{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:02:23Z","timestamp":1771606943837,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61801142 No.62071136 No.61971153 No.62002083"],"award-info":[{"award-number":["No.61801142 No.62071136 No.61971153 No.62002083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["LBH-Q20085 LBH-Z20051"],"award-info":[{"award-number":["LBH-Q20085 LBH-Z20051"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["3072021CF0814 3072021CF0807 3072021CF0808."],"award-info":[{"award-number":["3072021CF0814 3072021CF0807 3072021CF0808."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels.<\/jats:p>","DOI":"10.3390\/rs14030442","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T22:47:32Z","timestamp":1642546052000},"page":"442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9601-536X","authenticated-orcid":false,"given":"Nan","family":"Su","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Jiayue","family":"He","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0751-7726","authenticated-orcid":false,"given":"Yiming","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Chunhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Xiangwei","family":"Xing","sequence":"additional","affiliation":[{"name":"Beijing Remote Sensing Information Institute, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, M., Xu, P., and Guo, Z. (2017, January 19\u201321). SAR Ship Detection Using Sea-Land Segmentation-Based Convolutional Neural Network. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958806"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Lin, Z. (2017). Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, L., Wu, H., Zhong, Z.-C., Zheng, L., Deng, Q., and Hu, H. (2021). TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13132558"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, J., and Zhan, R. (2020). R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12122031"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wei, S., Su, H., Ming, J., Wang, C., Yan, M., Kumar, D.P., 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_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050531"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, Z., Leng, X., Lei, Y., Xiong, B., Ji, K., and Kuang, G. (2021). BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images. Remote Sens., 13.","DOI":"10.3390\/rs13214209"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, X., Wang, N., and Gao, X. (2021). A Robust One-Stage Detector for Multiscale Ship Detection with Complex Background in Massive SAR Images. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3128060"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/2150704X.2020.1837988","article-title":"SSS-YOLO: Towards More Accurate Detection for Small Ships in SAR Image","volume":"12","author":"Wang","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khanna, A., Gupta, D., P\u00f3lkowski, Z., Bhattacharyya, S., and Castillo, O. (2021). YOLOv3 Remote Sensing SAR Ship Image Detection. Data Analytics and Management, Springer.","DOI":"10.1007\/978-981-15-8335-3"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhuge, Y., Claramunt, C., and Men, S. (2021). N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction. Remote Sens., 13.","DOI":"10.3390\/rs13050871"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ke, X., Zhang, X., Zhang, T., Shi, J., and Wei, S. (2021, January 11\u201316). SAR Ship Detection Based on an Improved Faster R-CNN Using Deformable Convolution. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554697"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, T., Lei, P., and Bai, X. (2019). A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11060620"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images","volume":"16","author":"Lin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/TGRS.2020.3005151","article-title":"An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images","volume":"59","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","article-title":"A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/2150704X.2021.1987574","article-title":"A Regional Attention-Based Detector for SAR Ship Detection","volume":"13","author":"Qi","year":"2021","journal-title":"Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gao, F., He, Y., Wang, J., Hussain, A., and Zhou, H. (2020). Anchor-Free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12162619"},{"key":"ref_25","first-page":"1","article-title":"A Lightweight Faster R-CNN for Ship Detection in SAR Images","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","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_27","first-page":"1","article-title":"A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1109\/TGRS.2018.2889353","article-title":"Learning Deep Ship Detector in SAR Images From Scratch","volume":"57","author":"Deng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","first-page":"1","article-title":"Enriching SAR Ship Detection via Multistage Domain Alignment","volume":"19","author":"Jeong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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_31","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_32","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 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China.","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., and Li, X. (2020). FcaNet: Frequency Channel Attention Networks. arXiv.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_37","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014). Network In Network. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","article-title":"Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images","volume":"57","author":"Cui","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Guo, C., Fan, B., Zhang, Q., Xiang, S., and Pan, C. (2020, January 14\u201319). AugFPN: Improving Multi-Scale Feature Learning for Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (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_42","doi-asserted-by":"crossref","unstructured":"Yu, X., Gong, Y., Jiang, N., Ye, Q., and Han, Z. (2020, January 1\u20135). Scale Match for Tiny Person Detection. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093394"},{"key":"ref_43","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (2017). Learning non-maximum suppression. arXiv.","DOI":"10.1109\/CVPR.2017.685"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Girshick, R.B., and Doll\u00e1r, P. (2019, January 27\u201328). Rethinking ImageNet Pre-Training. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00502"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C. (2014). Microsoft COCO: Common objects in context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_47","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., and He, K. (2018, January 18\u201323). Non-Local Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_49","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_50","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., and Fu, Y.R. (2020, January 14\u201319). Rethinking Classification and Localization for Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q., and Yan, J. (2019, January 15\u201320). Grid R-CNN. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00754"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Yang, S., Loy, C.C., and Lin, D. (2019, January 15\u201320). Region Proposal by Guided Anchoring. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00308"},{"key":"ref_56","unstructured":"Zhang, X., Wan, F., Liu, C., Ji, R., and Ye, Q. (2019). FreeAnchor: Learning to Match Anchors for Visual Object Detection. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 15\u201320). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 14\u201319). Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kong, T., Sun, F., Liu, H., Jiang, Y., and Shi, J. (2019). FoveaBox: Beyond Anchor-Based Object Detector. arXiv.","DOI":"10.1109\/TIP.2020.3002345"},{"key":"ref_60","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. 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