반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

딥러닝을 이용한 IOT 기기 인식 시스템

A Deep Learning based IOT Device Recognition System

  • 추연호 (한국기술교육대학교 컴퓨터공학부) ;
  • 최영규 (한국기술교육대학교 컴퓨터공학부)
  • Chu, Yeon Ho (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
  • 투고 : 2019.05.03
  • 심사 : 2019.06.18
  • 발행 : 2019.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

키워드

참고문헌

  1. K. Song, G. Kim, T. Kim, C. Ryu, S. Park, J.H. Lee, J.K. Lee, and S. Hwang, "Trusted Reality Technology, from a Post-Smartphone Perspective," Electronics and Telecommunications Trend, 2018.
  2. P. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  3. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Deection," Computer Vision and Pattern Recognition, vol.1, pp. 886-893, 2005.
  4. E. Osuna, R. Freund and F. Girosit, "Training support vector machines: an application to face detection," In IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'97), pp. 130-136, San Juan, Puerto Rico, 1997.
  5. Y. Freund and R. E. Schapire, "Experiments With a New Boosting Algorithm. In Machine Learning," In Proceedings of the Thirteen International Conference In Machine Learning, Bari, pp. 148-156, 1996.
  6. Y. Chu, B. Lee and Y. Choi, "A Video based Traffic Light Recognition System for Intelligent Vehicles," Journal of the Semiconductor & Display Technology, Vol. 14, No. 2. June 2015.
  7. A. Krizhevsky, I. Sutskever and GE. Hinton, "Imagenet classification with deep convolutional neural networks," In proc. Advances in Neural Information Processing Systems 25, pp. 1090-1098, 2012.
  8. C. Szegedy, et al., "Going Deeper with Convolutions," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2014.
  9. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," In Proc. International Conference on Learning Representations, http://arxiv.org/abs/1409.1556, 2014.
  10. K. He, X. Zhang, et al., "Deep Residual Learning for Image Recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
  11. B. Liu and X. Zang, "Caffe2 vs. TensorFlow: Which is a Better Deep Learning Framework?" Stanford University, http://cs242.stanford.edu/assets/projects/ 2017/liubaigexzang.pdf
  12. G. Levi and T. Hassner, "Age and Gender Classification Using Convolutional Neural Networks," IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on CVPR, Boston, June 2015.