|Face Detection With Different Scales Based on Faster R-CNN|
|Wenqi Wu; Yingjie Yin; Xingang Wang; De Xu
|Source Publication||IEEE Transactions on Cybernetics
|Abstract||In 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.|
|Keyword||Deep Convolutional Neural Network (Dcnn)
|Corresponding Author||Yingjie Yin|
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.
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.
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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