GBNet: Gated Boundary-Aware Network for Camouflaged Object Detection

Camouflaged object detection involves identifying camouflaged objects visually blended into the surroundings, holding crucial significance in various visual applications. Existing methods primarily focus on leveraging boundary information to enhance camouflaged object detection. However, they often overlook the background interference near the object boundaries, which leads to coarse boundary predictions and results in suboptimal detection performance. In this paper, to address this problem, we propose GBNet, a gated boundary-aware network designed to enhance boundary precision and improve overall detection performance. Specifically, GBNet incorporates a boundary-enhanced module that selectively filters extraneous background information through a boundary gate block, ensuring the generation of high-quality boundary information. Additionally, a boundary-aware decoder is designed to enrich the representation ability of the decoder by injecting high-quality boundary features and aggregating contextual features. With meticulous design, GBNet excels in accurately segmenting camouflaged objects in challenging scenarios. Extensive experiments demonstrate that GBNet outperforms 19 state-of-the-art methods significantly across four widely-used benchmark datasets. The source code is publicly available at https://github.com/wooownn/GBNet.