## 2 Related Work  
The purpose of ordinary object detection [2] is to discover and locate the types and positions of all objects of interest in images or videos, which is a major problem in computer vision. Object detection can be practiced in multiple areas, for example, facial recognition, pedestrian detection, vehicle detection, and remote sensing detection. The task of object detection generally consists of two parts [5]: classification and localization. Classification refers to determining which predefined category an object belongs to, such as cats, dogs, or human beings. Positioning refers to determining the position of an object in an image or video, usually represented by a rectangular box called a bounding box. The disadvantage of object detection is that the object may appear at any position in the image, with different sizes, shapes, poses, and occlusion situations, and the background may be complex. Therefore, object detection requires strong feature extraction and representation capabilities, as well as effective search and filtering strategies. Existing ordinary object detection methods are, thus, not effective in detecting camouflage objects. For salient object detection (SOD), [PERSON] [6] proposed a new way in light fields. It combines a multi-modal and multi-level feature aggregation network, utilizing the complementary information of multi-view images, focus stacking, and depth maps in light field data to process objects. The information within the light field object was enriched, and it achieved good results, improving the performance. [PERSON] [7] proposed another way called BPFINet to handle the problem of salient object detection, which selected the most prominent object from the image. The method uses U-shaped structure features to gradually combine the low/high-level information with global information layer by layer. It also introduces a self-developed BAM module to improve the certainty of SOD and its accuracy. The creator of ERBANet [8] proposed a model for SOD that enhanced boundary perception and region partitioning, and utilized attention enhancement modules to improve detection performance, accuracy, and robustness. They proposed a model that solved the issue of SOD by using the depth of spatial dilated convolution based on spatial attention. This model can efficiently extract object information and mix feature extraction to expand the range of receptive fields, thereby improving the achievement and accuracy of SOD. [PERSON] [9] proposed a model that solved the issue of SOD by using a depth spatial dilated convolution based on spatial attention. This model can efficiently extract object information and mix feature extraction to expand the range of receptive fields, thereby improving the achievement and accuracy of SOD. [PERSON]'s [10] main contribution was to provide a new loss function for solving image segmentation problems. Starting from the contour of the image, using image optimization techniques, the \(L_{2}\) distance on the image contour is called region integration. The proposed new loss function obtained by utilizing image contours can be used alone or in combination with other loss functions. They also proved that the loss function could improve model performance and enhance training stability. [PERSON] [11] proposed a network structure for multi-channel SOD based on FPN, introducing a global feature module and utilizing control gates and pooling networks. They integrated the FPN network and SINet network into their own network, processed features of different scales through feature fusion, and then convoluted the two single-channel character maps to achieve dimensional maps. This method can extract some large and observed information from multiple channels and retain big parts in the feature map. [PERSON] [12] introduced a novel approach to salient object detection (SOD) through the development of CFPN, a method designed to effectively integrate features from various levels, thereby enhancing the accuracy and completeness of detection.