한국시뮬레이션학회논문지 (Journal of the Korea Society for Simulation)

한국시뮬레이션학회 (The Korea Society for Simulation)
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합성곱 신경망을 활용한 군사용 CCTV 객체 인식

Object Recognition Using Convolutional Neural Network in military CCTV

  • Ahn, Jin Woo (University of Freiburg, Computer Science) ;
  • Kim, Dohyung (Johns Hopkins University, Applied Mathematics and Statistics) ;
  • Kim, Jaeoh (Department of Data Science, Inha University)
  • 투고 : 2022.03.07
  • 심사 : 2022.05.16
  • 발행 : 2022.06.30
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초록

병력감축 등 국방 및 안보환경의 변화에 따라 육군의 경계시스템에도 변화가 시급한 상황이다. 또한 경계작전의 특성상 인간의 실수가 번번이 발생하고 있으며 이러한 실수가 전체 경계작전의 실패로 귀결되는 상황은 경계시스템의 인공지능 도입이 필요한 것에 대한 중요한 이유이다. 본 연구의 목적은 합성곱 신경망 방법을 활용하여 군사용 CCTV에 적합한 인공지능 영상인식 시스템을 개발하는 것이다. 본 연구에서 개발한 시스템의 주요 특징은 먼저, 군사용 CCTV의 특징상 상대적으로 작은 객체를 인식해야하는 상황에 적합한 학습데이터를 활용한 것이다. 둘째, 학습용 데이터 셋에 대해 데이터 증강 알고리즘을 활용하여 군사용에 보다 적합하도록 유도한 것이다. 셋째, 군사용 영상의 위장, 악천후 등 상황을 고려하여 영상의 잡음을 개선하는 알고리즘을 적용하였다. 본 연구에서 제안하는 시스템의 성능 평가결과 객체의 인식능력이 기존 방법에 비해 우수함을 확인하였다.

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

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과제정보

이 논문은 인하대학교의 지원에 의하여 연구되었음.

참고문헌

  1. Army Headquarters Guard, "Field Manual(FM)", pp. 31-36, 2014.
  2. Bochkovskiy, A., C. Wang and H.M. Liao, "Yolov4: Optimal Speed and Accuracy of Object Detection", arXiv.org, 2020.
  3. Dalal, N. and B. Triggs, "Histograms of Oriented Gradients for Human Detection", 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
  4. Everingham, M., L. Van-Gool, C.K.I. Williams, J. Winn and A. Zisserman, "The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results", 2012.
  5. Girshick, R., "Fast R-CNN", 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
  6. Girshick, R., J. Donahue, T. Darrell and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation", 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
  7. He, K., X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  8. Krizhevsky, A., I. Sutskever, G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Proc. of the 25th International Conference on Neural Information Processing Systems, Granada, Vol. 1, pp. 1097-1105, 2012.
  9. Kuznetsova, A., H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset and S. Kamali, "The Open Images Dataset V4", International Journal of Computer Vision, Vol. 128, No. 7, pp. 1956-1981, 2020. https://doi.org/10.1007/s11263-020-01316-z
  10. LeCun, Y., P. Haffner, L. Bottou and Y. Bengio, "Object Recognition with Gradient-Based Learning", Shape, Contour and Grouping in Computer Vision, Springer Berlin, Heidelberg, pp. 319-345, 1999.
  11. Loh, Y.P. and C.S. Chan, "Getting to Know Low-Light Images with the Exclusively Dark Dataset", Computer Vision and Image Understanding, Vol. 178, pp. 30-42, 2019. https://doi.org/10.1016/j.cviu.2018.10.010
  12. Lowe, D.G., "Distinctive Image Features from ScaleInvariant Keypoints", International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  13. Mao, Q., H. Sun, Y. Liu and R. Jia, "Mini-yolov3: RealTime Object Detector for Embedded Applications" IEEE Access, Vol. 7, pp. 529-538, 2019.
  14. Park, T.W., T.H. Kim and H.J. Han, "Evaluation and Improvement on GOP Scientific Guard System : Based on the Results of the Survey", Journal of the Military Operations Research Society of Korea, Vol. 46, No. 2, pp. 57-72, 2020.
  15. Redmon, J., S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  16. Redmon, J. and A. Farhadi, "YOLOv3: An Incremental Improvement", arXiv.org, 2018.
  17. Ren, S., K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149, 2017. https://doi.org/10.1109/TPAMI.2016.2577031
  18. Szegedy, C., L. Wei, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going Deeper with Convolutions", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
  19. Yu, X., Y. Gong, N. Jiang, Q. Ye and Z. Han, "Scale Match for Tiny Person Detection", 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1246-1254, 2020.
  20. Zamir, S.W., A. Arora, S. Khan, M. Hayat, F.S. Khan, M.H. Yang and L. Shao, "Multi-Stage Progressive Image Restoration" 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  21. Zhang, S., Y. Xie, J. Wan, H. Xia, S.Z. Li and G. Guo, "Widerperson: A Diverse Dataset for Dense Pedestrian Detection in the Wild", IEEE Transactions on Multimedia, Vol. 22, No. 2, pp. 380-393, 2020. https://doi.org/10.1109/tmm.2019.2929005