Abstract
The enhanced grey wolf algorithm is merged with the corrosion algorithm, and a grey wolf corrosion optimization algorithm is proposed to precisely identify the boundaries within infrared images of power equipment. The refined grey wolf algorithm employs exponential entropy as its fitness function and incorporates an individual wolf selection mechanism, enabling binary segmentation with adaptively chosen thresholds and high resemblance to the original image. Simulation results of actual infrared images captured in substations reveal that, compared to conventional edge detection algorithms such as Sobel, Roberts, Prewitt, Log, and Canny, the algorithm proposed in this paper yields comprehensive and well-defined boundaries with a high degree of conformity to the original infrared image. This proposed algorithm demonstrates significant applicability in the domain of infrared image processing and target recognition for power equipment.
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