HYBRID AUTOMATED UAV TARGET RECOGNITION SYSTEM

Abstract

Accurate image classification is one of the core challenges in computer vision. At the annual AUVSI SUAS competition, this challenge is faced in the form of ground target classification from an unmanned aerial vehicle (UAV). Additionally, due to the constraints imposed by the UAV platform, the system design must consider factors such as size, weight, and power consumption. To meet performance requirements while respecting such limitations, the system was broken into two subsystems: an onboard subsystem and a ground based subsystem. This design allows the onboard subsystem, comprised of a DSLR camera and single-board computer, to capture ground target images and perform rudimentary target detection and localization. For further processing and to ultimately classify the targets in each image, data packets are sent to the ground-based subsystem via a 5 GHz wireless link. Convolutional networks are utilized on the ground to achieve state-of-the-art accuracy in classification.