DOI QR코드

DOI QR Code

Target Classification of Active Sonar Returns based on Convolutional Neural Network

컨볼루션 신경망 기반의 능동소나 표적 식별

  • Received : 2017.06.07
  • Accepted : 2017.07.10
  • Published : 2017.10.31

Abstract

Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.

최근 딥 러닝 알고리듬이 다양한 분야에 적용되어 좋은 성능을 내고 있지만, 소나시스템에는 아직 활발히 적용되지 않고 있다. 본 논문에서는 기뢰와 같은 금속 물체와 바위로부터 반사된 능동소나 수신음 데이터를 딥 러닝 알고리듬의 하나인 컨볼루션 신경망으로 식별하는 실험을 수행하였다. 과적합 방지 및 성능 향상을 위해 데이터 확장을 하였고, 확장 및 하이퍼파라미터 값 변화에 따른 성능 변화를 분석하였다. 훈련데이터를 수신각도에 독립적인 경우와 의존적인 경우로 나누어 실험을 수행하였고, 그 결과 각각 88.9%, 94.9%의 성능을 보였다. 이는 이전 연구에서 인공신경망 및 Support Vector Machine 알고리듬을 적용하여 얻은 성능보다 최대 4.5% 포인트 향상되었다.

Keywords

References

  1. J. W. Seok, T. W. Kim, and K. S. Bae, "Active Sonar Target Recognition Using Fractional Fourier Transform," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 11, pp. 2505-2511, Nov. 2013. https://doi.org/10.6109/jkiice.2013.17.11.2505
  2. R. P. Gorman and T. J. Sejnowski, "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets," Neural Networks, vol. 1, no.1, pp. 75-89, 1988. https://doi.org/10.1016/0893-6080(88)90023-8
  3. J. H. Park, C. S. Hwang, and K. S. Bae, "Analysis of target classification performances of active sonar returns depending on parameter values of SVM kernel functions," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 5, pp. 1083-1088, Nov. 2013. https://doi.org/10.6109/jkiice.2013.17.5.1083
  4. J. W. Seok, T. H. Kim, and K. S. Bae, "Active Sonar Target Recognition Using Fractional Fourier Transform," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 11, pp. 2505-2511, Nov. 2013. https://doi.org/10.6109/jkiice.2013.17.11.2505
  5. H. Liu and L. Carin, "Class-based Target Classification in Shallow Water Channel based on Hidden Markov Model," Proceedings of International Conference Acoustics Speech and Signal Processing, vol. 3, pp. 2889-2892, 2002.
  6. J. H. Seok, "Multi-aspect Based Active Sonar Target Classification," Journal of Korea Multimedia Society, vol. 19, no. 10, pp. 1775-1781, Oct. 2016. https://doi.org/10.9717/kmms.2016.19.10.1775
  7. Center for Machine Learning and Intelligent Systems. UCI Machine Learning Repository [Internet]. Available: http://archive.ics.uci.edu/ml/index.html.
  8. J. Bergstra and Y. Bengio, "Random Search for Hyper-Parameter Optimization," Journal of Machine Learning Research, vol. 13, pp. 281-305, Nov. 2012.
  9. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Backpropagation applied to handwritten zip code recognition," Neural Computation, vol.1, no.4, pp. 541-551, Dec. 1989. https://doi.org/10.1162/neco.1989.1.4.541
  10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems, pp. 1097-1105, Dec. 2012.
  11. K. He, X. Zhang, S. Ren, and J. Sun, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," in Proceedings of the 2015 IEE International Conference on Computer Vision, Santiago, pp. 1026-1034, 2015.
  12. N. Srivastava, G. Hiton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks fromOvertting," Journal of Machine Learning Research, vol. 15, pp. 1929-1958, Jun. 2014.
  13. D. Kingma and J. L. Ba, "ADAM: A Method for stochastic optimization," 3rd International Conference for Learning Representations, San Diego, pp. 1-15, 2015.
  14. J. H. Kim, F. BieBmann, and S. W. Lee, "Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 5. pp. 867-876, Sept. 2015. https://doi.org/10.1109/TNSRE.2014.2375879
  15. A. Ng, "Nuts and bolts of building AI applications using Deep Learning," Conference on Neural Information Processing Systems, Tutorial, 2016.