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Application of Deep Learning Technique in UAV’s Search and Rescue Operations

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Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 868))

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Abstract

This paper is concerned with the application of Deep Learning techniques for analyzing image data for search and rescue operations of Unmanned Aerial Vehicle (UAV). It uses Keras and its Tensorflow backend to model a deep Convolutional Neural Network (CNN) Learning technique and train the model with MNIST digits dataset to predict the hand- written word from the image data received from the ground level. The paper explains the stages involved in the implementation of LeNet method of Deep Learning techniques for developing a classifier for long distance recognition of handwritten words.

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References

  1. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of the CVPR (2012)

    Google Scholar 

  2. Colomina, I., Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogram. Remote Sens. 92, 79–97 (2014)

    Article  Google Scholar 

  3. Deng, L.: A tutorial survey of architectures, algorithms and applications for deep learning. APSIPA Trans. Signal Inf. Process. 3, e2 (2014)

    Google Scholar 

  4. Gundlach, J.: Design Unmanned Aircraft Systems: A Comprehensive Approach. American Institute of Aeronautics and State University, Blacksburg (2012)

    Book  Google Scholar 

  5. Ghonge, M.M., Jawandhiya, P.M.: Review of Unmanned Aircraft System (UAS). Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(4), 1646 (2013)

    Google Scholar 

  6. Howard, A.G.: Some improvements on deep convolutional neural network based image classification, arXiv preprint arXiv:1312.5402 (2013)

  7. Klemas, V.V.: Coastal and environmental remote sensing from unmanned aerial vehicle: an overview. J. Coastal Res. 31(5), 1260–1267 (2015)

    Article  Google Scholar 

  8. Li, H., Zhao, R., Wang, X.: Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification, arXiv preprint arXiv:1412.4526 (2014)

  9. Nonami, K., Kendoul, F., Suzuki, S., Wang, W., Nakazawa, D.: Autonomous Flying Robots, Unmanned Aerial Vehicles and Micro Aerial Vehicles. Springer, Tokyo (2010). ISBN 978-4-431-53855-4

    Chapter  Google Scholar 

  10. Pereira, E., Bencatel, R., Correira, J., Felix, L., Goncalves, G., Morgano, J., Sousa, J.: Unmanned air vehicles for costal and environmental research. In: da silva, C.P. (ed.) Proceedings of the ICS, Journal of Coastal Research, Special Issue No. 56, pp. 1557–1561 (2009)

    Google Scholar 

  11. Ren, J.S.J., Xu, L.: On vectorization of deep convolutional neural networks for vision tasks. In: Proceedings of the AAAI (2015)

    Google Scholar 

  12. Schmidhuberc, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the ICLR (2015)

    Google Scholar 

  14. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Proceedings of the NIPS (2013)

    Google Scholar 

  15. Tompson, J., Goroshin, R., Jain, A., et al.: Efficient object localization using convolutional networks. In: Proceedings of the CVPR (2015)

    Google Scholar 

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Naing, K.M., Zakeri, A., Iliev, O., Venkateshaiah, N. (2019). Application of Deep Learning Technique in UAV’s Search and Rescue Operations. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_62

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