{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:50:02Z","timestamp":1781797802633,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dalian Neusoft Institute of Information Joint Fund Project","award":["LH-JSRZ-202203"],"award-info":[{"award-number":["LH-JSRZ-202203"]}]},{"name":"Dalian Neusoft Institute of Information Joint Fund Project","award":["LJKMZ20222006"],"award-info":[{"award-number":["LJKMZ20222006"]}]},{"name":"Dalian Neusoft Institute of Information Joint Fund Project","award":["52271359"],"award-info":[{"award-number":["52271359"]}]},{"name":"Fundamental Scientific Research Project for Liaoning Education Department","award":["LH-JSRZ-202203"],"award-info":[{"award-number":["LH-JSRZ-202203"]}]},{"name":"Fundamental Scientific Research Project for Liaoning Education Department","award":["LJKMZ20222006"],"award-info":[{"award-number":["LJKMZ20222006"]}]},{"name":"Fundamental Scientific Research Project for Liaoning Education Department","award":["52271359"],"award-info":[{"award-number":["52271359"]}]},{"name":"National Natural Science Foundation of CHINA","award":["LH-JSRZ-202203"],"award-info":[{"award-number":["LH-JSRZ-202203"]}]},{"name":"National Natural Science Foundation of CHINA","award":["LJKMZ20222006"],"award-info":[{"award-number":["LJKMZ20222006"]}]},{"name":"National Natural Science Foundation of CHINA","award":["52271359"],"award-info":[{"award-number":["52271359"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.<\/jats:p>","DOI":"10.3390\/rs15102589","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:27:04Z","timestamp":1684204024000},"page":"2589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-8593","authenticated-orcid":false,"given":"Chen","family":"Peng","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7328-1398","authenticated-orcid":false,"given":"Zhou","family":"Hui","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Environmental Information Institute, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Peng","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9835-9983","authenticated-orcid":false,"given":"Liu","family":"Bingxin","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xiao, X., Zhou, Z., Wang, B., Li, L., and Miao, L. (2019). Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation. Remote Sens., 11.","DOI":"10.3390\/rs11212506"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155939","DOI":"10.1016\/j.scitotenv.2022.155939","article-title":"UAV Remote Sensing Applications in Marine Monitoring: Knowledge Visualization and Review","volume":"838","author":"Yang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Fu, Q., Luo, K., Song, Y., Zhang, M., Zhang, S., Zhan, J., Duan, J., and Li, Y. (2022). Study of Sea Fog Environment Polarization Transmission Characteristics. Appl. Sci., 12.","DOI":"10.3390\/app12178892"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.isatra.2020.01.038","article-title":"High-Efficiency Sub-Microscale Uncertainty Measurement Method Using Pattern Recognition","volume":"101","author":"Zhao","year":"2020","journal-title":"ISA Trans."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"An, Q., Pan, Z., and You, H. (2018). Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18020334"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A Survey of Deep Neural Network Architectures and Their Applications","volume":"234","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, S., Ding, L., and Bruzzone, L. (2020). Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12040701"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, L., Chen, G., Pan, Z., Lei, B., and An, Q. (2018, January 22\u201327). Inshore Ship Detection in SAR Images Based on Deep Neural Networks. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519555"},{"key":"ref_12","first-page":"21510590","article-title":"SAR Target Recognition Based on Efficient Fully Convolutional Attention Block CNN","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","first-page":"21706089","article-title":"Study on Pixel Entanglement Theory for Imagery Classification","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1080\/2150704X.2018.1475770","article-title":"Combining a Single Shot Multibox Detector with Transfer Learning for Ship Detection Using Sentinel-1 SAR Images","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 18\u201321). A Modified Faster R-CNN Based on CFAR Algorithm for SAR Ship Detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TGRS.2020.2997200","article-title":"Ship Detection in Large-Scale SAR Images via Spatial Shuffle-Group Enhance Attention","volume":"59","author":"Cui","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TGRS.2019.2953098","article-title":"Simultaneous Single-\/Dual-and Quad-Pol SAR Imaging over Swaths of Different Widths","volume":"58","author":"Villano","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, R., Xu, F., Pei, J., Wang, C., Huang, Y., Yang, J., and Wu, J. (August, January 28). An Improved Faster R-CNN Based on MSER Decision Criterion for SAR Image Ship Detection in Harbor. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898078"},{"key":"ref_20","first-page":"21603357","article-title":"BANet: A Balance Attention Network for Anchor-Free Ship Detection in SAR Images","volume":"60","author":"Hu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"FINet: A Feature Interaction Network for SAR Ship Object-Level and Pixel-Level Detection","volume":"60","author":"Hu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"22538002","DOI":"10.1109\/TGRS.2023.3235002","article-title":"MTU-Net: Multilevel TransUNet for Space-Based Infrared Tiny Ship Detection","volume":"61","author":"Wu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"21590405","article-title":"Arbitrary-Oriented Ship Detection through Center-Head Point Extraction","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JOE.2021.3126090","article-title":"IA-Net: An Inception\u2013Attention-Module-Based Network for Classifying Underwater Images From Others","volume":"47","author":"Yang","year":"2022","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, G., Song, B., Liang, P., Xu, J., and Yue, T. (2022). Voids Filling of DEM with Multiattention Generative Adversarial Network Model. Remote Sens., 14.","DOI":"10.3390\/rs14051206"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ting, L., Baijun, Z., Yongsheng, Z., and Shun, Y. (2021, January 15\u201317). Ship Detection Algorithm Based on Improved YOLO V5. Proceedings of the 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China.","DOI":"10.1109\/CACRE52464.2021.9501331"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, Z., Wang, B., Miao, L., An, Z., and Xiao, X. (2021). Domain Adaptive Ship Detection in Optical Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13163168"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, Z., Meng, C., Cheng, J., Zhang, Z., and Chang, S. (2022). A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14246312"},{"key":"ref_31","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":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, C., Su, H., Gao, L., and Wang, T. (2022). Deep Learning for SAR Ship Detection: Past, Present and Future. Remote Sens., 14.","DOI":"10.3390\/rs14112712"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Liu, N., Cui, Z., Cao, Z., Pi, Y., and Lan, H. (August, January 28). Scale-Transferrable Pyramid Network for Multi-Scale Ship Detection in SAR Images. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898865"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, X., Li, D., Liu, H., Wan, J., Chen, Z., and Liu, Q. (2022). A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14153829"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_38","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 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_39","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_40","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2589\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:35:49Z","timestamp":1760124949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,16]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102589"],"URL":"https:\/\/doi.org\/10.3390\/rs15102589","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,16]]}}}