{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:36:48Z","timestamp":1772822208429,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology on Near-Surface Detection Laboratory","award":["TCGZ2018A001"],"award-info":[{"award-number":["TCGZ2018A001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.<\/jats:p>","DOI":"10.3390\/s21238146","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T02:48:13Z","timestamp":1638845293000},"page":"8146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8801-4209","authenticated-orcid":false,"given":"Haozhen","family":"Zhu","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6288-8541","authenticated-orcid":false,"given":"Yao","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]},{"given":"Huihui","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]},{"given":"Chen","family":"Jing","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]},{"given":"Yingjiao","family":"Rong","sequence":"additional","affiliation":[{"name":"Science and Technology on Near Surface Detection Laboratory, Wuxi 214035, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9038-0278","authenticated-orcid":false,"given":"Changyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TGRS.2010.2071879","article-title":"Ship surveillance with TerraSAR-X","volume":"49","author":"Brusch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2015.2437353","article-title":"Tandem-L: A highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth\u2019s surface","volume":"3","author":"Moreira","year":"2015","journal-title":"Geosci. Remote Sens. Mag. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TGRS.2016.2606481","article-title":"New hierarchical saliency filtering for fast ship detection in high-resolution SAR images","volume":"55","author":"Shigang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/LGRS.2018.2838263","article-title":"Superpixel-level CFAR detectors for ship detection in SAR imagery","volume":"15","author":"Pappas","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1109\/TGRS.2018.2876603","article-title":"Ship detection in SAR images based on Maxtree representation and graph signal processing","volume":"57","author":"Salembier","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/TGRS.2009.2037432","article-title":"The Terrasar X satellite","volume":"48","author":"Pitz","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"811","article-title":"Multilayer CFAR detection of ship targets in very high resolution SAR images","volume":"12","author":"Hou","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","unstructured":"Lu, G., Peng, L., and Wang, L. (2016, January 10\u201311). Rotation sliding window of the hog feature in remote sensing images for ship detection. Proceedings of the 9th International Symposium on Computational Intelligence & Design, Hangzhou, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/LGRS.2016.2631638","article-title":"Synthetic aperture radar ship detection using Haar-like features","volume":"14","author":"Schwegmann","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","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_11","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_12","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-FCN: Object detection via region-based fully convolutional networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., and Lin, S. (November, January 27). RepPoints: Point set representation for object detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00975"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the 2016 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_15","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017, January 13\u201314). Ship detection in SAR images based on an improved faster R-CNN. Proceedings of the SAR in Big Data Era: Models, Methods & Applications Conference, Beijing, China.","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pan, Z., Yang, R., and Zhang, A.Z. (2020). MSR2N: Multi-stage rotational region based network for arbitrary-oriented ship detection in SAR images. Sensors, 20.","DOI":"10.3390\/s20082340"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8893182","DOI":"10.1155\/2021\/8893182","article-title":"End-to-end ship detection in SAR images for complex scenes based on deep CNNs","volume":"2021","author":"Chen","year":"2021","journal-title":"J. Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8941","DOI":"10.1109\/JSTARS.2021.3109002","article-title":"Boosting ship detection in SAR images with complementary pretraining techniques","volume":"14","author":"Bao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, J.M., Zhi, X.Y., Shi, T.J., Zhang, W., Cui, Y., and Zhao, S.G. (2021). PAG-YOLO: A portable attention-guided YOLO network for small ship detection. Remote Sens., 13.","DOI":"10.3390\/rs13163059"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jiang, Y.H., Li, W.W., and Liu, L. (2021). R-CenterNet plus: Anchor-free detector for ship detection in SAR images. Sensors, 21.","DOI":"10.3390\/s21175693"},{"key":"ref_23","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":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Zhan, X., and Kumar, D. (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_25","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_27","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wei, S., Su, H., Ming, J., Wang, C., Yan, M., Kumar, D., Shi, J., and Zhang, X. (2020). Precise and robust ship detection for high-resolution SAR imagery based on HR-SDNet. Remote Sens., 12.","DOI":"10.3390\/rs12010167"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guo, W., Li, W., Gong, W., and Cui, J. (2020). Extended feature pyramid network with adaptive scale training strategy and anchors for object detection in aerial images. Remote Sens., 12.","DOI":"10.3390\/rs12050784"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/JSTARS.2021.3049851","article-title":"A novel CNN-based detector for ship detection based on rotatable bounding box in SAR Images","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H., Wu, Y.H., Chen, P.Y., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2020). DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution. arXiv.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201327). Soft-NMS\u2014Improving object detection with one line of code. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_38","first-page":"318","article-title":"Focal loss for dense object detection","volume":"41","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra R-CNN: Towards balanced learning for object detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_40","unstructured":"Zhou, X., Wang, D., and Krhenb\u00fchl, P. (2019). Objects as Points. arXiv."},{"key":"ref_41","unstructured":"(2020, July 15). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/8146\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:42:05Z","timestamp":1760168525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/8146"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,6]]},"references-count":41,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21238146"],"URL":"https:\/\/doi.org\/10.3390\/s21238146","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,6]]}}}