Infrared Image Segmentation for Electrical Equipment based on Fuzzy Inference

Fuzzy theory is considered to address the uncertainty of infrared image segmentation of electrical equipment, and a new algorithm based on fuzzy inference for infrared image segmentation of electrical equipment is proposed in this paper. First, the intensity, global fault probability, and local grayscale features were defined using the pixel grayscale of the fault region in the infrared image of the electrical equipment, Mahalanobis distance between pixel points, image center of mass, and image dilation operation. Subsequently, 27 fuzzy rules were formulated based on the fuzzy language values of the features, and an infrared image segmentation algorithm based on fuzzy inference was designed. Finally, the algorithm was compared with the traditional Otsu and FCM algorithms in terms of subjective and objective evaluation indexes. Further, the experimental results show that the segmentation accuracy and misclassification error of the proposed algorithm are better than those of the other two algorithms. The algorithm can filter out interference regions with high luminance in infrared images, and exhibits a better segmentation effect on infrared images with small luminance differences and small fault areas.