## 1 Introduction  
With the advancements in mobile internet technology, there is a growing demand for systems capable of uploading images across various application scenarios. Once these images are uploaded to the server, they are first converted to grayscale, followed by image segmentation to extract the effective region from the original image, thereby facilitating subsequent processing (Figure 1) [1]. Image segmentation technology is a crucial component in computer vision, serving as the foundation for numerous applications, including object recognition and understanding [2], medical imaging [3], autonomous systems and robotics [4, 5], augmented reality [6], virtual reality [7], video surveillance and security [8, 9], as well as environmental monitoring and agriculture [10]. However, image segmentation under complex background conditions remains challenging due to factors such as noise interference, uneven lighting, and blurred target edges [11, 12]. Image segmentation algorithms can be categorized into four primary techniques based on different processing methods: threshold-based segmentation [14], edge-based segmentation [15], region-based segmentation [16], and theory-based segmentation [17]. This paper focuses on threshold-based image segmentation methods, which utilize grayscale information and threshold extraction to segment images by setting one or more thresholds that classify pixels into different categories, effectively separating the background from the foreground [18]. Threshold-based segmentation is widely used in image processing today, offering several advantages, including stable performance, simple models, and ease of implementation. It is extensively applied across various fields of image segmentation processing [19; 20; 21; 22]. In complex images, the differences between the target and background regions necessitate the selection of an optimal threshold for image segmentation, which divides the image into target and background areas. The effectiveness of segmentation is directly influenced by the choice of this optimal threshold. Extensive research has been conducted on this issue, with scholars introducing meta-heuristic algorithms (MAs) to optimize processing and improve segmentation accuracy [23]. [PERSON] et al. [24] proposed a rapid extraction method for iterative threshold segmentation, enhancing algorithm accuracy. [PERSON] et al. [25] utilized manual threshold adjustment to ensure optimal image segmentation results. [PERSON] [26] introduced a variational mode decomposition (VMD) method that employs the OTSU algorithm to determine the optimal threshold, providing precision while overcoming the limitations of fixed thresholds. [PERSON] [27] introduced the concept of chaotic mapping to optimize the initialization step, while [PERSON] [28] proposed the Salp Swarm Algorithm (SSA) to determine optimal multi-level thresholds. [PERSON] [29] applied the improved whale optimization algorithm (IWOA) to solve the multi-threshold segmentation of grayscale images. [PERSON] [30] proposed a Tsallis entropy threshold segmentation method based on multi-scale and multi-directional Gabor transformation. [PERSON] et al. [31] utilized the equilibrium slime mould algorithm (ESMA) to search for multi-level thresholds using the entropy method, significantly improving segmentation accuracy. [PERSON] [32] proposed a novel image segmentation method based on genetic algorithms (GA) and entropy to enhance image segmentation quality. [PERSON] [33] further improved this by proposing a new multi-level threshold criterion based on Masi entropy for color satellite images, thereby enhancing algorithm efficiency. [PERSON] [34] developed an improved Cuckoo Search-Differential Evolution (CS-DE) variant to search for thresholds in the Masi entropy multi-level threshold method. [PERSON] [35] proposed  
Figure 1: The original images to be processed [13]. an improved Barnacle Mating Optimizer based on the logistic model and chaotic map (LCBMO), achieving significant breakthroughs in precision and efficiency.