Jiangnan University

Linbai Xie

Gang Wang

Qigao Fan

Li Peng

Wentao Huang

Guofeng Yang

Jiwei Wen

Haichi Luo

Zhengqing Zhao

Yixin Zhu

Kaitao Bi

Hui Liu

Yueyang Li

A micro-robot magnetic drive device and a control method based on double closed loop three-dimensional path tracking are disclosed. The method includes: inputting a desired tracking path, obtaining current pose information of a magnetic micro-robot through a camera, and then calculating a position of a center of mass, an actual axial direction, coordinates of a desired position point with the shortest distance from the center of mass on a desired tracking path, and a tangent direction of this point; calculating a horizontal distance, a vertical distance, a direction angle error, and a pitch angle error of the two points according to the actual axial direction, the tangent direction, and disturbance compensation; and obtaining a required rotating magnetic field according to a designed position closed loop controller.

Micro-robot magnetic drive device and control method based on double closed loop three-dimensional path tracking

The present invention relates to the field of pedestrian detection, and particularly relates to a multi-scale aware pedestrian detection method based on an improved full convolutional network. Firstly, a deformable convolution layer is introduced in a full convolutional network structure to expand a receptive field of a feature map. Secondly, a cascade-region proposal network is used to extract multi-scale pedestrian proposals, discriminant strategy is introduced, and a multi-scale discriminant layer is defined to distinguish pedestrian proposals category. Finally, a multi-scale aware network is constructed, a soft non-maximum suppression algorithm is used to fuse the output of classification score and regression offsets by each sensing network to generate final pedestrian detection regions. Experiments show that there is low detection error on the datasets Caltech and ETH, and the proposed algorithm is better than the current detection algorithms in terms of detection accuracy and works particularly well with far-scale pedestrians.

Multi-scale aware pedestrian detection method based on improved full convolutional network

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Wuxi, China

Linbai Xie