ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery

  • Seyed Majid AzimiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to enhance the speed-wise performance, we construct our method primarily using channel shuffling and grouped convolutions. We apply inception modules and deformable modules to consider the size and geometric shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and compared against the state-of-the-art real-time object detection networks. ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on test sets of both datasets. We show that our algorithm achieves real-time performance by running at the speed of 14 frames per second on NVIDIA Jetson TX2 showing high potential for this method for real-time processing in UAVs.


UAV imagery Real-time vehicle detection On-board embedded processing Convolutional neural networks Traffic monitoring 


  1. 1.
    Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR (2017)Google Scholar
  2. 2.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357 (2017)
  3. 3.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: NIPS (2016)Google Scholar
  4. 4.
    Dai, J., et al.: Deformable convolutional networks. In: ICCV (2017)Google Scholar
  5. 5.
    Azimi, S.M., Vig, E., Bahmanyar, R., Körner, M., Reinartz, P.: Towards multi-class object detection in unconstrained remote sensing imagery. In: ACCV (2018)Google Scholar
  6. 6.
    Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  7. 7.
    Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  8. 8.
    Hsieh, M., Lin, Y., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: ICCV (2017)Google Scholar
  9. 9.
    Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: CVPR (2017)Google Scholar
  10. 10.
    Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\) 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)
  11. 11.
    Kim, K.H., Hong, S., Roh, B., Cheon, Y., Park, M.: PVANET: deep but lightweight neural networks for real-time object detection. arXiv preprint arXiv:1608.08021 (2016)
  12. 12.
    Azimi, S.M., Vig, E., Kurz, F., Reinartz, P.: Segment-and-count: vehicle counting in aerial imagery using atrous convolutional neural networks. In: ISPRS (2018)Google Scholar
  13. 13.
    Liu, K., Mattyus, G.: Fast multiclass vehicle detection on aerial images. IEEE GRSL Lett. 12, 1938–1942 (2015)Google Scholar
  14. 14.
    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). Scholar
  15. 15.
    Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: A large contextual dataset for classification, detection and counting of cars with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 785–800. Springer, Cham (2016). Scholar
  16. 16.
    Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)Google Scholar
  17. 17.
    Azimi, S.M., Fischer, P., Körner, M., Reinartz, P.: Aerial LaneNet: lane marking semantic segmentation in aerial imagery using wavelet-enhanced cost-sensitive symmetric fully convolutional neural networks. arXiv preprint arXiv:1803.06904 (2018)
  18. 18.
    Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: CVPR (2017)Google Scholar
  19. 19.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)Google Scholar
  20. 20.
    Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar
  21. 21.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: ICLR (2016)Google Scholar
  22. 22.
    Azimi, S.M., Britz, D., Engstler, M., Fritz, M., Mücklich, F.: Advanced steel microstructural classification by deep learning methods. Sci. Rep. - Nat. 8, 2128 (2018)CrossRefGoogle Scholar
  23. 23.
    Wang, R.J., Li, X., Ao, S., Ling, C.X.: Pelee: a real-time object detection system on mobile devices. arXiv preprint arXiv:1804.06882 (2018)
  24. 24.
    Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: CVPR (2016)Google Scholar
  25. 25.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR (2017)Google Scholar
  26. 26.
    Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083 (2017)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.German Aerospace Center (DLR), Remote Sensing Technology InstituteWeßlingGermany
  2. 2.Chair of Remote SensingTechnical University of MunichMunichGermany

Personalised recommendations