关键词: 电力设备红外图像分割, Chan-Vese模型, 水平集演化, 高斯核函数

Abstract: Infrared image segmentation of power equipment is the basis of pattern recognition for power equipment and infrared fault diagnosis. The Chan-Vese model can effectively segment images with strong noise and blurred edges, but its segmentation speed is slow, and it can not eliminate the unrelated background effectively in the segmentation of power equipment infrared images. In this paper, an improved Chan-Vese model is proposed. A binary function is used as the initial level set function instead of the distance function, and multiple initial contours are applied. Moreover, the data term of the Chan-Vese model is simplified, and the length regularization term is replaced by a Gaussian kernel function. Due to these modifications, a much larger time step can be adopted in the iterative process and the curve evolution speed can be accelerated. Experimental results on power equipment infrared image segmentation prove that the new model has higher segmentation speed and better performance to eliminate the unrelated background compared with the traditional Chan-Vese model.

Key words: power equipment infrared image segmentation, Chan-Vese model, level set evolution, Gaussian