{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:57:58Z","timestamp":1772042278740,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nation Natural Science Foundation of China","award":["62231026"],"award-info":[{"award-number":["62231026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>At present, the majority of deep learning-based ship object detection algorithms concentrate predominantly on enhancing recognition accuracy, often overlooking the complexity of the algorithm. These complex algorithms demand significant computational resources, making them unsuitable for deployment on resource-constrained edge devices, such as airborne and spaceborne platforms, thereby limiting their practicality. With the purpose of alleviating this problem, a lightweight and high-accuracy synthetic aperture radar (SAR) ship image detection network (LHSDNet) is proposed. Initially, GhostHGNetV2 was utilized as the feature extraction network, and the calculation amount of the network was reduced by GhostConv. Next, a lightweight feature fusion network was designed to combine shallow and deep features through lightweight convolutions, effectively preserving more information while minimizing computational requirements. Lastly, the feature extraction module was integrated through parameter sharing, and the detection head was lightweight to save computing resources further. The results from our experiments demonstrate that the proposed LHSDNet model increases mAP50 by 0.7% in comparison to the baseline model. Additionally, it illustrates a pronounced decrease in parameter count, computational demand, and model file size by 48.33%, 51.85%, and 41.26%, respectively, when contrasted with the baseline model. LHSDNet achieves a balance between precision and computing resources, rendering it more appropriate for edge device implementation.<\/jats:p>","DOI":"10.3390\/rs16234527","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:04:04Z","timestamp":1733198644000},"page":"4527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["LHSDNet: A Lightweight and High-Accuracy SAR Ship Object Detection Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Dahai","family":"Dai","sequence":"first","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Dataspace, Hefei 230000, China"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Dataspace, Hefei 230000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6466-1276","authenticated-orcid":false,"given":"Penghui","family":"Ji","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pelich, R., Chini, M., Hostache, R., Matgen, P., Lopez-Martinez, C., Nuevo, M., and Eiden, G. (2019). Large-scale automatic vessel monitoring based on dual-polarization sentinel-1 and AIS data. Remote Sens., 11.","DOI":"10.3390\/rs11091078"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5284","DOI":"10.1109\/JSTARS.2020.3021390","article-title":"A deep cross-modality hashing network for SAR and optical remote sensing images retrieval","volume":"13","author":"Xiong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, S., Zhu, M., He, Y., Zheng, Z., Zhou, F., and Zhou, G. (October, January 26). Ship detection with SAR based on YOLO. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324538"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, C., Fu, X., Dong, J., Feng, C., and Chang, H. (2023). LPDNet: A lightweight network for SAR ship detection based on multi-level Laplacian denoising. Sensors, 23.","DOI":"10.3390\/s23136084"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Santos, N.P., Rodrigues, V.B., Pinto, A.B., and Damas, B. (2023, January 26\u201327). Automatic detection of civilian and military personnel in reconnaissance missions using a UAV. Proceedings of the 2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Tomar, Portugal.","DOI":"10.1109\/ICARSC58346.2023.10129575"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108515","DOI":"10.1016\/j.compag.2023.108515","article-title":"Research on CBF-YOLO detection model for common soybean pests in complex environment","volume":"216","author":"Zhu","year":"2024","journal-title":"Comput. Electron. Agriculture"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, X., Xia, Y., Zhang, W., Zheng, C., and Zhang, Z. (2023). YOLO-ViT-Based method for unmanned aerial vehicle infrared vehicle target detection. Remote Sens., 15.","DOI":"10.3390\/rs15153778"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12559-022-10061-z","article-title":"Surface defect detection algorithm based on feature-enhanced YOLO","volume":"15","author":"Xie","year":"2023","journal-title":"Cogn. Computation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extraction"},{"key":"ref_11","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, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_14","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":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Kang, F., and Wang, Y. (2022). An improved apple object detection method based on lightweight YOLOv4 in complex backgrounds. Remote Sens., 14.","DOI":"10.3390\/rs14174150"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"Attention receptive pyramid network for ship detection in SAR Images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107787","DOI":"10.1016\/j.patcog.2020.107787","article-title":"A CenterNet++ model for ship detection in SAR images","volume":"112","author":"Guo","year":"2021","journal-title":"Pattern Recognition"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"24554","DOI":"10.1109\/ACCESS.2024.3365777","article-title":"MSFA-YOLO: A multi-scale SAR ship detection algorithm based on fused attention","volume":"12","author":"Tang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Zhou, Y., Gong, Y., and Yao, Z. (2024, January 20\u201322). Research on a SAR image ship detection algorithm based on feature refinement. Proceedings of the IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10558","DOI":"10.1109\/TPAMI.2024.3447085","article-title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","volume":"46","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intelligence"},{"key":"ref_22","unstructured":"Howard, A.G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_24","first-page":"1","article-title":"A high-effective implementation of ship detector for SAR images","volume":"19","author":"Gao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Letters"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108414","DOI":"10.1109\/ACCESS.2024.3438797","article-title":"GS-YOLO: A lightweight SAR ship detection model based on enhanced GhostNetV2 and SE attention mechanism","volume":"12","author":"Lv","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/TAES.2023.3344396","article-title":"A lightweight convolutional neural network for ship target detection in SAR images","volume":"60","author":"Hao","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Systems"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"37030","DOI":"10.1109\/ACCESS.2024.3373893","article-title":"SHIP-YOLO: A Lightweight synthetic aperture radar ship detection model based on YOLOv8n algorithm","volume":"12","author":"Luo","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.1007\/s11760-024-03258-2","article-title":"EMO-YOLO: A lightweight ship detection model for SAR images based on YOLOv5s. Signal","volume":"18","author":"Pan","year":"2024","journal-title":"Image Video Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13981","DOI":"10.1109\/JSTARS.2024.3435989","article-title":"A hierarchical feature fusion and attention network for automatic ship detection from SAR images","volume":"17","author":"Mao","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Han, Q., Han, X., Niu, L., and Fan, Y. (2024, January 18\u201320). Light-YOLOv7: Lightweight ship object detection algorithm based on CA and EMA. Proceedings of the 9th International Conference on Automation, Control and Robotics Engineering (CACRE), Jeju Island, Republic of Korea.","DOI":"10.1109\/CACRE62362.2024.10635091"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TIP.2022.3231126","article-title":"Lightweight deep neural networks for ship target detection in SAR imagery","volume":"32","author":"Wang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/JSTARS.2020.3041783","article-title":"Learning slimming SAR ship object detector through network pruning and knowledge distillation","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., and Chen, J. (2024, January 16\u201322). DETRs beat YOLOs on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). RepVGG: Making VGG-style ConvNets great again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.H., He, H., Zhuo, W., Wen, S., Lee, C.H., and Chan, S.H.G. (2023, January 17\u201324). Run, don\u2019t walk: Chasing higher FLOPS for faster neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sens., 11.","DOI":"10.3390\/rs11070765"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., and Howard, A. (2024). MobileNetV4-Universal models for the mobile ecosystem. arXiv.","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019). FCOS: Fully convolutional one-stage object detection. arXiv.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_40","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., and Wei, X. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:45:42Z","timestamp":1760114742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":40,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234527"],"URL":"https:\/\/doi.org\/10.3390\/rs16234527","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]}}}