A Temperature Identification Method Based on Chromaticity Statistical Features of Raw Format Visible Image and K-nearest Neighbor Algorithm
编号:65 访问权限:仅限参会人 更新:2020-10-29 22:46:22 浏览:218次 口头报告

报告开始:2020年11月02日 09:30(Asia/Shanghai)

报告时间:15min

所在会场:[E] Electrotechnical Theory and New Electromagnetic Technology [E2] Session 15 and Session 20

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摘要
Temperature monitoring is important to ensure the safe operation of power grid. The fault temperature is generally in the normal range; therefore, infrared detection is generally used. In this paper, the chromaticity information of raw format visible images of aluminum plate is studied. First, establish image library of aluminum plate at different temperatures, extract gray values of R, G, and B components of images according to the pixel arrangement of filter, and calculate gray frequency to obtain the gray frequency distribution. Then the statistical features of the gray frequency distribution are calculated and selected by Fisher discrimination. Finally, the selected features are combined into input feature vector, and the KNN algorithm is used for temperature identification. The results show that the accuracy of temperature prediction model is about 1.1 °C. The above results provide a new technical route for detecting normal temperature using visible image information.
关键词
fisher discrimination,gray frequency distribution,k-nearest neighbor algorithm,raw,temperature identification
报告人
Wenmao Li
Huazhong University of Science and Technology

稿件作者
Wenmao Li Huazhong University of Science and Technology
Qizheng Ye Huazhong University of Science and Technology
Zhe Yuan Huazhong University of Science and Technology
Yang He Huazhong University of Science and Technology
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重要日期
  • 会议日期

    11月02日

    2020

    11月04日

    2020

  • 10月27日 2020

    初稿截稿日期

  • 11月03日 2020

    报告提交截止日期

  • 11月04日 2020

    注册截止日期

  • 11月17日 2020

    终稿截稿日期

主办单位
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
承办单位
Huazhong University of Science and Technology
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