Proceedings Article | 14 February 2020
KEYWORDS: Detection and tracking algorithms, Image segmentation, Digital filtering, Image processing, Feature extraction, Image filtering, Unmanned aerial vehicles, Target detection, Image processing algorithms and systems, Neural networks
In the MBZIRC 2020 competition, an Unmanned Aerial Vehicle (UAV) is required to intercept a moving balloon and put it into a specific location. The core of the task is to accurately identify the balloon’s centroid, which is also the purpose of this article. The process is composed of two sections: first identify the balloon candidate region based on Faster-RCNN, an end to end object detection algorithm, following a new method based on the color of balloon to extract the centroid finally. In terms of Faster-RCNN, images of balloon sample library are used to generate a number of target candidate regions by region proposal network(RPN), next the neural network is trained to generate a model, which can finally output the boundary box of the balloon, which we called candidate region. Next, in the candidate region, the process includes three parts: feature extraction, target segmentation and centroid marking. Improve the saturation to enhance the image, thus reducing the impact of reflection of sunlight. Then replace the color of the balloon to pure black, with the use of adaptive filtering to segment the balloon region preliminarily. Finally, to minimize the affections of noise, the largest connected region in the image is chosen to calculate its centroid position. Experimented with different backgrounds of images such as sky, grass, flowers and buildings, our method has gotten wonderful results, thus verifying the high accuracy of our method.
