{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T17:22:13Z","timestamp":1792257733252,"version":"4.4.0"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aims to develop an enhanced YOLO algorithm to improve the ship detection performance of synthetic aperture radar (SAR) in complex marine environments. Current SAR ship detection methods face numerous challenges in complex sea conditions, including environmental interference, false detection, and multi-scale changes in detection targets. To address these issues, this study adopts a technical solution that combines multi-level feature fusion with a dynamic detection mechanism. First, a cross-stage partial dynamic channel transformer module (CSP_DTB) was designed, which combines the transformer architecture with a convolutional neural network to replace the last two C3k2 layers in the YOLOv11n main network, thereby enhancing the model\u2019s feature extraction capabilities. Second, a general dynamic feature pyramid network (RepGFPN) was introduced to reconstruct the neck network architecture, enabling more efficient multi-scale feature fusion and information propagation. Additionally, a lightweight dynamic decoupled dual-alignment head (DYDDH) was constructed to enhance the collaborative performance of localization and classification tasks through task-specific feature decoupling. Experimental results show that the proposed DRGD-YOLO algorithm achieves significant performance improvements. On the HRSID dataset, the algorithm achieves an average precision (mAP50) of 93.1% at an IoU threshold of 0.50 and an mAP50\u201395 of 69.2% over the IoU threshold range of 0.50\u20130.95. Compared to the baseline YOLOv11n algorithm, the proposed method improves mAP50 and mAP50\u201395 by 3.3% and 4.6%, respectively. The proposed DRGD-YOLO algorithm not only significantly improves the accuracy and robustness of synthetic aperture radar (SAR) ship detection but also demonstrates broad application potential in fields such as maritime surveillance, fisheries management, and maritime safety monitoring, providing technical support for the development of intelligent marine monitoring technology.<\/jats:p>","DOI":"10.3390\/s25165194","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:19:02Z","timestamp":1755789542000},"page":"5194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Dynamic Multi-Scale Feature Fusion Network for Enhanced SAR Ship Detection"],"prefix":"10.3390","volume":"25","author":[{"given":"Rui","family":"Cao","sequence":"first","affiliation":[{"name":"Navigation and Ship Engineering College, Dalian Ocean University, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghua","family":"Sui","sequence":"additional","affiliation":[{"name":"Navigation and Ship Engineering College, Dalian Ocean University, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MGRS.2022.3218801","article-title":"Sparse Synthetic Aperture Radar Imaging from Compressed Sensing and Machine Learning: Theories, Applications, and Trends","volume":"10","author":"Xu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4857","DOI":"10.1109\/TCSVT.2025.3526449","article-title":"Visible-infrared person re-identification with real-world label noise","volume":"35","author":"Zhang","year":"2025","journal-title":"IEEE Trans. IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15019","DOI":"10.1109\/JSTARS.2025.3574184","article-title":"Deformable Feature Fusion and Accurate Anchors Prediction for Lightweight SAR Ship Detector Based on Dynamic Hierarchical Model Pruning","volume":"18","author":"Guo","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1109\/TMM.2024.3521699","article-title":"FasterSal: Robust and Real-Time Single-Stream Architecture for RGB-DSalient Object Detection","volume":"27","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1007\/s11760-023-02962-9","article-title":"SAR ship detection network based on global context and multi-scale feature enhancement","volume":"18","author":"Zhou","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s11760-018-1408-4","article-title":"A new CFAR algorithm based on variable window for ship target detection in SAR images","volume":"13","author":"Chen","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"131127","DOI":"10.1016\/j.neucom.2025.131127","article-title":"RDFCNet: RGB-guided depth feature calibration network for RGB-D Salient Object Detection","volume":"652","author":"Wei","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox Detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Proceedings, Part I 14, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/LSP.2025.3548934","article-title":"Efficient Feature Focus Enhanced Network for Small and Dense Object Detection in SAR Images","volume":"32","author":"Li","year":"2025","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tang, H., Gao, S., Li, S., Wang, P., Liu, J., Wang, S., and Qian, J. (2024). A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7. Remote Sens., 16.","DOI":"10.3390\/rs16030486"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7029","DOI":"10.1109\/JSTARS.2024.3376558","article-title":"DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments","volume":"17","author":"Tang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, K., Zhang, Z., Chen, Z., and Liu, G. (2024). Object-Enhanced YOLO Networks for Synthetic Aperture Radar Ship Detection. Remote Sens., 16.","DOI":"10.3390\/rs16061001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118928","DOI":"10.1016\/j.eswa.2022.118928","article-title":"M2YOLOF: Based on effective receptive fields and multiple-in-single-out encoder for object detection","volume":"213","author":"Wang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shi, D. (2024, January 17\u201321). TransNeXt: Robust Foveal Visual Perception for Vision Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01683"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_23","unstructured":"Xu, X., Jiang, Y., Chen, W., Huang, Y., Zhang, Y., and Sun, X. (2022). DAMO-YOLO: A Report on Real-Time Object Detection Design. arXiv."},{"key":"ref_24","unstructured":"Tan, Z., Wang, J., Sun, X., Lin, M., and Li, H. (2021, January 4). Giraffedet: A heavy-neck paradigm for object detection. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 11\u201317). Tood: Task-aligned one-stage object detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_26","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018, January 8\u201314). Group normalization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_28","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable convnets v2: More deformable, better results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., and Su, H. (2021). SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13183690"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., Pan, D., Li, J., Su, H., and Zhou, Y. (2020). LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12182997"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s00530-024-01617-0","article-title":"RG-YOLO: Multi-scale feature learning for underwater target detection","volume":"31","author":"Zheng","year":"2025","journal-title":"Multimed. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., and Peng, Y. (2024). Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.107917"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fu, Z., Ling, J., Yuan, X., Li, H., Li, H., and Li, Y. (2024). Yolov8n-FADS: A Study for Enhancing Miners\u2019 Helmet Detection Accuracy in Complex Underground Environments. Sensors, 24.","DOI":"10.3390\/s24123767"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tang, H., and Jiang, Y. (2024, January 12\u201314). An Improved YOLOv8n Algorithm for Object Detection with CARAFE, MultiSEAMHead, and TripleAttention Mechanisms. Proceedings of the 2024 7th International Conference on Computer Information Science and Application Technology (CISAT), Hangzhou, China.","DOI":"10.1109\/CISAT62382.2024.10695221"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yi, X., Chen, H., Wu, P., Wang, G., Mo, L., Wu, B., Yi, Y., Fu, X., and Qian, P. (2024). Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head. Agronomy, 14.","DOI":"10.3390\/agronomy14061285"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/5194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:40Z","timestamp":1760034760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/5194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["s25165194"],"URL":"https:\/\/doi.org\/10.3390\/s25165194","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]}}}