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Object Detection in Aerial Navigation using Wavelet Transform and Convolutional Neural Networks: A First Approach

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Abstract

This paper proposes a first approach based on wavelet analysis inside image processing for object detection with a repetitive pattern and binary classification in the image plane, in particular for navigation in simulated environments. To date, it has become common to use algorithms based on convolutional neural networks (CNNs) to process images obtained from the on-board camera of unmanned aerial vehicles (UAVs) in the spatial domain, being useful in detection and classification tasks. CNN architecture can receive images without pre-processing, as input in the training stage. This advantage allows us to extract the characteristic features of the image. Nevertheless, in this work, we argue that characteristics at different frequencies, low and high, also affect the performance of CNN during training. Thus, we propose a CNN architecture complemented by the 2D discrete wavelet transform, which is a feature extraction method. Wavelet analysis allows us to use time-frequency information through a multiresolution analysis. Therefore, we have investigated the combination of multiresolution analysis, via wavelets, in conjunction with CNN architectures, to use the information in the wavelet domain as input to the training stage. The information improves the learning capacity, eliminates the overfitting, and achieves a better efficiency in the detection of a target. Also, our learning model was evaluated in the aerial navigation of a drone.

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ACKNOWLEDGMENTS

J. M. Fortuna-Cervantes is a doctoral fellow of CONACYT (México) in the program of “Ciencias Aplicadas” at IICO-UASLP. To INAOE for giving the facilities to carry out the research internship where part of this work was done.

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Authors and Affiliations

  1. Universidad Autónoma de San Luis Potosí, Facultad de Ciencias-IICO, Karakorum1470, Lomas 4a, 78210, San Luis Potosí, S.L.P., Mexico

    J. M. Fortuna-Cervantes & M. Mejía-Carlos

  2. Universidad Autónoma de San Luis Potosí, Coordinación Académica Región Altiplano Oeste, Salinas-Santo Domingo 200, 78600, Salinas, S.L.P, Mexico

    M. T. Ramírez-Torres

  3. Computer Science Department of the Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro 1, Sta. Ma. Tonantzintla, 72840, San Andrés Cholula, Puebla, Mexico

    J. Martínez-Carranza

  4. Universidad Autónoma de San Luis Potosí, Facultad de Ciencias, Chapultepec 1570, Privadas del Pedregal, 78295, San Luis Potosí, S.L.P., Mexico

    J. S. Murguía-Ibarra

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  1. J. M. Fortuna-Cervantes
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  2. M. T. Ramírez-Torres
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  3. J. Martínez-Carranza
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  5. M. Mejía-Carlos
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Correspondence to J. M. Fortuna-Cervantes, M. T. Ramírez-Torres, J. Martínez-Carranza, J. S. Murguía-Ibarra or M. Mejía-Carlos.

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Fortuna-Cervantes, J.M., Ramírez-Torres, M.T., Martínez-Carranza, J. et al. Object Detection in Aerial Navigation using Wavelet Transform and Convolutional Neural Networks: A First Approach. Program Comput Soft 46, 536–547 (2020). https://doi.org/10.1134/S0361768820080113

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