Transactions of Nanjing University of Aeronautics & Astronautics
Graph Guided Edge-Aware Learning for Camouflaged Object Detection via Swin Transformer
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
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.
Keywords:
Project Supported:
This work was supported in part by the National Natural Science Foundation of China （No.62127813）； the Joint Funds of the National Natural Science Foundation of China（No.U2341226）； and the Jilin Provincial Talent Special Project （No.20240602015RC）.
Clc Number:
TP391.41
Articles
Mobile website
