{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:43:20Z","timestamp":1776822200559,"version":"3.51.2"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Image Video Process."],"DOI":"10.1186\/s13640-026-00690-x","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:19:41Z","timestamp":1776820781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Yolov5-BSD: a maritime remote sensing object detector based on Yolov5"],"prefix":"10.1186","volume":"2026","author":[{"given":"Tianyi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2226-482X","authenticated-orcid":false,"given":"Xiantao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Cen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"690_CR1","unstructured":"Z. Gevorgyan, SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv (2022). http:\/\/arxiv.org\/abs\/2205.12740 Accessed 4 Mar 2024"},{"issue":"15","key":"690_CR2","doi-asserted-by":"publisher","first-page":"3731","DOI":"10.3390\/rs14153731","volume":"14","author":"W Wang","year":"2022","unstructured":"W. Wang, X. Zhang, W. Sun, M. Huang, A Novel Method of Ship Detection under Cloud Interference for Optical Remote Sensing Images. Remote Sens. 14(15), 3731 (2022). (Accessed 2024\u201305-15)","journal-title":"Remote Sens."},{"issue":"1s","key":"690_CR3","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1145\/3472810.","volume":"18","author":"C Yan","year":"2022","unstructured":"C. Yan, L. Meng, L. Li, J. Zhang, Z. Wang, J. Yin, J. Zhang, Y. Sun, B. Zheng, Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention. ACM Trans. Multimedia Comput. Commun. Appl. 18(1s), 29\u201312918 (2022). https:\/\/doi.org\/10.1145\/3472810. (Accessed 2024-12-21)","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"690_CR4","doi-asserted-by":"crossref","unstructured":"S. Mishra, P. Shalu, D. Soman, I. Singh, Advanced Ship Detection System using Yolo v7. In: 2023 IEEE 7th Conference on Information and Communication Technology (CICT), pp. 1\u20136. IEEE, Jabalpur, India (2023). https:\/\/ieeexplore.ieee.org\/document\/10455662\/ Accessed 15 May 2024","DOI":"10.1109\/CICT59886.2023.10455662"},{"key":"690_CR5","doi-asserted-by":"publisher","first-page":"8458","DOI":"10.1109\/JSTARS.2021.3104230","volume":"14","author":"Z Zhang","year":"2021","unstructured":"Z. Zhang, L. Zhang, Y. Wang, P. Feng, R. He, ShipRSImageNet: A Large-Scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images. IEEE J. Sel. Topics. Appl. Earth Obs. Remote Sens. 14, 8458\u20138472 (2021). (Accessed 2024\u201304-15)","journal-title":"IEEE J. Sel. Topics. Appl. Earth Obs. Remote Sens."},{"key":"690_CR6","doi-asserted-by":"publisher","first-page":"2145","DOI":"10.1109\/JSTARS.2020.2987827","volume":"13","author":"Y Zhuang","year":"2020","unstructured":"Y. Zhuang, L. Li, H. Chen, Small Sample Set Inshore Ship Detection From VHR Optical Remote Sensing Images Based on Structured Sparse Representation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2145\u20132160 (2020). (Accessed 2024\u201306-20)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"690_CR7","doi-asserted-by":"publisher","first-page":"11058","DOI":"10.1109\/JSTARS.2021.3123080","volume":"14","author":"P Qin","year":"2021","unstructured":"P. Qin, Y. Cai, J. Liu, P. Fan, M. Sun, Multilayer Feature Extraction Network for Military Ship Detection From High-Resolution Optical Remote Sensing Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 11058\u201311069 (2021). (Accessed 2024\u201306-20)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"1","key":"690_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TCSVT.2021.3067449.","volume":"32","author":"C Yan","year":"2022","unstructured":"C. Yan, Y. Hao, L. Li, J. Yin, A. Liu, Z. Mao, Z. Chen, X. Gao, Task-Adaptive Attention for Image Captioning. IEEE Trans. Circuits Syst. Video Technol. 32(1), 43\u201351 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3067449. (Accessed 2024-12-21)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"3s","key":"690_CR9","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1145\/3468872.","volume":"17","author":"C Yan","year":"2021","unstructured":"C. Yan, T. Teng, Y. Liu, Y. Zhang, H. Wang, X. Ji, Precise No-Reference Image Quality Evaluation Based on Distortion Identification. ACM Trans. Multimedia Comput. Commun. Appl. 17(3s), 110\u2013111021 (2021). https:\/\/doi.org\/10.1145\/3468872. (Accessed 2024-12-21)","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"4","key":"690_CR10","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1145\/3404374.","volume":"16","author":"C Yan","year":"2020","unstructured":"C. Yan, Z. Li, Y. Zhang, Y. Liu, X. Ji, Y. Zhang, Depth Image Denoising Using Nuclear Norm and Learning Graph Model. ACM Trans. Multimedia Comput. Commun. Appl. 16(4), 122\u2013112217 (2020). https:\/\/doi.org\/10.1145\/3404374. (Accessed 2024-12-21)","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"4","key":"690_CR11","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TPAMI.2020.2975798.","volume":"43","author":"C Yan","year":"2021","unstructured":"C. Yan, B. Gong, Y. Wei, Y. Gao, Deep Multi-View Enhancement Hashing for Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1445\u20131451 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2975798. (Accessed 2024-12-21)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"690_CR12","doi-asserted-by":"publisher","first-page":"134","DOI":"10.3390\/s24010134","volume":"24","author":"J Zhou","year":"2024","unstructured":"J. Zhou, T. Su, K. Li, J. Dai, Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5. Sensors 24(1), 134 (2024). (Accessed 2024\u201304-02)","journal-title":"Sensors"},{"key":"690_CR13","first-page":"6","volume":"38","author":"Y Chenggang","year":"2022","unstructured":"Y. Chenggang, S. Yaoqi, Z. Hao, Z. Chenwei, Z. Zunjie, Z. Bolun, Z. Xiaofei, Review of omnimedia content quality evaluation. J. Signal Process. 38, 6 (2022)","journal-title":"J. Signal Process."},{"key":"690_CR14","doi-asserted-by":"crossref","unstructured":"R. Girshick, Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2015","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"690_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). (Accessed 2024\u201306-20)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"690_CR16","doi-asserted-by":"crossref","unstructured":"W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A.C. Berg, SSD: Single Shot MultiBox Detector, in Computer Vision - ECCV 2016. ed. by B. Leibe, J. Matas, N. Sebe, M. Welling (Springer, Cham, 2016), pp.21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"690_CR17","doi-asserted-by":"crossref","unstructured":"W. Xiong, Z. Xiong, Y. Cui, An explainable attention network for fine-grained ship classification using remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 60, 1\u201314 (2022). Accessed 15 May 2024","DOI":"10.1109\/TGRS.2022.3162195"},{"issue":"1","key":"690_CR18","doi-asserted-by":"publisher","first-page":"130","DOI":"10.3390\/rs16010130","volume":"16","author":"S Ma","year":"2023","unstructured":"S. Ma, W. Wang, Z. Pan, Y. Hu, G. Zhou, Q. Wang, A., Recognition Model Incorporating Geometric Relationships of Ship Components. Remote Sens. 16(1), 130 (2023)","journal-title":"Remote Sens."},{"key":"690_CR19","doi-asserted-by":"crossref","unstructured":"H., A., Jidesh, P.: Detection and Classification of Ships using a self-attention Residual Network. In: 2022 IEEE 6th Conference on Information and Communication Technology (CICT), pp. 1\u20136. IEEE, Gwalior, India (2022). https:\/\/ieeexplore.ieee.org\/document\/9997853\/ Accessed 15 May 2024","DOI":"10.1109\/CICT56698.2022.9997853"},{"key":"690_CR20","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1109\/JSTARS.2023.3241969","volume":"16","author":"B Guo","year":"2023","unstructured":"B. Guo, R. Zhang, H. Guo, W. Yang, H. Yu, P. Zhang, T. Zou, Fine-Grained Ship Detection in High-Resolution Satellite Images With Shape-Aware Feature Learning. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 16, 1914\u20131926 (2023). (Accessed 2024\u201305-15)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"690_CR21","doi-asserted-by":"crossref","unstructured":"B. Van Ricardo Zalukhu, A.W. Wijayanto, M.I. Habibie, Marine Vessels Detection on Very High-Resolution Remote Sensing Optical Satellites using Object-Based Deep Learning. In: 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 149\u2013154. IEEE, Solo, Indonesia (2022). https:\/\/ieeexplore.ieee.org\/document\/9994340\/ Accessed 15 May 2024","DOI":"10.1109\/COMNETSAT56033.2022.9994340"},{"key":"690_CR22","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017). https:\/\/openaccess.thecvf.com\/content_iccv_2017\/html\/He_Mask_R-CNN_ICCV_2017_paper.html Accessed 20 Jun 2024","DOI":"10.1109\/ICCV.2017.322"},{"key":"690_CR23","doi-asserted-by":"publisher","first-page":"49422","DOI":"10.1109\/ACCESS.2022.3173732","volume":"10","author":"Z Zhang","year":"2022","unstructured":"Z. Zhang, L. Zhang, Y. Wang, P. Feng, B. Sun, Bidirectional Parallel Feature Pyramid Network for Object Detection. IEEE Access 10, 49422\u201349432 (2022). (Accessed 2024\u201305-15)","journal-title":"IEEE Access"},{"key":"690_CR24","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788. IEEE, Las Vegas, NV, USA (2016). http:\/\/ieeexplore.ieee.org\/document\/7780460\/ Accessed 2024\u201306-20","DOI":"10.1109\/CVPR.2016.91"},{"key":"690_CR25","doi-asserted-by":"crossref","unstructured":"K. Jiang, Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, J. Jiang, Multi-scale progressive fusion network for single image deraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"690_CR26","unstructured":"A.M. Rekavandi, L. Xu, F. Boussaid, A.-K. Seghouane, S. Hoefs, M. Bennamoun, A Guide to Image and Video Based Small Object Detection Using Deep Learning : Case Study of Maritime Surveillance. arXiv (2022). http:\/\/arxiv.org\/abs\/2207.12926 Accessed 1 Apr 2024"},{"key":"690_CR27","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature Pyramid Networks for Object Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944. IEEE, Honolulu, HI (2017). http:\/\/ieeexplore.ieee.org\/document\/8099589\/ Accessed 20 Jun 2024","DOI":"10.1109\/CVPR.2017.106"},{"key":"690_CR28","unstructured":"S. Liu, L. Qi, H. Qin, J. Shi, J. Jia, Path Aggregation Network for Instance Segmentation. arXiv (2018). http:\/\/arxiv.org\/abs\/1803.01534 Accessed 23 Jun 2024"},{"key":"690_CR29","doi-asserted-by":"publisher","unstructured":"al., G.J.: ultralytics\/yolov5: V6.0 - YOLOv5n \u2019Nano\u2019 Models, Roboflow Integration, TensorFlow Export, OpenCV DNN Support. https:\/\/doi.org\/10.5281\/zenodo.5563715.","DOI":"10.5281\/zenodo.5563715."},{"key":"690_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3107352","volume":"60","author":"Y Xiao","year":"2022","unstructured":"Y. Xiao, X. Su, Q. Yuan, D. Liu, H. Shen, L. Zhang, Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201319 (2022). https:\/\/doi.org\/10.1109\/TGRS.2021.3107352. (Accessed 2024\u201312-21)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"690_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3291822","volume":"61","author":"Y Xiao","year":"2023","unstructured":"Y. Xiao, Q. Yuan, Q. Zhang, L. Zhang, Deep Blind Super-Resolution for Satellite Video. IEEE Trans. Geosci. Remote Sens. 61, 1\u201316 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3291822. (Accessed 2024\u201312-21)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"690_CR32","doi-asserted-by":"publisher","first-page":"2264","DOI":"10.1109\/TPAMI.2020.3042298.","volume":"44","author":"P Yi","year":"2022","unstructured":"P. Yi, Z. Wang, K. Jiang, J. Jiang, T. Lu, J. Ma, A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2264\u20132280 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3042298. (Accessed 2024-12-21)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"690_CR33","doi-asserted-by":"crossref","unstructured":"J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, Deformable Convolutional Networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017). https:\/\/openaccess.thecvf.com\/content_iccv_2017\/html\/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.html Accessed 20 Jun 2024","DOI":"10.1109\/ICCV.2017.89"},{"key":"690_CR34","unstructured":"X. Zhu, H. Hu, S. Lin, J. Dai, Deformable ConvNets v2: More Deformable, Better Results. arXiv (2018). http:\/\/arxiv.org\/abs\/1811.11168 Accessed 20 Jun 2024"}],"container-title":["Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-026-00690-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13640-026-00690-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-026-00690-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:19:47Z","timestamp":1776820787000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13640-026-00690-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["690"],"URL":"https:\/\/doi.org\/10.1186\/s13640-026-00690-x","relation":{},"ISSN":["3091-454X"],"issn-type":[{"value":"3091-454X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"18 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","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":"Competing interests"}}],"article-number":"7"}}