Camouflaged object detection (COD) is an arduous challenge due to the striking resemblance of camouflaged objects to their surroundings. The abundance of similar background information can significantly impede the efficiency of camouflaged object detection algorithms. Prior research in this domain has often relied on supplementary prior knowledge to guide model training. However, acquiring such prior knowledge is resource-intensive. Furthermore, the additional provided prior information is typically already embedded in the original image, but this information is underutilized. To address these issues, in this paper, we introduce a novel Camouflage Cues Guidance Network (CCGNet) for camouflaged object detection that does not rely on additional prior knowledge. Specifically, we use an adaptive approach to track the learning state of the model with respect to the camouflaged object and dynamically extract the cues of the camouflaged object from the original image. In addition, we introduce a foreground separation module and an edge refinement module to effectively utilize these camouflage cues, assisting the model in fully separating camouflaged objects and enabling precise edge prediction. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.

Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings. Although current deep learning methods have made significant progress in detecting camouflaged objects, many of them heavily rely on additional prior information. However, acquiring such additional prior information is both expensive and impractical in real-world scenarios. Therefore, there is a need to develop a network for camouflage object detection that does not depend on additional priors. In this paper, we propose a novel adaptive feature aggregation method that effectively combines multi-layer feature information to generate guidance information. In contrast to previous approaches that rely on edge or ranking priors, our method directly leverages information extracted from image features to guide model training. Through extensive experimental results, we demonstrate that our proposed method achieves comparable or superior performance when compared to state-of-the-art approaches.