改进Chan-Vese模型的电力设备红外图像分割算法

针对电力设备在线监测系统中红外图像分割效果差，速度慢等问题，提出一种改进的Chan-Vese模型的红外图像分割算法。首先，通过引入边缘能量项，一方面增强模型的局部控制能力，另一方面有效抑制了轮廓偏移。其次，利用径向基函数取代了传统的长度正则项，简化了计算。然后，通过引入内部能量项省去初始化过程，节省了算法的运行时间。经实验验证，Dice重合率（Dice similarity coefficient, DSC）平均值为0.9808，错误分割率（ratio of segmentation error, RSE）平均值为0.025，算法运行时间比其他模型总体平均值低66.8%。改进后的Chan-Vese模型分割算法的Dice重合率和错误分割率等均优于GAC-CV、CV-RSF、区域型水平集和Multiphase-CV模型分割算法。

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.