{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:15:56Z","timestamp":1740154556463,"version":"3.37.3"},"reference-count":49,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China (NSFC)","doi-asserted-by":"crossref","award":["No. 41930112"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.<\/jats:p>","DOI":"10.3390\/rs14205268","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T14:09:23Z","timestamp":1666620563000},"page":"5268","source":"Crossref","is-referenced-by-count":31,"title":["YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1333-0354","authenticated-orcid":false,"given":"Simin","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1649-073X","authenticated-orcid":false,"given":"Song","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Lun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Ruochen","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Hengsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9103-3307","authenticated-orcid":false,"given":"Jiaming","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-3349","authenticated-orcid":false,"given":"Yong","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China"},{"name":"The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Jiang","family":"Qian","sequence":"additional","affiliation":[{"name":"The School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"497","article-title":"Research progress on aircraft detection and recognition in SAR imagery","volume":"9","author":"Guo","year":"2020","journal-title":"J. Radars"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/JSTARS.2017.2787573","article-title":"Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images","volume":"11","author":"Ao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/36.368224","article-title":"Localized Radon transform-based detection of ship wakes in SAR images","volume":"33","author":"Copeland","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","unstructured":"Novak, L.M., Owirka, G.J., Brower, W.S., and Weaver, A.L. (1997). The automatic target-recognition system in SAIP. Linc. Lab. J., 10, Available online: http:\/\/www.geo.uzh.ch\/microsite\/rsl-documents\/research\/SARlab\/GMTILiterature\/PDF\/NOBW97.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, P., Li, Q., Zhang, B., Wu, F., Zhao, K., Du, X., Yang, C., and Zhong, R. (2021). On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13101995"},{"key":"ref_7","first-page":"11","article-title":"Performance of a high-resolution polarimetric SAR automatic target recognition system","volume":"6","author":"Novak","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_8","first-page":"806","article-title":"A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images","volume":"10","author":"Qin","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/JOE.2012.2210800","article-title":"On the COSMO-SkyMed PingPong mode to observe metallic targets at sea","volume":"38","author":"Nunziata","year":"2012","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9325","DOI":"10.1109\/ACCESS.2020.2964540","article-title":"Attention mask R-CNN for ship detection and segmentation from remote sensing images","volume":"8","author":"Nie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Chang, H., Ma, B., Wang, N., and Chen, X. (2020, January 23\u201328). Dynamic R-CNN: Towards high quality object detection via dynamic training. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58555-6_16"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 20\u201325). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 16\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_19","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_20","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 19\u201325). You only look one-level feature. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"ref_23","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30, Available online: https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf."},{"key":"ref_25","unstructured":"Yin, W., Kann, K., Yu, M., and Sch\u00fctze, H. (2017). Comparative study of CNN and RNN for natural language processing. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Xu, Z., Sun, K., and Mao, J. (2020, January 14\u201316). Research on ResNet101 network chemical reagent label image classification based on transfer learning. Proceedings of the 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Weihai, China.","DOI":"10.1109\/ICCASIT50869.2020.9368658"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 19\u201325). Bottleneck transformers for visual recognition. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_30","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1109\/JSTARS.2018.2871556","article-title":"Ground target classification in noisy SAR images using convolutional neural networks","volume":"11","author":"Wang","year":"2018","journal-title":"IEEE J. Sel. Top Appl. Earth Obs. Remote Sens."},{"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","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_34","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 European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_35","first-page":"1026","article-title":"Improved SSD algorithm for small-sized SAR ship detection","volume":"42","author":"Juan","year":"2020","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, P., Li, Y., Zhou, H., Liu, B., and Liu, P. (2020). Detection of small ship objects using anchor boxes cluster and feature pyramid network model for SAR imagery. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8020112"},{"key":"ref_37","first-page":"5217712","article-title":"A Robust One-Stage Detector for Multiscale Ship Detection with Complex Background in Massive SAR Images","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6623","DOI":"10.1109\/TGRS.2020.2978268","article-title":"A patch-topixel convolutional neural network for small ship detection with PolSAR images","volume":"58","author":"Jin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","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 Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chang, Y.L., Anagaw, A., Chang, L., Wang, Y.C., Hsiao, C.Y., and Lee, W.H. (2019). Ship detection based on YOLOv2 for SAR imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070786"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Su, N., He, J., Yan, Y., Zhao, C., and Xing, X. (2022). SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14030442"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"42301","DOI":"10.1007\/s11432-017-9405-6","article-title":"A coupled convolutional neural network for small and densely clustered ship detection in SAR images","volume":"62","author":"Zhao","year":"2019","journal-title":"Sci. China Technol. Sci."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Peng, C., Xue, X., and Sun, J. (2018, January 8\u201314). Exfuse: Enhancing feature fusion for semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201315). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_47","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 13\u201319). Cspnet: A new backbone that can enhance learning capability of cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., and Ramanan, D. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_49","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., and Lin, D. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5268\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T22:24:21Z","timestamp":1737152661000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5268"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":49,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205268"],"URL":"https:\/\/doi.org\/10.3390\/rs14205268","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}