{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:08:25Z","timestamp":1764785305887,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271367"],"award-info":[{"award-number":["42271367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image interpretation, it often prioritizes recognition over computational efficiency and underutilizes SAR image prior information. Therefore, this paper proposes YOLOv5s-based ship detection in SAR images. Firstly, for comprehensive detection enhancement, we employ the lightweight YOLOv5s model as the baseline. Secondly, we introduce a sub-net into YOLOv5s, learning traditional features to augment ship feature representation of Constant False Alarm Rate (CFAR). Additionally, we attempt to incorporate frequency-domain information into the channel attention mechanism to further improve detection. Extensive experiments on the Ship Recognition and Detection Dataset (SRSDDv1.0) in complex SAR scenarios confirm our method\u2019s 68.04% detection accuracy and 60.25% recall, with a compact 18.51 M model size. Our network surpasses peers in mAP, F1 score, model size, and inference speed, displaying robustness across diverse complex scenes.<\/jats:p>","DOI":"10.3390\/rs16050733","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T07:50:26Z","timestamp":1708415426000},"page":"733","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s"],"prefix":"10.3390","volume":"16","author":[{"given":"Xue","family":"Wen","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Shaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Jianmei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Tangjun","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4950-8011","authenticated-orcid":false,"given":"Yan","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Shi, J., and Wei, S. (2019). Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection. Remote Sens., 11.","DOI":"10.3390\/rs11212483"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2019). High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11101206"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wu, R., Xu, K., Wang, J., and Sun, W. (2019). R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11060631"},{"key":"ref_4","first-page":"5206613","article-title":"Ship Detection in SAR Images by Aggregating Densities of Fisher Vectors: Extension to a Global Perspective","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/07038992.2001.10854896","article-title":"Automatic Detection of Ships in RADARSAT-1 SAR Imagery","volume":"27","author":"Wackerman","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images","volume":"47","author":"Gao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., and Ke, X. (2021). Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection. Remote Sens., 13.","DOI":"10.3390\/rs13142771"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhang, X., Zhang, T., Xu, X., and Zeng, T. (2022). RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification. Remote Sens., 14.","DOI":"10.3390\/rs14143345"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"50693","DOI":"10.1109\/ACCESS.2018.2869289","article-title":"A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection from SAR Images","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"Joseph, R., and Ali, F. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., and Li, X. (2021, January 11\u201317). FcaNet: Frequency Channel Attention Networks. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Adil, M., Buono, A., Nunziata, F., Ferrentino, E., Velotto, D., and Migliaccio, M. (2022). On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship. Remote Sens., 14.","DOI":"10.3390\/rs14225813"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1109\/JSTARS.2013.2247741","article-title":"A Notch Filter for Ship Detection with Polarimetric SAR Data","volume":"6","author":"Marino","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/JOE.2011.2109491","article-title":"Generalized-K (GK)-Based Observation of Metallic Objects at Sea in Full-Resolution Synthetic Aperture Radar (SAR) Data: A Multipolarization Study","volume":"36","author":"Ferrara","year":"2011","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, C., Tu, M., Xiong, D., Tu, F., and Liao, M. (2018). Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020072"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/LGRS.2015.2412174","article-title":"A Bilateral CFAR Algorithm for Ship Detection in SAR Images","volume":"12","author":"Leng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_19","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_20","first-page":"1","article-title":"R-fcn: Object detection via region-based fully convolutional networks","volume":"29","author":"Dai","year":"2016","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Computer Vision\u2014ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer International Publishing. Proceedings, Part I. European Conference on Computer Vision.","DOI":"10.1007\/978-3-319-46478-7"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_24","first-page":"4004905","article-title":"Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/LGRS.2020.2993899","article-title":"ShipDeNet-20: An only 20 convolution layers and< 1-MB lightweight SAR ship detector","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/LGRS.2016.2565705","article-title":"Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2021). Injection of Traditional Hand-Crafted Features into Modern CNN-Based Models for SAR Ship Classification: What, Why, Where, and How. Remote Sens., 13.","DOI":"10.3390\/rs13112091"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108365","DOI":"10.1016\/j.patcog.2021.108365","article-title":"A polarization fusion network with geometric feature embedding for SAR ship classification","volume":"123","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_29","first-page":"5215514","article-title":"Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10070","DOI":"10.1109\/TGRS.2019.2931308","article-title":"Multi-Scale Rotation-Invariant Haar-Like Feature Integrated CNN-Based Ship Detection Algorithm of Multiple-Target Environment in SAR Imagery","volume":"57","author":"Ai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"1","article-title":"HOG-ShipCLSNet: A Novel Deep Learning Network with HOG Feature Fusion for SAR Ship Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8048","DOI":"10.1109\/JSTARS.2021.3102989","article-title":"Multitask Learning for Ship Detection From Synthetic Aperture Radar Images","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"215904","DOI":"10.1109\/ACCESS.2020.3041372","article-title":"A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/T-C.1974.223784","article-title":"Discrete Cosine Transform","volume":"C-23","author":"Ahmed","year":"1974","journal-title":"IEEE Trans. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","article-title":"Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation","volume":"52","author":"Zheng","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lei, S., Lu, D., Qiu, X., and Ding, C. (2021). SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset. Remote Sens., 13.","DOI":"10.3390\/rs13245104"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201317). Oriented R-CNN for Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019). 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea, 27 October\u20132 November 2019, IEEE."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Computer Vision\u2014ECCV 2018. Part XIV, Proceedings of the 15th European Conference, Munich, Germany, 8\u201314 September 2018, Springer.","DOI":"10.1007\/978-3-030-01264-9"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 3\u20138). Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_43","first-page":"3163","article-title":"R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/733\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:02:38Z","timestamp":1760104958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050733"],"URL":"https:\/\/doi.org\/10.3390\/rs16050733","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,2,20]]}}}