A Temperature Identification Method Based on Chromaticity Statistical Features of Raw Format Visible Image and K-nearest Neighbor Algorithm

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.