FFENet: A frequency fusion and enhancement network for camouflaged object detection

Abstract

The goal of camouflaged object detection (COD) is to accurately find camouflaged objects hidden in their surroundings. Although most of the existing frequency-domain based COD models can boost the performance of COD to a certain extent by utilizing the frequency domain information, the frequency feature fusion strategies they adopt tend to ignore the complementary effects between high-frequency features and low-frequency features. In addition, most of the existing frequency-domain based COD models also do not consider enhancing camouflaged objects using low-level frequency-domain features. In order to solve these problems, we present a frequency fusion and enhancement network (FFENet) for camouflaged object detection, which mainly includes three stages. In the frequency feature extraction stage, we design a frequency feature learning module (FLM) to extract corresponding high-frequency features and low-frequency features. In the frequency feature fusion stage, we design a frequency feature fusion module (FFM) that can increase the representation ability of the fused features by adaptively assigning weights to the high-frequency features and the low-frequency features using a cross-attention mechanism. In the frequency feature guidance information enhancement stage, we design a frequency feature guidance information enhancement module (FGIEM) to enhance the contextual information and detail information of camouflaged objects in the fused features under the guidance of the low-level frequency features. Extensive experimental results on the COD10K, CHAMELEON, NC4K and CAMO datasets show that our model is superior to most existing COD models.