Abstract
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.
Highlights
The emergence and rapid development of remote sensing of UAVs (Unmanned Aerial Vehicle) has enabled remote sensing scientific research to move from macro to micro
The evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) performs more stable under noise interference conditions and improves the accuracy rate
For the intensity non-uniformity and non-high-intensity targets existing in the infrared image of the substation equipment, an intuitionistic fuzzy clustering method based on local information is proposed
Summary
The emergence and rapid development of remote sensing of UAVs (Unmanned Aerial Vehicle) has enabled remote sensing scientific research to move from macro to micro. This type of method combines local information with the intensity information of the image to overcome the lack of noise and non-uniformity, achieves certain results These improvements limit spatial information to local areas, which means that the effect of high-intensity non-target objects on image segmentation still exists. The IFCM algorithm pays more attention to the feature point clustering problem, which determines that the segmentation effect may not be ideal when the image has strong spatial correlation To this end, for the intensity non-uniformity and non-high-intensity targets existing in the infrared image of the substation equipment, an intuitionistic fuzzy clustering method based on local information is proposed. The main work of this paper includes: 1) Proposing a fuzzy clustering algorithm combined with global distribution information, which is used to segment the target of high-intensity non-power equipment existing in the image. The objective function by the Lagrange Multiplier method and comparing it with the traditional IFCM segmentation method
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