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.
Person classification leveraging Convolutional Neural Network for obstacle avoidance via Unmanned Aerial Vehicles
