Edge Attention Learning for Efficient Camouflaged Object Detection
Zijian Liu, Ping Jiang, Lixin Lin, Xiaoheng Deng
Abstract
Detecting camouflaged objects is expected to be a challenging task due to the hard-distinguihsed boundaries of targets. Although existing learning-based methods have concentrated on utilizing boundary information to enhance camouflaged object detection, the absence of boundary difficulty estimation causes them to treat all boundary regions as equal, thereby making it more challenging to distinguish high intrinsic similarity boundary regions. To address this issue, by filtering redundant information on easy boundaries, we have proposed Edge Attention Network (EANet) to extract informative boundary knowledge. Specifically, we propose an Edge-attention Guidance module to prevent misleading segmentation by extracting critical boundary features. Then, Progressive Recognition module is proposed to progressively generate boundary-informative. The experimental results on three real-world datasets have demonstrated that our EANet outperforms existing methods across all three mertrics, while maintaining low computation.