Boosting edge detection via Fusing Spatial and Frequency Domains

Abstract

Deep learning-based edge detection methods have shown great advantages and obtained promising performance. However, most of the current methods only extract features from the spatial (RGB) domain for edge detection and the information that can be mined is limited. As a result, they could not work well for the scenarios where the object is similar in color to the background. To combat this challenge, we propose a novel edge detection method by incorporating the features of both spatial and frequency domains, named Fusing Spatial and Frequency Domains (FSFD). A Frequency Perception (FP) module is constructed to extract the edges of objects in the frequency domain, which can avoid the indistinguishable situation in the spatial domain due to similar colors. A Multi-Scale Enhancement (MSE) module is designed to learn multi-scale feature in spatial domain, enabling the model to perceive edges of small objects. Spatial-Frequency Fusion (S2F) module is further introduced to fuse the features of spatial and frequency domains using an online learning manner. Adequate experiments are conducted on popular BSDS500, NYUDV2, and Multicue datasets. The results show that our method can outperform other methods as the state-of-the-art when dealing with the problem of edge detection. The codes will be released on https://github.com/JingDongdong/FSFD.