Electric Equipment Infrared Image Segmentation Method Based on Improved Chan-Vese Model

To address the problems of poor infrared image segmentation and slow speed in the online monitoring system of power equipment, an improved infrared image segmentation algorithm based on the Chan-Vese model is proposed. First, by introducing the edge energy term, the local control ability of the model is enhanced and the contour shift is effectively suppressed. Second, a radial basis function is used to replace the traditional length regularization term, which simplifies the calculation. Subsequently, the initialization process is omitted by introducing internal energy items, which reduces the running time of the algorithm. After the experimental verification, the average DSC was 0.9808, the average value was 0.025, and the algorithm running time was 66.8% lower than the overall average of the other models. The improved Chan-Vese model segmentation algorithms DSC and RSE are better than the GAC-CV, CV-RSF, regional level set, and multiphase-CV model segmentation algorithms.