Research on Component Identification for Electrical Equipment Based on Infrared Thermography
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摘要: 常见的电力设备有变压器、开关柜、断路器等，这些设备都由多个组件构成。通过这类设备的红外热成像实现了对其组件的识别。基于红外热成像信息量较少的特点，采用多种算法融合。首先是基于Lab模型采用改进的K-means聚类和形态学的结合，提取红外图像中的高温区域，充分保证了效率和可靠性。其次采用改进的SURF（speeded-up robust features）和感知哈希算法的结合，确定被提取区域中的三相组件。SURF的作用是将已知的电气设备可见光图像和被提取区域中所有的图像进行对比，找出红外图像中特征点匹配最多的区域。将其和其他红外区域进行对比，通过感知哈希算法找到其他区域中匹配度最高的两个区域，以此定位出红外图像中的三相组件。此研究适用于大量红外图像数据的识别定位，为基于红外成像的电气设备故障信息提取提供思路。Abstract: Common electrical equipment includes transformers, switchgears, and circuit breakers, which are composed of multiple components. In this study, the identification of these components was realized via infrared thermal imaging of such devices. Based on the characteristics of infrared thermal imaging with less information, a variety of algorithms have been used for fusion. First, based on the Lab model, a combination of improved K-means clustering and morphology was used to extract the high-temperature region in the infrared image, which guaranteed efficiency and reliability. Second, a combination of improved SURF and perceptual hash algorithms was used to determine the three-phase components in the extracted area. The role of SURF was to compare the visible image of the known electrical device with all the images in the extracted area to determine the area with the most matching feature points in the infrared image. Compared with other infrared regions, we found two regions with the highest matching degree in other regions via the perceptual hash algorithm to locate the three-phase devices in the infrared image. This study is applicable to infrared image recognition and positioning without a large number of image data sets and provides ideas for the extraction of fault information of electrical equipment based on infrared imaging.
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Keywords:
- image processing /
- infrared thermal imaging /
- K-means /
- SURF /
- perceptual hash algorithm
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图 1 某红外图片的RGB各分量的图像及直方分布图
Figure 1. Image and histogram of RGB components of an infrared picture
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图 2 某红外图片的a和b分量的图像及直方分布图
Figure 2. Image and histogram of the a and b components of an infrared image
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图 6 两种算法计算时长
Figure 6. The length of time required for the two algorithms to calculate
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图 11 其他类型设备三相的识别定位
Figure 11. Three-phase identification and positioning of other types of equipment
表 1 两种算法的分割精度
Table 1 Segmentation accuracy of two algorithms
K-means algorithm Proposed Figure 3 0.09 0.04 Figure 11(b) 0.17 0.01 Figure 11(d) 0.09 0.06 Figure 11(c) 0.13 0.02 Figure 11(a) 0.14 0.01 
下载: 导出CSV
表 2 特征点匹配结果
Table 2 Feature point matching result
Area No. 7 13 18 31 Others Proposed 9 2 18 2 0 SURF 2 0 4 2 0
下载: 导出CSV
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