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Proceedings Paper

A method for centroid extraction based on Faster-RCNN
Author(s): Xiaodan Zhang; Zhifeng Qiu; Luofang Jiao; Yu Yang; Bin Sun; Limei Xu
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Paper Abstract

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.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290T (14 February 2020); doi: 10.1117/12.2539308
Show Author Affiliations
Xiaodan Zhang, Univ. of Electronic Science and Technology of China (China)
Zhifeng Qiu, Univ. of Electronic Science and Technology of China (China)
Luofang Jiao, Univ. of Electronic Science and Technology of China (China)
Yu Yang, Univ. of Electronic Science and Technology of China (China)
Bin Sun, Univ. of Electronic Science and Technology of China (China)
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province (China)
Limei Xu, Univ. of Electronic Science and Technology of China (China)
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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