Picture Management of Power Supply Safety Management System Based on Deep Learning Technology
Authors: 
Sun Yuwei, Zhu Jianbao, Ma Qingshan, Yu Xinchun, Shi Ye, Chen YuAuthors Info & Claims
Sun Yuwei, Zhu Jianbao, Ma Qingshan, Yu Xinchun, Shi Ye, Chen YuAuthors Info & ClaimsPages 71 - 75
Abstract
With the advent of the era of big data, power supply security management systems will get a lot of picture data. In the face of massive image data, this paper studies the image management technology based on convolutional neural network. Aiming at the high repetition rate of self built image database samples and the problem that many sample classes contain uncorrelated images, two algorithms are proposed to improve the quality of the database: de duplication and de uncorrelation. By using the depth convolution neural network, the Embedding represented by the corresponding image is taken, and the distance between Embedding is calculated in the Euclidean space to achieve the purpose of de duplication and de uncorrelation. In this paper, "time" and "accuracy" are used to evaluate the performance of de duplication and de uncorrelation algorithms. The comparison examples of some sample classes before and after removing repetition and before and after removing uncorrelation are shown. The Recall-value of the database after removing duplicate and uncorrelated is tested based on the GoogLe Netplus-model respectively, which proves the effectiveness of the two filtering algorithms and overcomes the complexity of the traditional filtering process.
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Index Terms
- Picture Management of Power Supply Safety Management System Based on Deep Learning Technology
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Published In
December 2019
99 pages
ISBN:9781450372640
DOI:10.1145/3377458
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- Wuhan Univ.: Wuhan University, China
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Association for Computing Machinery
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Published: 07 March 2020
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ICSCC 2019
ICSCC 2019: 2019 5th International Conference on Systems, Control and Communications
December 21 - 23, 2019
Wuhan, China
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