## 1 Introduction  
In complex and diverse search and rescue environments, such as indoor spaces, urban ruins, flooded areas, and wilderness, images captured by mobile devices are often blurred due to factors like shooting distance, motion blur, and lighting variations. This makes the precise image segmentation of injured individuals a significant challenge, yet it is crucial for the efficient allocation of resources and effective rescue operations [1]. Accurately segmenting disaster victims enables the rapid assessment of their injuries, ensuring that critical cases receive timely medical attention. Additionally, analyzing the posture of the injured and their surrounding environment provides valuable insights into their conditions, further improving the efficiency and safety of rescue efforts [2]. However, search and rescue environments present unique challenges: individuals may blend into intricate backgrounds, lighting conditions vary widely, and key features are often obscured by debris. These factors make it difficult for traditional image segmentation methods to perform effectively, as they struggle to distinguish between targets and backgrounds, often resulting in unstable segmentation quality and complicating the rescue process [3]. Traditional image processing techniques and deep learning methods are two primary approaches to semantic segmentation [4, 5]. In recent years, deep learning advancements have significantly outpaced traditional segmentation techniques, such as thresholding, edge detection, region growing, region splitting and merging, boundary segmentation, and clustering-based methods. Although these methods are effective for simpler images [6, 7], they often struggle with complex scenes. Conversely, deep learning-based segmentation hasbecome the preferred approach due to its enhanced accuracy and robustness, positioning it as a key focus in research and application. [PERSON], [PERSON] et al. [8] proposed an unsupervised multi-view K-Means clustering algorithm (U-MV-KM) that does not require initialization and can automatically determine the optimal number of clusters, enhancing its effectiveness and practicality in handling multi-view data. [PERSON], [PERSON] et al. [9] introduced a pseudo-label enhanced multi-view deep conceptual factorization fuzzy clustering algorithm (PE-MV-DCFCM), which effectively addresses the challenges of deep information extraction and the preservation of inter-view similarity by deeply integrating conceptual factorization and pseudo-label learning in multi-view data. [PERSON] et al. [10] introduced U-Net, a U-shaped encoder-decoder network that performs image segmentation by using the encoder to downsample and extract low-level features and the decoder to upsample, restoring image details and spatial dimensions. Despite its effectiveness, U-Net requires substantial computational resources due to a large number of parameters and slower processing speeds. Later, [PERSON] et al. [11] improved upon U-Net with MAGRes-UNet, a network that reduces noise ambiguity, enhances contextual information, and addresses the gradient vanishing issue. Following this, the Google Brain team [12] developed DeepLabv3+, which markedly improved segmentation accuracy and practicality, particularly in detail and edge processing, offering significant advances over DeepLabv1. [PERSON] et al. [13] proposed a novel segmentation network called MAGRes-UNet, which effectively addresses the issues of insufficient information fusion, the neglect of small-scale contextual information, and high computational complexity in the traditional U-Net for medical image segmentation. This is achieved by integrating multi-attention gate modules and residual blocks. [PERSON] et al. [14] proposed the Pyramid Scene Parsing Network (PSPNet), which utilizes average pooling at various scales to capture a broader scope of semantic information, enabling it to effectively extract both global and local features. PSPNet's capabilities make it ideal for complex, large-scale scenes, allowing it to focus on fine-grained details.