Adaptive Kernel Selection Module Combined With Feature Enhanced Perception Network for Camouflaged Object Detection

Camouflaged object detection plays a crucial role in applications such as automatic sorting and defect inspection in industrial production, yet existing methods often struggle to flexibly capture features of diverse shapes, orientations, and scales due to their reliance on fixed receptive fields and rigid windowing schemes. To address these limitations, we propose a dual-branch joint network comprising a reference branch and a segmentation branch. The reference branch learns supplementary cues from salient objects that co-occur with camouflaged targets, guiding the segmentation branch toward more accurate delineation. Within the segmentation branch, we introduce three novel modules: 1) a deformable window interaction mechanism that replaces fixed-size transformer windows with learnable quadrilateral windows to adaptively extract features of arbitrary shape and orientation; 2) a feature enhancement perception module that fuses rich multiscale representations through parallel dilated convolutions at varying rates and channel-/spatial-attention mechanisms; and 3) a receptive field adjustment adaptive module that dynamically adjusts its receptive field size to balance sensitivity to fine details and global context. Comprehensive experiments on COD10 K, NC4K, CAMO, and R2C7K benchmarks demonstrate that our model outperforms the majority of current state-of-the-art approaches, while ablation studies and sensitivity analyses confirm the individual and combined effectiveness of our proposed components.