Abstract:
Abstract： When using conventional methods to deal with infrared image segmentation of electrical equipment， it is easy to have the shortcomings of poor segmentation accuracy and low computational efficiency in determing the optimal threshold. Therefore， a multi-threshold infrared image segmentation method based on improved slime mold algorithm optimizing Tsallis entropy is proposed. The optimal threshold of image segmentation is determined by using the heuristic search mechanism of slime mold algorithm to effectively reduce the time complexity of the algorithm. In the traditional slime mold algorithm， Henon chaotic mapping is introduced to optimize the initial population diversity， and a dynamic lens imaging opposite learning mechanism is designed to improve the search accuracy of the algorithm. Tsallis entropy is used to evaluate the quality of slime mold individuals， and an improved slime mold algorithm iteratively searches for the optimal image segmentation threshold. We construct experimental analysis using a common infrared image dataset of electrical equipment. The results show that compared with contrast model， the segmentation model achieves lower misclassification error， higher peak signal-to-noise ratio and structural similarity degree. The improved model demonstrates performance advantages in processing infrared image segmentation with non-uniform background and high noise.

Key words: Key words： slime mould algorithm, infrared image segmentation, image entropy, Henon chaos, lens imaging