Spectral Residual Transformation for Thermal Defect Detection of Power Equipment
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摘要: 本文提出一种基于谱残差变换的电力设备热缺陷识别技术。首先，根据电力设备红外图像中自然背景的冗余特性和热缺陷目标的显著性特征来构建谱残差变换模型，对电力设备红外图像进行谱残差变换，生成具有显著性信息的热缺陷初始识别结图。然后，采用引导滤波技术对初始识别结果进行处理，联合利用红外图像中的温差信息和空间结构信息，提升热缺陷的识别率，生成最终识别结果图。实验结果表明：与其他传统热缺陷识别方法相比，本文所提出的方法在识别精度与识别效率上有显著优势，满足电力设备热缺陷带电检测的应用需求。Abstract: This study introduces a thermal defect detection technique for power equipment based on a spectral transformation model. First, the spectral residual transform model is constructed according to the redundancy of the natural background and significance of the thermal defect target in infrared images of power equipment. Then, the infrared image of the power equipment is transformed by spectral residuals to remove redundant image information of the natural background target, and a result map with significant information is generated. The experimental results show that compared with other traditional thermal defect detection methods, the proposed method has significant advantages in terms of recognition accuracy and efficiency and meets the application requirements of thermal fault detection of power equipment.
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图 8 有无引导滤波处理对诊断精度的影响
Figure 8. Diagnosis accuracy with and without the guided filtering step
表 1 不同识别方法的AUC指标
Table 1 AUC values of different diagnosis methods
Test images RX LDP LRR SRT 1 0.9707 0.8312 0.8574 0.9969 2 0.9901 0. 9132 0. 9324 0.9990 3 0.9893 0. 9253 0. 9486 0.9993 
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表 2 不同识别方法的运行时间
Table 2 Running time of different detection methods
s Test images RX LDP LRR SRT 1 0.59 0.75 0.47 0.63 2 0.53 0.61 0.34 0.55 3 0.84 0.98 0.56 0.91
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