An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

With the transformation of the national energy and power sector, the steady advancement of intelligent power grid construction and the continuous improvement of the Ubiquitous Power Internet of Things technology framework, it has further requirements for realizing state comprehensive awareness and efficient processing of information data, and has been widely used in power equipment. The infrared image recognition technology, which has been widely used in thermal fault diagnosis of power equipment, also requires deeper research. For traditional Intuitionistic Fuzzy C-means (IFCM) algorithm for image segmentation is sensitive to the clustering center lead to low final clustering precision and detail, the time complexity and the high shortage. The paper puts forward a kind of applicable to power equipment of the infrared image segmentation based on space distribution information of Intuitionistic Fuzzy clustering algorithm. Non-target objects with high intensity and uneven image intensity in infrared image have strong interference to image segmentation. The proposed algorithm can effectively suppress the interference. Firstly, the gaussian model is introduced into the global spatial distribution information of power equipment to improve the IFCM. Secondly, the spatial operator optimization membership function of local spatial information is used to solve the problem of edge blurring and uneven image intensity. Through experiments on the data set containing 300 infrared images of power equipment, the relative regional error rate is about 10%, which is less affected by the change of fuzzy factor m. The effectiveness and applicability of this algorithm are verified, which is obviously better than other comparison algorithms.