# Camouflaged Object Detection That Does Not Require Additional Priors
## 1 Introduction  
Deep learning has made significant strides in the field of object detection [1; 2; 3]. In object detection research, scholars primarily emphasize detecting objects in ordinary scenes [4]. However, as deep learning algorithms have become increasingly industrialized in recent years, the research community has started to emphasize the practicality of camouflaged object detection (COD). Specifically, COD is a research field that focuses on identifying camouflaged objects within images [5; 6]. Camouflaged objects can be categorized into two main types: natural and artificial camouflage [7]. Natural camouflage refers to how organisms utilize brightness, color, and body shape to conceal themselves in their environment [8]. Many species employ this strategy to survive, either to avoid predators or to capture prey [9; 10]. On the other hand, artificial camouflage involves using techniques such as paint, clothing, and accessories to create visual deception and conceal the object. This form of camouflage is commonly employed in military applications [11; 12], art [13], and other domains [14; 15]. The primary objective of camouflaged object detection is to generate a binary detection map for the camouflaged objects in the image [7]. However, detecting camouflage presents a challenge due to the visual similarity between the foreground and background [16; 17; 18]. Early camouflaged object detection algorithms drew inspiration from natural processes such as animal predation and used a two-part model of search and detection [19]. These early algorithms relied solely on basic images for training. Subsequent researchers concluded that training on image features alone was insufficient to identify and understandcamouflaged objects accurately [20]. Therefore, researchers introduced additional prior knowledge during model training. Numerous COD algorithms [20; 21; 22] have achieved commendable results by relying on the guidance of prior knowledge. While prior knowledge has made significant contributions, the drawback lies in the manual labeling requirement, incurring substantial costs. Meanwhile, we have discovered that satisfactory results can be achieved without relying on any prior knowledge, using only cues extracted from the original image to guide model training. As depicted in Figure 1, our prediction results outperform BGNet [20] and S-MGL [21], which exhibit unsatisfactory performance in predicting the boundaries of the object even with the incorporation of boundary priors during training. Furthermore, owing to the inadequate understanding of the camouflaged object's overall structure, there exists a significant issue of uneven pixel distribution in the prediction results. Based on the above issues, we propose a Camouflage Cues Guidance Network (CCGNet) for camouflaged object detection. Specifically, we introduce the adaptive feature fusion Module (AFFM), which vigilantly monitors the model's learning state and dynamically selects and integrates features to generate cues for camouflage. The camouflage cues comprise the available knowledge from each model layer, which can be employed to rectify and complement features across different layers and direct the model's attention toward the overall image structure. In order to effectively utilize the camouflage cues, we introduce the foreground separation module (FSM). This module leverages the camouflage cues to complement the multi-layer features, allowing the model to focus more on the overall structure of the camouflaged objects and effectively separate them from similar environments. Additionally, to refine the model's representation further, especially the edge information, we propose the edge refinement module (ERM). ERM achieves finer edge predictions by combining features with contextual information. As shown in Figure 1, in comparison to other COD algorithms, we achieve visually superior edge delineation results and mitigate uneven pixel distribution without the use of priors.