DPSNet: A Detail Perception Synergistic Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that aims to identify objects that blend into their backgrounds. This has various applications in computer vision, including military surveillance, animal behavior analysis, and image editing. However, existing COD methods often face issues of ambiguous boundaries and inaccurate localization due to ineffective feature fusion and boundary guidance. To address these challenges, we propose a new hierarchical synergistic prediction framework, named detail perception synergistic network (DPSNet), which leverages an incremental strategy and a novel detail perception loss (DP Loss) for accurate COD. Specifically, our DPSNet consists of a baseline network and an enhanced network, generating preliminary and fine-grained predictions, respectively. To adapt to the morphological variations of camouflaged objects, we design a morphological awareness adaptation module (MAAM) that adaptively fuses inconsistent morphological and texture features. To enhance the contextual semantic feature representations and obtain clear predictions, we propose a semantic aggregation enhancement module (SAEM) that aggregates contextual semantic features with a deep aggregate group with different receptive fields. Furthermore, to guide the network in learning boundary information for more accurate predictions, we present a DP Loss that balances global information and hard samples. Without any additional tricks, extensive experiments demonstrate that our proposed DPSNet achieves real-time performance and significantly surpasses 37 state-of-the-art (SOTA) methods across four standard metrics on four widely used benchmark datasets. The code will be available at https://github.com/fxle/DPSNet.