Camouflaged object detection (COD) presents a persistent challenge in
accurately identifying objects that seamlessly blend into their surroundings.
However, most existing COD models overlook the fact that visual systems operate
within a genuine 3D environment. The scene depth inherent in a single 2D image
provides rich spatial clues that can assist in the detection of camouflaged
objects. Therefore, we propose a novel depth-perception attention fusion
network that leverages the depth map as an auxiliary input to enhance the
network's ability to perceive 3D information, which is typically challenging
for the human eye to discern from 2D images. The network uses a trident-branch
encoder to extract chromatic and depth information and their communications.
Recognizing that certain regions of a depth map may not effectively highlight
the camouflaged object, we introduce a depth-weighted cross-attention fusion
module to dynamically adjust the fusion weights on depth and RGB feature maps.
To keep the model simple without compromising effectiveness, we design a
straightforward feature aggregation decoder that adaptively fuses the enhanced
aggregated features. Experiments demonstrate the significant superiority of our
proposed method over other states of the arts, which further validates the
contribution of depth information in camouflaged object detection. The code
will be available at this https URL

本文提出了一种新颖的深度感知注意力融合网络，利用深度图作为辅助输入，增强网络感知三维信息的能力，从而改善伪装物体检测。通过使用特定的编码器提取色彩和深度信息，并引入深度加权的交叉注意力融合模块来动态调整深度和 RGB 特征图的融合权重。最后，采用简单而有效的特征聚合解码器自适应地融合改进的聚合特征。实验表明，我们提出的方法在伪装物体检测方面具有显著的优势，进一步验证了深度信息在其中的贡献。