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Real-Time Lightweight CNN in Robots with Very Limited Computational Resources: Detecting Ball in NAO

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Computer Vision Systems (ICVS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11754))

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Abstract

This paper proposed a lightweight CNN architecture called Binary-8 for ball detection on NAO robots together with a labelled dataset of 1000+ images containing balls in various scenarios to address the most basic and key issue in robot soccer games: detecting the ball. In contrast to the existing ball detection methods base on traditional machine learning and image processing, this paper presents a lightweight CNN object detection approach for CPU. In order to deal with the problems of tiny size, blurred image, occlusion and many other similar objects during detection, the paper designed a network structure with strong enough feature extraction ability. In order to achieve real time performance, the paper uses the ideas of depthwise separable convolution and binary weights. Besides, we also use SIMD (Single Instruction Multiple Data) to accelerate the operations. Full procedure and net structure have been given in this paper. Experimental results show that the proposed CNN architecture can run at full frame rate (140 Fps on CPU) with an accurate percentage of 97.13%.

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References

  1. Pedoeem, J., Huang, R.: YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. arXiv preprint arXiv:1811.05588 (2018)

  2. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  3. Wang, R.J., Li, X., Ling, C.X.: Pelee: a real-time object detection system on mobile devices. In: Advances in Neural Information Processing Systems, pp. 1963–1972 (2018)

    Google Scholar 

  4. Van Nguyen, H., Zhou, K., Vemulapalli, R.: Cross-domain synthesis of medical images using efficient location-sensitive deep network. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 677–684. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_83

    Chapter  Google Scholar 

  5. Han, S., Mao, H., Dall, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149 (2015)

  6. Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. arXiv preprint arXiv:1504.04788 (2015)

  7. Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)

  8. Gong, Y., Liu, L., Yang, M., Bourdev, L.: Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)

  9. Lin, Z., Courbariaux, M., Memisevic, R., Bengio, Y.: Neural networks with few multiplications. arXiv preprint arXiv:1510.03009 (2015)

  10. Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv:1405.3866 [cs.CV]

  11. Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. arXiv preprint arXiv:1603.05279 (2016)

  12. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  13. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

    Google Scholar 

  14. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  15. Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861 (2017)

    Google Scholar 

  16. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  17. Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

    Google Scholar 

  18. 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_2

    Chapter  Google Scholar 

  19. 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)

  20. Redmon, J., Lu, Y., Agirbau, L.: Huang ImageNet Classification [EB/OL], 3 November 2013. https://pjreddie.com/darknet/imagenet/

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Acknowledgements

The authors would like to acknowledge Team TJArk in RoboCup Standard Platform League for providing NAO robots and robot soccer field to capture dataset images. They also offer a lot of technical supports on operating and programming on NAO.

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Correspondence to Qingqing Yan .

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Yan, Q., Li, S., Liu, C., Chen, Q. (2019). Real-Time Lightweight CNN in Robots with Very Limited Computational Resources: Detecting Ball in NAO. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-34995-0_3

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