Infrared image segmentation method for power equipment based on improved cluster region growth

Abstract

For overheating defects of power equipment, the use of infrared technology is widely popular at present, which is less costly and more efficient than the traditional manual detection of thermal defects of power equipment. However, infrared images have the nature of concentrated intensity and low contrast, and picture segmentation has always been a difficult point. This paper proposes a combination of K-mean clustering and improved region growing algorithm, compared with the traditional region growing algorithm, solves the need to manually select the seed point to produce uneven gray scale and over-segmentation and under-segmentation, etc., through the K-mean clustering algorithm to automatically select the number of seeds as well as the seed node, and the introduction of Canny operator to reduce the error in order to achieve a better segmentation effect. Finally compare other algorithms fuzzy C-mean segmentation and fuzzy threshold segmentation.