EPFDNet: Camouflaged object detection with edge perception in frequency domain

Abstract

Camouflaged object detection (COD) is a relatively new field of computer vision research. The challenge of this task lies in accurately segmenting camouflaged objects from backgrounds that are similar in appearance. In fact, the generation of reliable edges is an effective mean of distinguishing between the foreground and background of the image, which is beneficial for assisting in determining the location of camouflaged objects. Inspired by this, we design an Edge Encoder that decomposes features into different frequency bands adopting learnable wavelets and focuses on high-frequency components with sufficient edge details to extract accurate edges. Subsequently, the Feature Aggregation Module is proposed to integrate contextual features, which generates rough edge details by sensing the difference between two branch features and use this information to further refine our edge features. Furthermore, the Stage Enhancement Module is developed to enhance the features through reverse attention guidance and dilate convolution, which mines the detailed structural information of the camouflaged objects area by eliminating foreground. The superiority of our proposed method (EPFDNet) over the existing 17 state-of-the-art methods is demonstrated through extensive experiments on three widely used COD benchmark datasets. The code has been released at https://github.com/LitterMa-820/EPFDNet.