HDPNet: Hourglass Vision Transformer with Dual-Path Feature Pyramid for Camouflaged Object Detection

Jinpeng He, Biyuan Liu, Huaixin Chen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8627-8636

Existing camouflaged object detection methods often struggle with detecting small objects and fine object boundaries. To alleviate these issues we propose a novel hourglass vision Transformer with Dual-path Feature Pyramid (HDPNet). Specifically we construct an hourglass Transformer encoder that effectively captures the global semantic cues while extracting detailed feature maps at various scales preserving the spatial details and fine-grained boundaries of the camouflaged object. To ensure the preservation of essential cues of hourglass features we introduce a dual-path feature pyramid decoder (DPFD). This decoder performs coarse-to-fine feature fusion laterally mitigating the dilution of essential feature cues caused by the semantic gaps. In addition to further facilitate the local feature modeling in the encoder to mine the correlation between local features and global semantic cues from the camouflaged region we design a feature interaction enhancement module (FIEM). This module adopts a symmetric structure enables detailed appearance features and global semantic features to complement each other enhancing the model's ability to capture a wide range of fine-grained details. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms 25 existing methods across three challenging COD benchmark datasets particularly excelling in the detection of small objects and fine boundaries. The code is available at https://github.com/LittleGrey-hjp/HDPNet.