"Y Li","year":"2020","unstructured":"Li Y, Li S, Du H, Chen L, Zhang D, Li Y (2020) YOLO-ACN: focusing on small target and occluded object detection. IEEE Access 8:227288\u2013227303. https:\/\/doi.org\/10.1109\/ACCESS.2020.3046515","journal-title":"IEEE Access"},{"key":"6527_CR26","doi-asserted-by":"publisher","DOI":"10.1080\/22797254.2024.2307613","author":"X Jiang","year":"2024","unstructured":"Jiang X, Cai J, Wang B (2024) YOLOSeaShip: a lightweight model for real-time ship detection. Eur J Remote Sens. https:\/\/doi.org\/10.1080\/22797254.2024.2307613","journal-title":"Eur J Remote Sens"},{"key":"6527_CR27","unstructured":"Gevorgyan, Z (2022) SIoU loss: More powerful learning for bounding box regression. arXiv, arXiv:2205.12740"},{"key":"6527_CR28","doi-asserted-by":"publisher","first-page":"7190","DOI":"10.3390\/s23167190","volume":"23","author":"G Wang","year":"2023","unstructured":"Wang G, Chen Y, An P, Hong H, Hu J, Huang T (2023) UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 23:7190. https:\/\/doi.org\/10.3390\/s23167190","journal-title":"Sensors"},{"key":"6527_CR29","unstructured":"Jeune PL, Zerga\u00efnoh-Mokraoui A (2023) Rethinking intersection over union for small object detection in few-shot regime. ArXiv\u00a0abs\/2307.09562 (2023): n. pag"},{"key":"6527_CR30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2301.10051","author":"Z Tong","year":"2023","unstructured":"Tong Z, Chen Y, Xu Z, Yu R (2023) Wise-IoU: bounding box regression loss with dynamic focusing mechanism. ArXiv. https:\/\/doi.org\/10.48550\/arXiv.2301.10051","journal-title":"ArXiv"},{"key":"6527_CR31","doi-asserted-by":"publisher","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","volume":"8","author":"S Wei","year":"2020","unstructured":"Wei S, Zeng X, Qu Q, Wang M, Su H, Shi J (2020) HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation. IEEE Access 8:120234\u2013120254. https:\/\/doi.org\/10.1109\/ACCESS.2020.3005861","journal-title":"IEEE Access"},{"issue":"18","key":"6527_CR32","doi-asserted-by":"publisher","first-page":"3690","DOI":"10.3390\/rs13183690","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang T, Zhang X, Li J, Xu X, Wang B, Zhan X, Xu Y, Ke X, Zeng T, Su H (2021) SAR ship detection dataset (SSDD): official release and comprehensive data analysis. Remote Sens 13(18):3690. https:\/\/doi.org\/10.3390\/rs13183690","journal-title":"Remote Sens"},{"key":"6527_CR33","doi-asserted-by":"crossref","unstructured":"Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020). Bridging 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":"6527_CR34","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F, S\u00fcnderhauf N (2021) Varifocalnet: An IoU-aware dense object detector, In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp 8510\u20138519","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"6527_CR35","doi-asserted-by":"crossref","unstructured":"Feng C, Zhong Y, Gao Y, Scott MR, HuangW (2021) Tood: Task-aligned one-stage object detection. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"6527_CR36","volume-title":"Computer vision\u2014ECCV 2020 ECCV 2020. lecture notes in computer science","author":"J Wang","year":"2020","unstructured":"Wang J et al (2020) Side-aware boundary localization for more precise object detection. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) Computer vision\u2014ECCV 2020 ECCV 2020. lecture notes in computer science, vol 12349. Springer, Cham"},{"issue":"8","key":"6527_CR37","doi-asserted-by":"publisher","first-page":"10070","DOI":"10.1109\/TPAMI.2023.3248583","volume":"45","author":"Z Zheng","year":"2023","unstructured":"Zheng Z et al (2023) Localization distillation for object detection. IEEE Trans Pattern Anal Mach Intell 45(8):10070\u201310083. https:\/\/doi.org\/10.1109\/TPAMI.2023.3248583","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6527_CR38","doi-asserted-by":"publisher","unstructured":"Liu S, Qi L, Qin H-F, Shi J-P, Jia J-Y. (2018) Path aggregation network for instance segmentation. In:\u00a02018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition;\u00a0Salt Lake City, UT, United States:\u00a0IEEE\u00a0(2018). pp 8759\u20138768. https:\/\/doi.org\/10.1109\/CVPR.2018.00913","DOI":"10.1109\/CVPR.2018.00913"},{"key":"6527_CR39","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"2","key":"6527_CR40","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.32604\/cmc.2023.042311","volume":"77","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Han D, Chen P (2023) Swin-PAFF: a SAR ship detection network with contextual cross-information fusion. Comput Mater Contin 77(2):2657\u20132675. https:\/\/doi.org\/10.32604\/cmc.2023.042311","journal-title":"Comput Mater Contin"},{"key":"6527_CR41","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2024.2313854","author":"W Wang","year":"2024","unstructured":"Wang W, Han D, Chen C, Zhongdai W (2024) FastPFM: a multi-scale ship detection algorithm for complex scenes based on SAR images. Connect Sci. https:\/\/doi.org\/10.1080\/09540091.2024.2313854","journal-title":"Connect Sci"},{"issue":"1","key":"6527_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/09540091.2023.2257399","volume":"35","author":"H Wang","year":"2023","unstructured":"Wang H, Han D, Cui M, Chen C (2023) NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention. Connect Sci 35(1):1\u201332. https:\/\/doi.org\/10.1080\/09540091.2023.2257399","journal-title":"Connect Sci"},{"key":"6527_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-024-03212-2","author":"S Cai","year":"2024","unstructured":"Cai S, Meng H, Yuan M et al (2024) FS-YOLO: a multi-scale SAR ship detection network in complex scenes. SIViP. https:\/\/doi.org\/10.1007\/s11760-024-03212-2","journal-title":"SIViP"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06527-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06527-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06527-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T11:03:48Z","timestamp":1729335828000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06527-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,19]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6527"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06527-6","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,19]]},"assertion":[{"value":"21 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declrations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"34"}}