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Face Detection With Different Scales Based on Faster R-CNN
Wenqi Wu; Yingjie Yin; Xingang Wang; De Xu
Source PublicationIEEE Transactions on Cybernetics
2018-08
Issue0Pages:0
AbstractIn this paper, a position measurement system including drogue’s landmark detection and position computation for autonomous aerial refueling of UAVs is proposed. A multi-task parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue’s two salient parts’ landmark detection. Considering the drogue target’s geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy which fuses the measurement results of drogue’s different parts is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obvious lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.
KeywordDeep Convolutional Neural Network (Dcnn) Deep Learning Face Detection Faster R-cnn
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21698
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorYingjie Yin
Recommended Citation
GB/T 7714
Wenqi Wu,Yingjie Yin,Xingang Wang,et al. Face Detection With Different Scales Based on Faster R-CNN[J]. IEEE Transactions on Cybernetics,2018(0):0.
APA Wenqi Wu,Yingjie Yin,Xingang Wang,&De Xu.(2018).Face Detection With Different Scales Based on Faster R-CNN.IEEE Transactions on Cybernetics(0),0.
MLA Wenqi Wu,et al."Face Detection With Different Scales Based on Faster R-CNN".IEEE Transactions on Cybernetics .0(2018):0.
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