{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:59:40Z","timestamp":1780444780470,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EEG recognition and service robot control based on structure optimization deep network in the background of high noise","award":["61673079"],"award-info":[{"award-number":["61673079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the synthetic aperture radar (SAR) ship image, the target size is small and dense, the background is complex and changeable, the ship target is difficult to distinguish from the surrounding background, and there are many ship-like targets in the image. This makes it difficult for deep-learning-based target detection algorithms to obtain effective feature information, resulting in missed and false detection. The effective expression of the feature information of the target to be detected is the key to the target detection algorithm. How to improve the clear expression of image feature information in the network has always been a difficult point. Aiming at the above problems, this paper proposes a new target detection algorithm, the feature information efficient representation network (FIERNet). The algorithm can extract better feature details, enhance network feature fusion and information expression, and improve model detection capabilities. First, the convolution transformer feature extraction (CTFE) module is proposed, and a convolution transformer feature extraction network (CTFENet) is built with this module as a feature extraction block. The network enables the model to obtain more accurate and comprehensive feature information, weakens the interference of invalid information, and improves the overall performance of the network. Second, a new effective feature information fusion (EFIF) module is proposed to enhance the transfer and fusion of the main information of feature maps. Finally, a new frame-decoding formula is proposed to further improve the coincidence between the predicted frame and the target frame and obtain more accurate picture information. Experiments show that the method achieves 94.14% and 92.01% mean precision (mAP) on SSDD and SAR-ship datasets, and it works well on large-scale SAR ship images. In addition, FIERNet greatly reduces the occurrence of missed detection and false detection in SAR ship detection. Compared to other state-of-the-art object detection algorithms, FIERNet outperforms them on various performance metrics on SAR images.<\/jats:p>","DOI":"10.3390\/rs14143489","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"3489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An SAR Ship Object Detection Algorithm Based on Feature Information Efficient Representation Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Jimin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0437-2042","authenticated-orcid":false,"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-8431","authenticated-orcid":false,"given":"Shangbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilan","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7662-300X","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1109\/TITS.2019.2910643","article-title":"Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance","volume":"21","author":"Wei","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/LRA.2020.2967272","article-title":"CNN Based Road User Detection Using the 3D Radar Cube","volume":"5","author":"Palffy","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TIFS.2020.3007327","article-title":"Fine-Grained Facial Expression Recognition in the Wild","volume":"16","author":"Liang","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","article-title":"A Light CNN for Deep Face Representation with Noisy Labels","volume":"13","author":"Wu","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yu, J., Wu, T., Zhang, X., and Zhang, W. (2022). An Efficient Lightweight SAR Ship Target Detection Network with Improved Regression Loss Function and Enhanced Feature Information Expression. Sensors, 22.","DOI":"10.3390\/s22093447"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1109\/JSTARS.2018.2873190","article-title":"Inshore Ship Detection Based on Convolutional Neural Network in Optical Satellite Images","volume":"11","author":"Wu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10032","DOI":"10.1109\/JSEN.2020.2991741","article-title":"mm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs","volume":"20","author":"Sengupta","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TCDS.2017.2783944","article-title":"Multibranch Attention Networks for Action Recognition in Still Images","volume":"10","author":"Yan","year":"2018","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"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","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","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_14","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 23\u201328). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Glasgow, UK.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target Scattering Decomposition in Terms of Roll-Invariant Target Parameters","volume":"45","author":"Touzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1109\/JSTARS.2017.2681719","article-title":"Scattering Mechanism Based Snow Cover Mapping Using RADARSAT-2 C-Band Polarimetric SAR Data","volume":"10","author":"Muhuri","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Y., Wang, S., Liang, T., Zhao, Q., Tang, Z., and Ling, H. (2019). CBNet: A Novel Composite Backbone Network Architecture for Object Detection. arXiv.","DOI":"10.1609\/aaai.v34i07.6834"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018). Path Aggregation Network for Instance Segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, D., and Wu, Y. (2020). MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection. Remote Sens., 12.","DOI":"10.3390\/rs12193118"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Liu, Z., Zhu, C., Qi, J., and Zhao, D. (2021). Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block. Remote Sens., 13.","DOI":"10.3390\/rs13050862"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"23785","DOI":"10.1109\/ACCESS.2022.3154474","article-title":"Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images","volume":"10","author":"Gao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xia, R., Chen, J., Huang, Z., Wan, H., Wu, B., Sun, L., Yao, B., Xiang, H., and Xing, M. (2022). CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14061488"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"57452","DOI":"10.1109\/ACCESS.2022.3178597","article-title":"RSTC: A New Residual Swin Transformer for Offline Word-Level Writer Identification","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3180891","article-title":"Learning From Synthetic InSAR with Vision Transformers: The Case of Volcanic Unrest Detection","volume":"60","author":"Bountos","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Uppili Arasanipalai, A., and Hou, Q. (2020). Rotate to Attend: Convolutional Triplet Attention Module. arXiv.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"10922","DOI":"10.1109\/TIE.2019.2962437","article-title":"An Efficient Convolutional Neural Network Model Based on Object-Level Attention Mechanism for Casting Defect Detection on Radiography Images","volume":"67","author":"Hu","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2019). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. arXiv.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., 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_34","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_35","unstructured":"Trockman, A., and Zico Kolter, J. (2022). Patches Are All You Need?. arXiv."},{"key":"ref_36","unstructured":"Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y., Shen, H., Ren, J., Han, S., and Ding, E. (2020). PP-YOLO: An Effective and Efficient Implementation of Object Detector. arXiv."},{"key":"ref_37","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_38","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_39","doi-asserted-by":"crossref","unstructured":"Yu, J., and Zhang, W. (2021). Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4. Sensors, 21.","DOI":"10.3390\/s21093263"},{"key":"ref_40","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Deng, Y., Guan, D., Chen, Y., Yuan, W., Ji, J., and Wei, M. (2022, January 7\u201313). Sar-Shipnet: Sar-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-Resolution Feature Fusion. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual.","DOI":"10.1109\/ICASSP43922.2022.9747359"},{"key":"ref_42","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as Points. arXiv."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2019). GhostNet: More Features from Cheap Operations. arXiv.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., and Tan, T. (2021). Focal and Efficient IOU Loss for Accurate Bounding Box Regression. arXiv.","DOI":"10.1016\/j.neucom.2022.07.042"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:33Z","timestamp":1760140533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,21]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143489"],"URL":"https:\/\/doi.org\/10.3390\/rs14143489","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,21]]}}}