Frequency Domain-Based Cross-Layer Feature Aggregation Network for Camouflaged Object Detection
Chunhong Ren, Anzhi Wang, Chengbang Yang, Jintao Wu, Minghui Wang · IEEE Signal Processing Letters · 2025
Despite the progress of existing techniques in Camouflaged Object Detection, there are still problems such as multi-target omission, small-object misjudgment, and insufficient localization and segmentation accuracy. With the advantage of image frequency domain transformation, high-frequency features can capture detailed information such as edges and textures of the image, while low-frequency features depict the overall outline of the image, improving the accuracy of camouflage object detection. Therefore, this paper proposes a Frequency Domain-Based Cross-layer Feature Aggregation Network (FCFANet), aiming to improve the problems of multi-target omission, small target loss, object localization deviation, and insufficient segmentation accuracy in complex scenes. FCFANet mainly consists of an Intra- and Inter-layer Enhancement Module (IEM) and a Frequency-Spatial Interaction Fusion Module (FSIFM). IEM reduces noise and enhances the feature representation, while FSIFM extracts frequency information, and enhances feature discrimination by complementary fusion of spatial and frequency domains, thus realizing the precise positioning and high-precision segmentation of camouflaged objects. Compared with 16 state-of-the-art(SOTA) methods, experiments show that FCFANet outperforms other SOTA methods on four benchmark datasets.