|STUMPH, BRIAN - Marquette University|
|VIRTO, MIGUEL - Marquette University|
|MEDEIROS, HENRY - Marquette University|
Submitted to: IEEE International Conference on Robotics and Automation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/31/2019
Publication Date: 8/12/2019
Citation: Virto, M.H., Medeiros, H., Tabb, A., Rice, K.B., Leskey, T.C. 2019. Detecting invasive insects with unmanned aerial vehicles. IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2019.8794116.
Interpretive Summary: To study invasive insects, entomologists use various means to track insects in the environment. The work in this paper describes a new automated method using a drone or small Unmanned Aerial System (sUAS). The insects are coated with a fluorescent powder and cameras on the sUAS view the insects via ultraviolet (UV) lasers. Algorithms described in the paper are able to autonomously detect the insects, which will lead to better tracking and understanding of insects and their behaviors.
Technical Abstract: A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state-of-the-art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.