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.

中文翻译：

---

使用小波变换和卷积神经网络的空中导航目标检测：第一种方法

摘要

本文提出了一种基于小波分析的图像处理内部方法，该方法用于在图像平面中以重复模式和二进制分类进行对象检测，尤其是在模拟环境中导航时。迄今为止，使用基于卷积神经网络（CNN）的算法来处理从无人飞行器（UAV）的机载相机在空间域中获得的图像已变得普遍，这在检测和分类任务中很有用。CNN体系结构可以在不进行预处理的情况下接收图像，作为训练阶段的输入。这一优势使我们能够提取图像的特征。尽管如此，在这项工作中，我们认为，在低频和高频下的不同特征也会影响CNN在训练过程中的表现。从而，我们提出了一种CNN架构，辅以2D离散小波变换，这是一种特征提取方法。小波分析使我们能够通过多分辨率分析来使用时频信息。因此，我们研究了通过小波与CNN架构相结合的多分辨率分析的组合，以将小波域中的信息用作训练阶段的输入。该信息提高了学习能力，消除了过度拟合，并实现了更好的目标检测效率。此外，我们的学习模型是在无人机的空中导航中评估的。我们研究了通过小波与CNN架构相结合的多分辨率分析的组合，以将小波域中的信息用作训练阶段的输入。该信息提高了学习能力，消除了过度拟合，并实现了更好的目标检测效率。此外，我们的学习模型是在无人机的空中导航中评估的。我们研究了通过小波与CNN架构相结合的多分辨率分析的组合，以将小波域中的信息用作训练阶段的输入。该信息提高了学习能力，消除了过度拟合，并实现了更好的目标检测效率。此外，我们的学习模型是在无人机的空中导航中评估的。