南京航空航天大学学报（英文版）2026，Vol.43Issue(4)：575-591,17.DOI:10.16356/j.1005-1120.2026.04.007
基于Swin Transformer的图引导边缘感知学习伪装目标检测
Graph Guided Edge-Aware Learning for Camouflaged Object Detection via Swin Transformer
摘要
Abstract
Compared with generic object detection,camouflaged object detection(COD)is more difficult due to its high similarity to the background.Most COD methods used convolutional neural network(CNN)in the past.In contrast,we note the great potential of Transformer in vision tasks,so we use Swin Transformer as the backbone to generate more robust multi-level features.Considering the complex characteristics between the camouflaged object and the background,we propose an edge-aware module(EAM)to generate a fine edge prior.In addition,the feature aggregation module(FAM)is used to fuse multi-level features,and the internal connections between edge points are investigated by feeding edge feature pixels into a graph convolutional network(GCN)to purify uncertain edge points,then guiding multi-level feature fusion operation.We reduce indistinguishable noise by cascading several FAMs and get prediction maps with sharp edges.Experimental results show that the proposed model can run at the real-time speed of 46 frames per second(FPS)on a single NVIDIA 2080Ti GPU.Comparing with 12 state-of-the-art COD methods on four public datasets,the proposed method outperforms other approaches.关键词
伪装目标检测/Swin Transformer/图卷积网络/边缘感知学习Key words
camouflage object detection(COD)/Swin Transformer/graph convolutional network(GCN)/edge-分类
信息技术与安全科学引用本文复制引用
王潇逸,李明岩,李彬,吴凡路,张明强,王冠,朱瑞,才华,付强..基于Swin Transformer的图引导边缘感知学习伪装目标检测[J].南京航空航天大学学报（英文版）,2026,43(4):575-591,17.基金项目
This work was supported in part by the National Natural Science Foundation of China(No.62127813) （No.62127813）
the Joint Funds of the National Natural Science Foundation of China(No.U2341226) （No.U2341226）
and the Jilin Provin-cial Talent Special Project(No.20240602015RC). （No.20240602015RC）
