Cross-frequency aware network for camouflaged object detection with octave-transformer

Camouflaged object detection (COD) aims to identify objects that are fully blended into their surrounding environments. Current mainstream COD methods primarily focus on pixel-level optimization using convolutional neural networks (CNNs), without sufficiently addressing the significance of frequency interactions between candidate targets and noisy backgrounds, which are crucial for obtaining accurate edge and localization information. This paper explores the integration of multi-frequency features, and constructs a cross-frequency aware network (CFANet). The proposed network utilizes precisely learned deep-layer low-frequency features to guide other layers, achieving coarse localization. To further refine segmentation, the network employs both Transformer and CNN structures to facilitate the interaction and optimization of high- and low-frequency features at local and global levels. The model adopts a localization-guided decoder structure (LGS) that allows deep-layer low-frequency features to play a key role in guiding localization. The discussion module (DM) comprises three feature extraction experts, who engage in a teacher-student learning framework to derive more accurate deep-layer low-frequency features. In the Octave-Transformer module (OTM), the high- and low-frequency fused features based on octave convolution (OctConv) and Transformer deeply mine semantic features and detailed information. Compared to 33 existing state-of-the-art COD methods, the proposed network achieves overall superior performance across four benchmark datasets. Additionally, the network demonstrates excellent performance in other downstream tasks, such as polyp segmentation, surface defect detection. Our code is available at https://github.com/wkkwll-df/CFANet.