Classification of UAVs' distorted images using Convolutional Neural Networks
Resumo
Currently, the use of unmanned aerial vehicles (UAVs) is becoming ever more common for acquiring images in precision agriculture, either to identify characteristics of interest or to estimate plantations. However, despite this growth, their processing usually requires specialized techniques and software. During flight, UAVs may undergo some variations, such as wind interference and small altitude variations, which directly influence the captured images. In order to address this problem, we proposed a Convolutional Neural Network (CNN) architecture for the classification of three linear distortions common in UAV flight: rotation, translation and perspective transformations. To train and test our CNN, we used two mosaics that were divided into smaller individual images and then artificially distorted. Results demonstrate the potential of CNNs for solving possible distortions caused in the images during UAV flight. Therefore this becomes a promising area of exploration.
Palavras-chave:
Convolutional Neural Networks, Precision Agriculture, Unmanned Aerial Vehicle, Linear Distortions, Image Processing
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A. Milella, G. Reina, and M. Nielsen, "A multi-sensor robotic platform for ground mapping and estimation beyond the visible spectrum," Precision agriculture, vol. 20, no. 2, pp. 423–444, 2019.
T. Kataoka, T. Kaneko, H. Okamoto, and S. Hata, "Crop growth estimation system using machine vision," in Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), vol. 2. IEEE, 2003, pp. b1079–b1083.
S. Sankaran, L. R. Khot, C. Z. Espinoza, S. Jarolmasjed, V. R. Sathuvalli, G. J. Vandemark, P. N. Miklas, A. H. Carter, M. O. Pumphrey, N. R. Knowles et al., "Low-altitude, high-resolution aerial imaging systems for row and eld crop phenotyping: A review," European Journal of Agronomy, vol. 70, pp. 112–123, 2015.
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J. M. Turner, "Economic potential of unmanned aircraft in agricultural and rural electric cooperatives," Ph.D. dissertation, 2016.
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D. DeTone, T. Malisiewicz, and A. Rabinovich, "Deep image homography estimation," arXiv preprint arXiv:1606.03798, 2016.
F. Erlik Nowruzi, R. Laganiere, and N. Japkowicz, "Homography estimation from image pairs with hierarchical convolutional networks," the IEEE International Conference on Computer in Proceedings of Vision Workshops, 2017, pp. 913–920.
R. C. Gonzalez, R. E. Woods et al., "Digital image processing," 2002.
Y. L. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, "Deep learning for visual understanding: A review," Neurocomputing, vol. 187, pp. 27–48, 2016.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazaré, T. Bui, and J. Collomosse, "Everything you wanted to know about deep learning for computer vision but were afraid to ask," in SIBGRAPI Tutorials. IEEE Computer Society, 2017, pp. 17–41.
Y. LeCun, Y. Bengio, and G. E. Hinton, "Deep learning," Nature, vol.521, no. 7553, pp. 436–444, 2015.
D. Scherer, A. C. Müller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," in Artificial Neural Networks ICANN 2010 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III, ser. Lecture Notes in Computer Science, vol. 6354. Springer, 2010, pp. 92– 101.
A. P. Marcos, N. L. S. Rodovalho, and A. R. Backes, "Coffee leaf rust detection using genetic algorithm," in 2019 XV Workshop de Visão Computacional (WVC). IEEE, 2019, pp. 16–20.
——, "Coffee leaf rust detection using convolutional neural network," IEEE, 2019, in 2019 XV Workshop de Visão Computacional (WVC). pp. 38–42.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, "Tensorow: A system for large-scale machine learning," 2016.
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A. R. de Geus, A. R. Backes, and J. R. Souza, "Variability evaluation of cnns using cross-validation on viruses images." in VISIGRAPP (4: VISAPP), 2020, pp. 626–632.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, "Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size," arXiv:1602.07360, 2016.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
Publicado
07/10/2020
Como Citar
SILVA, Leandro; D. JÚNIOR, Jocival; SANTOS, Jean; MARI, João Fernando; ESCARPINATI, Maurício; BACKES, André.
Classification of UAVs' distorted images using Convolutional Neural Networks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 98-103.
DOI: https://doi.org/10.5753/wvc.2020.13488.
