Hierarchical triple-branch network for camouflaged object detection via progressive feature refinement
摘要
Camouflaged object detection endeavors to recognize entities that blend seamlessly with their surroundings. Current methodologies struggle to efficiently integrate global context, local features and boundary details to produce precise predictions. Hence, we introduce HTBNet, a novel triple-branch architecture designed to extract and consolidate diverse information effectively, with the primary goal of enhancing the accurate identification of camouflaged targets. Specifically, the boundary exploration module extracts boundary information in a branch, while the token-enhanced localization module with spatial attention focuses on exact location information from three high-level feature layers in another branch. Subsequently, the boundary and positional information are fed into the triple attention cross fusion Module, employing reverse attention to extract crucial elements from the aforementioned features and facilitate the fusion of multi-scale features. Furthermore, the deep overlapping refinement module integrated a U-shaped residual architecture is incorporated to enhance fusion results, ultimately contributing to meticulous predictions. Experimental results demonstrate that the proposed method achieves state-of-the-art performance across three benchmark datasets. It improves the