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Efficient Semantic Segmentation for Visual Bird’s-Eye View Interpretation

  • Timo Sämann
  • Karl Amende
  • Stefan Milz
  • Christian Witt
  • Martin Simon
  • Johannes Petzold
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance. One such application is the interpretation of the visual bird’s-eye view, which requires the semantic segmentation of the four omnidirectional camera images. In this paper, we present an efficient semantic segmentation that sets new standards in terms of runtime and hardware requirements. Our two main contributions are the decrease of the runtime by parallelizing the ArgMax layer and the reduction of hardware requirements by applying the channel pruning method to the ENet model.

Keywords

Efficient semantic segmentation Channel pruning Embedded systems Bird’s-eye view generation 

Notes

Acknowledgments

We would like to thank Senthil Yogamani and our colleagues at Valeo Vision Systems in Ireland for collaboration on our dataset using automotive fisheye cameras. We would like to thank Valeo, especially Jörg Schrepfer, for the opportunity doing fundamental research.

References

  1. 1.
    Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)
  2. 2.
    Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding, pp. 3213–3223 (2016)Google Scholar
  3. 3.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  4. 4.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  5. 5.
    He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: International Conference on Computer Vision (ICCV), vol. 2, p. 6 (2017)Google Scholar
  6. 6.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  7. 7.
    Bagherinezhad, H., Rastegari, M., Farhadi, A.: LCNN: lookup-based convolutional neural network (2016)Google Scholar
  8. 8.
    Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. CoRR abs/1603.05279 (2016)Google Scholar
  9. 9.
    Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. CoRR abs/1405.3866 (2014)Google Scholar
  10. 10.
    Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. CoRR abs/1506.02626 (2015)Google Scholar
  11. 11.
    Zhang, B., Appia, V.V., Pekkucuksen, I., Liu, Y., Batur, A.U., Shastry, P., Liu, S., Sivasankaran, S., Chitnis, K.: A surround view camera solution for embedded systems. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 676–681 (2014)Google Scholar
  12. 12.
    Deng, L., Yang, M., Li, H., Li, T., Hu, B., Wang, C.: Restricted deformable convolution based road scene semantic segmentation using surround view cameras (2018)Google Scholar
  13. 13.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Valeo Comfort and Driving AssistanceSite Kronach (Germany)KronachGermany

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