Detection and localization of birds for Bird Deterrence using UAS

Abstract Cherry, grape, and blueberry growers lose around 80 million dollars annually to bird damage in the state of Washington alone. Growers of a wide range of crops have a critical need for a safe and cost-effective method for persistent bird deterrence, which would lead to significantly reduced production costs. The goal of this research is to build a completely autonomous Unmanned Aerial System (UAS) to deter birds from the blueberry fields and grape vineyards. In the effort to build the UAS, the most vital part of its implementation is the vision system. The primary objective of this paper is to build a system to detect and localize birds. To detect birds, background subtraction algorithms have been used and the performance of various background subtraction algorithms are measured. It is found out that ViBe, a background subtraction algorithm, performs best in the bird detection scenario and provides an accuracy of 63%. In the quest of improving the bird detection speed and obtaining it in real time, a split window technique is used to improve the detection speed by 13%. To estimate the distance of the detected bird, a stereo vision system is proposed. With our current system, an accurate measure of the distance of the object is possible from 2 to 7 meters with an error accuracy of 30 centimeters. The long-term goal is to combine the efforts of the paper to successfully create a completely autonomous Smart Scarecrow that can safely, effectively and reliably scare and deter birds from high-value crops.