Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging

Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging

The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid development. Infrared image of power equipment contains a lot of noise, and the edge is fuzzy. In this paper, we propose a new infrared image segmentation method for power equipment by using linear spectral clustering and maximal similarity-based region merging under complex backgrounds. In this method, the linear spectral clustering algorithm (LSC) is used to segment the image into super-pixels, and the pixels with similar color and distance are clustered to the same center. The calculated OTSU threshold based on the global image is used to pre-label the background of each super-pixel block. The maximum similarity-based region merging algorithm (MSRM) is utilized to merge the super-pixel blocks. Meanwhile, it obtains the target equipment, the over-segmentation and under-segmentation rates are reduced effectively. Finally, the mathematical morphology algorithm is used to post-process the image. Experimental results show that, compared with other algorithms, this new method can obtain more accurate and complete target equipment under complex background.