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Author:

Zhu, Ziqi (Zhu, Ziqi.) | Li, Jiafeng (Li, Jiafeng.) | Zhuo, Li (Zhuo, Li.) | Zhang, Jing (Zhang, Jing.)

Indexed by:

EI Scopus

Abstract:

Extreme weather recognition using GoogLeNet can achieve excellent performance, which is far superior to the conventional methods. However, the complexity of GoogLeNet is relatively high. Furthermore, for the small scale data, GoogLeNet usually cannot achieve the performance as the large scale data does. In this paper, a novel dual fine-tuning strategy is proposed to train the GoogLeNet model. Firstly, ILSVRC-2012 Dataset is applied to pre- train GoogLeNet and the initial model can be generated. Then, the initial model is fine-tuned on WeatherDataset, which is constructed in this paper, to obtain the model-1 for extreme weather recognition. Next, the structure of GoogLeNet is optimized by truncating operation to reduce the model size of GoogLeNet. And the truncated GoogLeNet is further fine-tuned on WeatherDataset to obtain the final recognition model. The experimental results have demonstrated that, compared with the original GoogLeNet, the recognition accuracy of the proposed method increases from 94.74% to 95.46%, which is improved by 0.72%. The model size of the optimized GoogLeNet is only 31.23% of original GoogLeNet. On CPU and GPU implementation, the proposed method processes the images 1.39 times faster and 2.44 faster than the original GoogLeNet respectively. In summary, the proposed method is better than the original GoogLeNet in the three aspects of recognition accuracy, recognition speed and model size. © 2017 IEEE.

Keyword:

Computer programming Optimization Extreme weather Computer science

Author Community:

  • [ 1 ] [Zhu, Ziqi]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China
  • [ 2 ] [Li, Jiafeng]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhuo, Li]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China
  • [ 4 ] [Zhuo, Li]Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, China
  • [ 5 ] [Zhang, Jing]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China

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Year: 2017

Volume: 2017-December

Page: 1-7

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 27

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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