## 2 Related Work
On the other hand, the biggest drawback of CNNs in processing image features for a long time is the loss of relevant structural information in the process of extracting images, and in terms of receptive fields, the actual receptive fields obtained by using CNNs alone will be much smaller than theoretical expectations. The current work focuses on how to design a robust algorithm to accurately detect and segment camouflaged objects in given images. There is limited research on the current state and future prospects of camouflaged object detection (COD), especially regarding statistics and analysis of deep learning-based algorithms. [PERSON] [25] comprehensively introduces all the research achievements in COD using deep learning. They provide in-depth analyses and discussions on model structures, learning paradigms, datasets, evaluation metrics, and performance comparisons, detailing all COD models involved and offering deep analysis and comparisons of some of the more effective models. In the context of sensor-based model research for camouflaged object detection, Transformers [26] were initially used as the backbone for encoders. SETR [27] marked the beginning of applying Transformers to semantic segmentation, and the introduction of Swin Transformer further accelerated the innovation of method models in the field of computer vision. SwinNet [28], by integrating RGB-D and RGB-T modes, achieved a cross-modal fusion model for salient object detection (SOD). It designed a dual-stream SwinT and used spatial alignment and channel recalibration modules to optimize features. DMT [29] was the first to introduce self-attention to the study of differentiating foreground and background information in images. HitNet [30] introduces a new cross-scale recursive method, significantly improving model evaluation metrics. Researchers then focused on exploring the combination of Transformers and CNNs to efficiently complement feature extraction and fusion, incorporating the idea of self-attention. UGTR [31] ingeniously combined CNNs and Transformers, explicitly using probabilistic representation models to learn the uncertainty of objects stored within the Transformer framework. CamoFormer [32] designed a COD algorithm inspired by multi-head self-attention, setting SwinT as its backbone and employing a multi-head self-attention masking strategy for feature aggregation. Currently, there is scant research using this approach, indicating substantial opportunities for further investigation in this field. Therefore, camouflaged object detection models that rely solely on completing CNN tasks often cannot search for information from a global perspective and cannot fully capture all contextual information. In 2017, [PERSON] [33] proposed a Transformer for natural language processing that utilized self-attention mechanisms to find and obtain certain dependency relationships, which were long-distance and could better obtain global information. However, learning contextual features directly from the entire image is very unwise and meaningless, as it is often disturbed by various obvious information. Therefore, like other camouflaged object research, we propose a method of exploring context based on attention to background features, fully combining the above advantages to achieve an improvement in the benchmark rate of camouflaged object detection.