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A novel classification method of wall in rural housing pictures based on adapnet network
Author(s): Xiaowei Xu; Wei Liu; Ye Tao; Xiaodong Wang; Jilong Wu
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Paper Abstract

Based on the pictures of rural housing buildings, the characteristics of housing for the poor are studied, and the appearance of wall is classified by the deep learning method. The degree of poverty is determined by the classification of wall characteristics. Using the transfer learning method, the ResNet101 network is combined with the AdaptNet network to train the house image set. The house pictures are classified using the trained model. Experiments show that the classification accuracy in the recognition of wooden walls and tile walls is improved.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731O (3 January 2020); doi: 10.1117/12.2557268
Show Author Affiliations
Xiaowei Xu, Ocean Univ. of China (China)
Wei Liu, Ocean Univ. of China (China)
Ye Tao, Qingdao Univ. of Science and Technology (China)
Xiaodong Wang, Ocean Univ. of China (China)
Jilong Wu, Ocean Univ. of China (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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