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A Novel Small Vehicle Detection Method Based on UAV Using Scale Adaptive Gradient Adjustment

  • Changju Feng
  • Zhichao LianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)

Abstract

Vehicle detection based on UAV video is a typical small object detection task. In recent years, multi-scale prediction framework has become one of key steps for small object detection. However, the performances of existing methods are still not satisfactory for small object detection. In this paper, inspired by that the scale of object has an impact on gradient descent in the deep learning process, we choose the intersection over union (IOU) as the evaluation metric to analyze the relationship between scale of objects and gradient. We have shown that the gradient adjustment methods should satisfy some rules and thus we propose a new gradient adjustment formula based on our analysis. In addition, we built a mixed small vehicle dataset based on UAV videos for better evaluation of small vehicle detection. In the comparison with existing methods, our proposed method has achieved better results. The performance of our method reveals the potential of scale adaptive gradient descent method.

Keywords

Small vehicle detection Gradient adjustment IOU 

References

  1. 1.
    He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2014)CrossRefGoogle Scholar
  2. 2.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  3. 3.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: NIPS (2015)Google Scholar
  4. 4.
    Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: CVPR (2017)Google Scholar
  5. 5.
    Zhao, Q., Sheng, T., Wang, Y., et al.: M2Det: a single-shot object detector based on multi-level feature pyramid network. In: AAAI (2019)Google Scholar
  6. 6.
    Tian, Z., Shen, C., Chen, H., et al.: FCOS: fully convolutional one-stage object detection. https://arxiv.org/abs/1904.01355. Accessed 14 Apr 2019
  7. 7.
    Pang, J., Chen, K., Shi, J., et al.: Libra R-CNN: towards balanced learning for object detection. In: CVPR (2019)Google Scholar
  8. 8.
    Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: DetNet: design backbone for object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 339–354. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01240-3_21CrossRefGoogle Scholar
  9. 9.
    Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: CVPR (2017)Google Scholar
  10. 10.
    Li, J., Liang, X., Wei, Y., et al.: Perceptual generative adversarial networks for small object detection. In: CVPR (2017)Google Scholar
  11. 11.
    Razakarivony, S., Jurie, F.: Vehicle detection in aerial imagery: a small target detection benchmark. J. Vis. Commun. Image Represent. 34, 187–203 (2016)CrossRefGoogle Scholar
  12. 12.
    Geiger, A., Lenz, P., Stiller, C., et al.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhu, H., Chen, X., Dai, W., et al.: Orientation robust object detection in aerial images using deep convolutional neural network. In: ICIP (2015)Google Scholar
  14. 14.
    Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: CVPR (2016)Google Scholar
  15. 15.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. In: CVPR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNanjingChina

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