Promoting camouflaged object detection through novel edge-target interaction and frequency-spatial fusion

The realm of camouflage object detection endeavors to precisely discern concealed targets that blend seamlessly into their surroundings. Recently, numerous research endeavors have converged on unraveling the disguise tactics employed by such objects, striving for efficient and accurate detection results. While these methods have garnered notable advancements in unveiling camouflaged targets, they still grapple with challenges posed by the high similarity between objects and their backgrounds, resulting in misidentifications, detection oversights, and the loss of intricate details. In light of these challenges, this paper introduces an innovative EF Net, aimed at elevating the integrity of target recognition and the finesse of detail capture through deepened interactions between E dge-target features and F requency-spatial information. Notably, recognizing that the edges of camouflaged objects harbor a wealth of intrinsic details and serve as crucial delimiters distinguishing them from their surroundings, we have devised a dual-path architecture, where an auxiliary Edge Detection (ED) branch collaborates seamlessly with the primary Object Segmentation (OS) branch. Within the OS branch, we meticulously crafted the Edge-induced Segmentation Refinement module (ESR), which ingeniously harnesses the refined edge information provided by the ED branch to bolster target segmentation accuracy and augment detail fidelity. Moreover, in the supporting ED branch, we propose the Structure-induced Edge Enhancement module (SEE), designed to accentuate edge information while suppressing irrelevant noise with the assistance of the OS branch, thereby ensuring the precise extraction of edge information. To further enrich the information on dual-path, we innovatively introduce a Frequency Decomposition Module (FDM), which decomposes images into low- and high-frequency components, tailored to enhance target segmentation and edge detection, respectively. Additionally, to ensure seamless integration of frequency-domain and spatial- domain information, we devised the versatile Frequency-Spatial Domain Mixer (FSDM), achieving precise information alignment and fusion of these two domains. Quantitative and qualitative experimental results demonstrate that our proposed EFNet significantly outperforms existing state-of-the-art methods on four widely used benchmark datasets.