Object detection based on RGC mask R‐CNN

Author:

Wu Minghu12,Yue Hanhui12,Wang Juan123,Huang Yongxi12,Liu Min12,Jiang Yuhan12,Ke Cong12,Zeng Cheng12

Affiliation:

1. Hubei Key Laboratory for High‐Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of TechnologyWuhan430068People's Republic of China

2. Hubei Power Grid Intelligent Control and Equipment Engineering Technology Research CenterWuhan430068People's Republic of China

3. Post‐doctoral Research Workstation, Wuhan Huaan Science and Technology Co., Ltd.Wuhan430068People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference30 articles.

1. 2014 IEEE Proc. Conf. Computer Vision and Pattern Recognition Columbus Ohio USA R. Girshick J. Donahue T. Darrell Rich feature hierarchies for accurate object detection and semantic segmentation 580 587

2. 2015 IEEE Proc. Int. Conf. Computer Vision Boston MA USA S. Gidaris N. Komodakis Object detection via a multi‐region and semantic segmentation‐aware CNN model 1134 1142

3. 2016 IEEE Proc. Conf. Computer Vision and Pattern Recognition Las Vegas NV USA S. Gidaris N. Komodakis LocNet: improving localization accuracy for object detection 789 798

4. SimonyanK. andZissermanA.: ‘Very deep convolutional networks for large‐scale image recognition’ 2014 arXiv preprint arXiv:1409.1556

5. 2016 Conf. and Workshop on Neural Information Processing Systems Barcelona Spain J. Dai Y. Li K. He R‐FCN: object detection via region‐based fully convolutional networks 379 387

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