한국군사과학기술학회지 (Journal of the Korea Institute of Military Science and Technology)

  • 제25권5호
  • /
  • Pages.443-461
  • /
  • 2022
  • /
  • 1598-9127(pISSN)
  • /
  • 2636-0640(eISSN)
한국군사과학기술학회 (The Korea Institute of Military Science and Technology)
권호 표지
DOI QR코드

DOI QR Code

어텐션 적용 YOLOv4 기반 SAR 영상 표적 탐지 및 인식

SAR Image Target Detection based on Attention YOLOv4

  • 박종민 (한국과학기술원 전기및전자공학부) ;
  • 육근혁 (한국과학기술원 전기및전자공학부) ;
  • 김문철 (한국과학기술원 전기및전자공학부)
  • Park, Jongmin (The Electrical Engineering of Korea Advanced Institute of Science and Technology) ;
  • Youk, Geunhyuk (The Electrical Engineering of Korea Advanced Institute of Science and Technology) ;
  • Kim, Munchurl (The Electrical Engineering of Korea Advanced Institute of Science and Technology)
  • 투고 : 2022.05.06
  • 심사 : 2022.09.16
  • 발행 : 2022.10.05
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

키워드

과제정보

본 연구는 국방과학연구소의 연구비 지원으로 수행되었습니다.

참고문헌

  1. J. Oh, et. al., "PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier," Remote Sensing, Vol. 13, No. 13, 2021.
  2. J. Oh, et. al., "SPAM-Net: A CNN-based SAR Target Recognition Network with Pose Angle Marginalization Learning," IEEE Transactions on Circuits and Systems for Video Technology(TCSVT), Vol. 32, pp. 701-714, 2021.
  3. R. Girshick, et. al., "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 580-587, 2014.
  4. S. Ren, et. al., "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Proceedings of the Neural Information Processing Systems(NIPS), 2015.
  5. J. Redmon, et. al., "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 779-788, 2016.
  6. W. Liu, et. al., "SSD: Single Shot MultiBox Detector," Proceedings of the European Conference on Computer Vision(ECCV), pp. 21-37, 2016.
  7. S. Woo, et. al., "CBAM: Convolutional Block Attention Module," Proceedings of the European Conference on Computer Vision(ECCV), pp. 3-19, 2018.
  8. Y. Zhang, et. al., "Image Super-Resolution Using Very Deep Residual Channel Attention Networks," Proceedings of the European Conference on Computer Vision(ECCV), pp. 286-301, 2018.
  9. X. Qin, et. al., "FFA-Net: Feature Fusion Attention Network foe Single Image Dehazing," Proceedings of AAAI Conference on Artificial Intelligence, Vol. 34, No. 07, pp. 11908-11915, 2020.
  10. Q. Hou, et. al., "Coordinate Attention for Efficient Mobile Network Design," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 13713-13722, 2021.
  11. Y. Cui, et. al., "CFAR Ship Detection in SAR Images based on Lognormal Mixture Models," 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar(APSAR), pp. 1-3, 2011.
  12. Y. Cui, et. al., "On Semiparametric Clutter Estimation for Ship Detection in Synthetic Aperture Radar Images," in IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 5, pp. 3170-3180, 2013. https://doi.org/10.1109/TGRS.2012.2218659
  13. M. Ester, et. al., "A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," kdd. Vol. 96, No. 34, pp. 226-231, 1996.
  14. A. Bochkovskiy, et. al., "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv:2004.10934, April, 2020.
  15. Sandia National Laboratory, "MSTAR Overview," Last Modified Jan 07, 2021, Accessed May 09, 2022, https://www.sdms.afrl.af.mil/index.php?collection=mstar