###### Abstract
Convolutional networks can use GPUs for efficient computation to improve processing speed and propose a series of innovative model structures, such as VGG [3] and ResNet50 [4], which are already sufficiently mature. There has also been a focus on designing efficient feature fusion processors, the corresponding preferences of which can be set for feature extraction from images.However, at present, there are still two obvious problems with CNN-based feature extraction models. First, such models are not sensitive to global position information. This is because the extraction of information based on CNN feature models has a translation function, which is considered an advantage of CNN feature models. However, this weight sharing is a disadvantage for processing camouflaged object detection objects. Second, the information features extracted by the convolutional model only consider a portion of the extracted pixels during each operation. Generally speaking, the receptive fields of the model, built according to the convolutional neural network, will increase with the number of layers of the built model, that is, the deeper the layers are, the larger the range of receptive fields will be. Most skeleton networks built based on CNN now exchange information loss for a wider range of receptive fields. In order to try to recover the lost structural information, the general model structure needs to add a decoder to recover the lost information. However, there is currently no suitable method to recover all unnoticed information. Furthermore, the skeletal network constructed using CNN must encompass the entire image within its receptive field range. However, previous experiments show that this was only in theory, and the range of receptive field perception is not as large as expected. If we can build a network that can sense a large range of receptive fields while retaining relevant details, it may be useful for processing the task of camouflaged object detection depending on contextual information. Due to the significant limitation of simple convolutional networks, researchers have found in recent years that Transformers have great potential in handling contextual information problems. Camouflaged object detection is also an emerging research field in semantic recognition, which has been proposed upon the combination of deep learning and computer vision to process images and videos. Because it is very difficult to distinguish between the disguised object and the environment, it is, consequently, very difficult to process images with such contents. A camouflaged object detection network based on deep learning is generally structured into two parts, namely, a module for locating or obtaining camouflage object features and a module for segmenting object detection. The two modules interact with each other to fuse features and obtain the final camouflage component. Our proposed approach, based on EfficientNet-B7 and SWIN-T, combines a new overall architecture for feature extraction, including feature encoding with an attention flow mechanism and feature fusion, as well as feature decoding with dual tasks. The main improvements are listed as follows:* The feature fusion multi-path joint idea is added in the encoder section, which can better utilize cross-layer links to fuse multiple layers of information. Deep networks leveraging Transformers more effectively account for the contextual information of objects, upgrade the correctness of positions, and reduce noise. * A novel fusion module is introduced, establishing a connection through the attention mechanism between the encoding and decoding phases. It merges local and global messages, addressing the challenge of disparate fusion techniques across two modes. It proposes an attention weight that is completely different from the single mode, and combines salient features and residual connections to achieve cross-modal guided fusion, that is, the joint dense hop module.