ICUAS'17 Paper Abstract


Paper ThB2.5

Sevil, Hakki Erhan (The University of Texas at Arlington Research Institute (UTARI)), Dogan, Atilla (The University of Texas at Arlington), Subbarao, Kamesh (University of Texas at Arlington), Huff, Brian (The University of Texas at Arlington)

Evaluation of Extant Computer Vision Techniques for Detecting Intruder sUAS

Scheduled for presentation during the "See-and-avoid Systems - I" (ThB2), Thursday, June 15, 2017, 15:05−15:25, Salon AB

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords See-and-avoid Systems, Navigation


In this study, we investigate the feasibility of detecting small intruder aircraft through camera images obtained onboard a small unmanned aircraft. The research group (Small Unmanned Aerial Vehicle Laboratory) from NASA Langley Research Center flew a set of missions with their small UAS (sUAS) where one of those vehicles is outfitted with three 4K resolution cameras located at the tips of the wings and one at the nose. We utilize the MathWorks Computer Vision System Toolbox components to process the video data that are provided by NASA. We demonstrate the capabilities of COTS (Commercial Off-The-Shelf) state-of-art algorithms to detect the intruder aircraft in the video files. In the evaluation of these algorithms, various parameters of each algorithm are tuned to improve the detection performance in the case of the NASA flights, and the results are presented. The aim is to analyze performance of existing COTS state-of-art algorithms in detecting intruder aircraft from the camera images.



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