Object detection from images captured by Unmanned Aerial Vehicles (UAVs) are widely used for surveillance, precision agricultural, package delivery, aerial photography, among others. Very recently, a benchmark on object detection using UAVs collected images called VisDrone2018 has been released. However, large performance drop is observed when current state-of-the-art object detection approaches developed primarily for ground-to-ground images are directly applied on the VisDrone2018 dataset. For example, the best detection model on the VisDrone2018 has only achieved detection accuracy of 0.31 mAP, significantly lower than that of ground-based object detection. This performance drop is mainly caused by several challenges, such as 1) varying flying altitudes from 1000 feet to 10 feet, 2) different weather conditions like foggy, rainy and low-light 3) a wide range of camera viewing angles. To overcome these challenges, in this paper we propose to leverage a novel approach of adversarial training that aims to learn domain invariant features with respect to varying altitudes, viewing angles, weather conditions, and object scales. The adversarial training draws on “free” meta-data that comes with the UAV datasets providing information about the data themselves, such as heights, scene visibility, viewing angles, etc. We demonstrate the effectiveness of our proposed algorithm on the recently proposed UAVDT dataset, and also show it to generalize well when applied to a different VisDrone2018 dataset. We will also show robustness of the proposed approach to variations in altitude, viewing angle, weather, and object scale.
Machine learning based perception algorithms are increasingly being used for the development of autonomous navigation systems of self-driving vehicles. These vehicles are mainly designed to operate on structured roads or lanes and the ML algorithms are primarily used for functionalities such as object tracking, lane detection and semantic understanding. On the other hand, Autonomous/ Unmanned Ground Vehicles (UGV) being developed for military applications need to operate in unstructured, combat environment including diverse off-road terrain, inclement weather conditions, water hazards, GPS denied environment, smoke etc. Therefore, the perception algorithm requirements are different and have to be robust enough to account for several diverse terrain conditions and degradations in visual environment. In this paper, we present military-relevant requirements and challenges for scene perception that are not met by current state-of-the-art algorithms, and discuss potential strategies to address these capability gaps. We also present a survey of ML algorithms and datasets that could be employed to support maneuver of autonomous systems in complex terrains, focusing on techniques for (1) distributed scene perception using heterogeneous platforms, (2) computation in resource constrained environment (3) object detection in degraded visual imagery.
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