## 1 Introduction  
Image segmentation is a classic problem in the field of computer vision, and it is also the main step of image analysis. Image segmentation refers to the segmentation of an image into several disjoint parts according to grayscale, color, spatial texture, geometric shapes, etc., so that the parts with similar characteristics in the same area are clearly different from the parts in different areas. It is widely used in machine vision, medical imaging, object detection and other fields [1]. Segmentation accuracy is an important indicator for detecting the quality of image segmentation, but in the process of research, the main challenge faced by many researchers is to find a perfect solution to obtain higher segmentation accuracy. At present, segmentation algorithms are mainly divided into threshold-based method, region growth-based method, histogram-based method and edge detection-based method and so on [2]. Among them, the threshold segmentation method is widely used in image processing due to its simplicity, efficiency, and fast segmentation speed. The use of threshold-based methods means that it is necessary to find an optimal threshold or a set of optimal threshold combinations [3]. The traditional image thresholding algorithm is effective for image binarization, but for multi-level image thresholding, the computational complexity of exhaustive search will increase significantly due to the increase in the number of thresholds, resulting in lower computational efficiency. Therefore, in order to reduce the computational cost, many research scholars integrate the meta-heuristic intelligent optimization algorithm such as Genetic Algorithm (GA) [4], Differential Evolution algorithm (DE) [5], Particle Swarm Optimization algorithm (PSO) [6], Artificial Bee Colony Algorithm (ABC) [7], etc., into the problem of multi-threshold segmentation of images. A better solution was obtained, which improved the accuracy of image segmentation to a certain extent and reduced the time complexity. [PERSON] et al. [8] took Kapur entropy as the objective function, and applied it to the task of color image segmentation using ABC, and achieved certain results in terms of computing efficiency and image segmentation quality. In [9], DE is employed for solving thresholding based image segmentation problem using Otsu objective function. [PERSON] et al. [10] studied several segmentation algorithms,and concluded that the threshold based segmentation method has a good segmentation performance on images with obvious peaks and valleys. [PERSON] et al. [11] applied the fuzzy C-means (ARKFCM) clustering algorithm based on the adaptive regular kernel to the magnetic resonance (MR) brain image segmentation problem, initialized the sensitive cluster center value and combined the PSO algorithm to optimize the cluster centroid, and obtained more Good to achieve the segmentation effect. In the research of [PERSON] et al. [12] they proposed an improved Otsu method and an improved GA for image segmentation. First, a morphological weighted adaptive algorithm is used to reduce morphological noise, and then the improved GA is used to optimize the largest Otsu globally. The image segmentation function is the between-class variance function to obtain the optimal image segmentation threshold. In fact, image segmentation based on the combination of threshold method and optimization algorithm can not only reduce the computational time complexity but also improve the segmentation accuracy and obtain higher segmentation quality pictures. After conducting a lot of research on the segmentation results, combining intelligent algorithms with [PERSON]'s method seems to be a feasible and worthy method [3]. Therefore, in this article, DE is used in [PERSON]'s method to improve the quality of image segmentation. Common threshold segmentation algorithms, including the maximum entropy method, balanced histogram threshold method, Otsu method, and k-means clustering, are widely used in industrial applications.