A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Received Apr 28, 2017; Accepted Jun 18, 2017

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1. Introduction

Recent successes of deep learning techniques in solving many complex tasks by learning from raw sensor data have created a lot of excitement in the research community. However, deep learning is not a recent technology. It started being used back in 1971, when Ivakhnenko [1] trained an 8-layer neural network using the Group Method of Data Handling (GMDH) algorithm. The term deep learning began to be used during the 2000s, when Convolutional Neural Networks (CNNs), a computational original model from the 80s [2] but trained efficiently in the 90s [3], were able to provide decent results in visual object recognition tasks. At the time, datasets were small and computers were not powerful enough, so the performance was often similar to or worse than that of classical Computer Vision algorithms. The development of CUDA for Nvidia GPUs which enabled over 1000 GFLOPS per second and the publication of the ImageNet dataset, with 1.2 million images classified in 1000 categories [4], were important facts for the popularization of CNNs with several layers (109 to 1010 connections and 107 to 109 parameters). These deep models show great performance not only in Computer Vision tasks but also in other tasks such as speech recognition, signal processing, and natural language processing [5]. More details about recent advances in deep learning can be found in [6, 7].

An evidence of the suitability of deep learning for many kinds of autonomous robotic applications is the increasing trend in deep learning robot related scientific publications over the past decades, which is expected to continue growing [8].

Due to the versatility, automation capabilities, and low cost of Unmanned Aerial Vehicles (UAVs), civilian applications in diverse fields have experienced a drastic...