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

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Object Detection Accuracy Improvements of Mobility Equipments through Substitution Augmentation of Similar Objects

유사물체 치환증강을 통한 기동장비 물체 인식 성능 향상

  • Heo, Jiseong (Defense AI Technology Center, Agency for Defense Development) ;
  • Park, Jihun (Defense AI Technology Center, Agency for Defense Development)
  • 허지성 (국방과학연구소 국방인공지능기술센터) ;
  • 박지훈 (국방과학연구소 국방인공지능기술센터)
  • Received : 2021.12.16
  • Accepted : 2022.04.29
  • Published : 2022.06.05
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Abstract

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.

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References

  1. A. Bochkovskiy, C. Y. Wang and H. Y. M. Liao, "Yolov4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020.
  2. T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar and C. L. Zitnick, "Microsoft Coco: Common Objects in Context," European Conference on Computer Vision, pp. 740-755, September, 2014.
  3. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn and A. Zisserman, "The Pascal Visual Object Classes(VOC) Challenge," International Journal of Computer Vision, Vol. 88, No. 2, pp. 303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4
  4. T. DeVries and G. W. Taylor, "Improved Regularization of Convolutional Neural Networks with Cutout," arXiv preprint arXiv:1708.04552., 2017.
  5. H. Zhang, M. Cisse, Y. N. Dauphin and D. Lopez-Paz, "Mixup: Beyond Empirical Risk Minimization," arXiv preprint arXiv:1710.09412. 2017.
  6. A. Krizhevsky and G. Hinton, "Learning Multiple Layers of Features from Tiny Images," 2009.
  7. S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe and Y. Yoo, "Cutmix: Regularization Strategy to Train Strong Classifiers with Localizable Features" In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023-6032, 2019.
  8. S. Lin, T. Yu, R. Feng, X. Li, X. Jin and Z. Chen, "Local Patch AutoAugment with Multi-Agent Collaboration." arXiv preprint arXiv:2103.11099, 2021.
  9. G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T. Y. Lin, E. D. Cubuk, Q. V. Le, B. Zoph, "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918-2928, 2021.
  10. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan and Q. V. Le, "Autoaugment: Learning Augmentation Strategies from Data," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113-123, 2019.
  11. E. D. Cubuk, B. Zoph, J. Shlens and Q. V. Le, "Randaugment: Practical Automated Data Augmentation with a Reduced Search Space," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702-703, 2020.
  12. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks," Advances in Neural Information Processing Systems, 28, pp. 91-99, 2015.
  13. M. Tan, R. Pang and Q. V. Le, "Efficientdet: Scalable and Efficient Object Detection," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781-10790, 2020.
  14. T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan and S. Belongie, "Feature Pyramid Networks for Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125, 2017.
  15. J. Hu, L. Shen and G. Sun, "Squeeze-and-Excitation Networks," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018.
  16. L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, "Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 4, pp. 834-848, 2017. https://doi.org/10.1109/TPAMI.2017.2699184
  17. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," In International Conference on Machine Learning, pp. 448-456, June, 2015.
  18. I. Loshchilov and F. Hutter, "Sgdr: Stochastic Gradient Descent with Warm Restarts," arXiv preprint arXiv:1608.03983, 2016.
  19. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," The Journal of Machine Learning Research, Vol. 15, No. 1, pp. 1929-1958, 2014.
  20. A. Telea, "An Image Inpainting Technique based on the Fast Marching Method," Journal of Graphics Tools, Vol. 9, No. 1, pp. 23-34, 2004. https://doi.org/10.1080/10867651.2004.10487596
  21. M. Bertalmio, A. L. Bertozzi and Sapiro, G., "Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting," In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, December, 2001.
  22. C. Park. "Kia Makes Korean High-Performance Military Vehicles," The Kyunghyang Shinmun, November 4, 2012, https://m.khan.co.kr/economy/auto/article/201211042143515. accessed November 19, 2021.
  23. KIA, "Kia Corporation's Special Vehicle Website," KIA Special Vehicle, 19 Nov. 2021, https://special.kia.com/en/kia/subpage/models-km450/Cargo-Truck.do#.Ya8Dc9BByUk. accessed 19 Nov. 2021.
  24. T. Y. Lin, P. Goyal, R. Girshick, K. He and P. Dollar, "Focal Loss for Dense Object Detection," Proceedings of the IEEE International Conference on Computer Vision, 2017.
  25. J. Redmon and F. Ali, "Yolov3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018.
  26. M. Tan and Q. Le, "Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks," International Conference on Machine Learning, PMLR, 2019.
  27. H. Wang, Q. Wang, F. Yang, W. Zhang and W. Zuo, "Data Augmentation for Object Detection via Progressive and Selective Instance-Switching," arXiv preprint arXiv:1906.00358, 2019.