Camouflaged Object Detection Based on Deep Learning with Attention-Guided Edge Detection and Multi-Scale Context Fusion

In nature, objects that use camouflage have features like colors and textures that closely resemble their background. This creates visual illusions that help them hide and protect themselves from predators. This similarity also makes the task of detecting camouflaged objects very challenging. Methods for camouflaged object detection (COD), which rely on deep neural networks, are increasingly gaining attention. These methods focus on improving model performance and computational efficiency by extracting edge information and using multi-layer feature fusion. Our improvement is based on researching ways to enhance efficiency in the encode–decode process. We have developed a variant model that combines Swin Transformer (Swin-T) and EfficientNet-B7. This model integrates the strengths of both Swin-T and EfficientNet-B7, and it employs an attention-guided tracking module to efficiently extract edge information and identify objects in camouflaged environments. Additionally, we have incorporated dense skip links to enhance the aggregation of deep-level feature information. A boundary-aware attention module has been incorporated into the final layer of the initial shallow information recognition phase. This module utilizes the Fourier transform to quickly relay specific edge information from the initially obtained shallow semantics to subsequent stages, thereby more effectively achieving feature recognition and edge extraction. In the latter phase, which is focused on deep semantic extraction, we employ a dense skip joint attention module to enhance the decoder’s performance and efficiency, ensuring accurate capture of deep-level information, feature recognition, and edge extraction. In the later stage of deep semantic extraction, we use a dense skip joint attention module to improve the decoder’s performance and efficiency in capturing precise deep information. This module efficiently identifies the specifics and edge information of undetected camouflaged objects across channels and spaces. Differing from previous methods, we introduce an adaptive pixel strength loss function for handling key captured information. Our proposed method shows strong competitive performance on three current benchmark datasets (CHAMELEON, CAMO, COD10K). Compared to 26 previously proposed methods using 4 measurement metrics, our approach exhibits favorable competitiveness.