Forest Fire Thermal Infrared Image Segmentation Based on K-V Model

With the growing problem of forest fires, thermal infrared imaging technology is gradually applied to monitor and control forest fires. The segmentation of thermal infrared images is of great significance as an important part of this technology. This paper proposes an image segmentation model based on K-means clustering and variational (K-V model), which is used to alleviate the problem that the forest fire thermal infrared image is difficult to be segmented due to the presence of smoke masking, boundary blur of the fire area and regional dispersion of the fire area. Experiments are on a data set obtained by transforming the forest fire thermal infrared images collected on the Internet. This paper tests the running time and qualitative segmentation results of the proposed K-V model, and obtains convincing performance.