Advanced search
Publication Cover
Remote Sensing Letters Volume 13, 2022 - Issue 11
Submit an article Journal homepage
132
Views
1
CrossRef citations to date
0
Altmetric
Research Article

D2ADet: dual-activation domain RetinaNet based on a deformable feature pyramid network for ship detection in SAR images

Shuang Zhaoa Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9370-4183
ORCID Icon,
Kai Qiaoc Beijing Institute of Tracking and Telecommunications Technology, Beijing, China
,
Weidong Yua Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
,
Rong Yanga Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
&
Jiyu Lva Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Pages 1153-1163 | Received 09 Nov 2021, Accepted 19 Sep 2022, Published online: 10 Oct 2022

References

  • Cai, Z., and N. Vasconcelos. 2018. “Cascade R-CNN: Delving into High Quality Object Detection.” 2018 IEEE Conference on Computer Vision and Pattern Recognition, 6154–6162. Salt Lake City: IEEE, June 18–21.
  • Lin, T. Y., P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie. 2017. “Feature Pyramid Networks for Object Detection.” 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2117–2125. Venice, Italy: IEEE, October 22–29.
  • Lin, T., P. Goyal, R. Girshick, K. He, and P. Dollár. 2020. “Focal Loss for Dense Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2): 318–327. doi:10.1109/TPAMI.2018.2858826.
  • Li, J., C. Qu, and J. Shao. 2017. “Ship Detection in SAR Images Based on an Improved Faster R-CNN.” 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), 1–6. Beijing, China: IEEE, November 13–14.
  • Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg. 2016. “SSD: Single Shot MultiBox Detector.” 2016 14th European Conference on Computer Vision, 21–37. Amsterdam, The Netherlands: IEEE, October 8–16.
  • Liu, S., L. Qi, H. Qin, J. Shi, and J. Jia 2018. “Path Aggregation Network for Instance Segmentation.” 2018 IEEE Conference on Computer Vision and Pattern Recognition, 8759–8768. Salt Lake City: IEEE, June 18–21.
  • Redmon, J., and A. Farhadi. 2017. “YOLO9000: Better, Faster, Stronger.” 2017 IEEE Conference on Computer Vision and Pattern Recognition, 6517–6525. Hawaii: IEEE, July 21–26.
  • Ren, S., K. He, R. Girshick, and J. Sun. 2017. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6): 1137–1149. doi:10.1109/TPAMI.2016.2577031.
  • Wang, Y., W. Li, X. Li, and X. Sun. 2018. “Ship Detection by Modified RetinaNet.” 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 1–5. doi:10.1109/PRRS.2018.8486308.
  • Wang, J., Y. Lin, J. Guo, and L. Zhuang. 2020. “SSS-YOLO: Towards More Accurate Detection for Small Ships in SAR Image.” Remote Sensing Letters 12 (2): 93–102. doi:10.1080/2150704x.2020.1837988.
  • Wang, Y., C. Wang, and H. Zhang. 2018. “Combining a Single Shot Multibox Detector with Transfer Learning for Ship Detection Using Sentinel-1 SAR Images.” Remote Sensing Letters 9 (8): 780–788. doi:10.1080/2150704X.2018.1475770.
  • Wei, S., X. Zeng, Q. Qu, M. Wang, H. Su, and J. Shi. 2020. “HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation.” IEEE Access 8: 120234–120254. doi:10.1109/ACCESS.2020.3005861.
  • Yang, R., Z. Pan, X. Jia, L. Zhang, and Y. Deng. 2021. “A Novel CNN-Based Detector for Ship Detection Based on Rotatable Bounding Box in SAR Images.” The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 1938–1958. doi:10.1109/JSTARS.2021.3049851.
  • Zan, L., K. Lu, and Z. Chen. 2019. “Automatic Detection of Ship Based on Rotation Invariant RetinaNet.” China High Resolution Earth Observation Conference, 515–525. Singapore: Springer.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.