Document Type
Article
Publication Date
3-8-2021
Publication Source
IEEE Access
Abstract
Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and bio-inspired attack stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the bio-inspired attack stream corresponding with the original image and its flipped image, respectively. The output from the bio-inspired attack stream is then fused into the main stream’s result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts.
Inclusive pages
43290 - 43300
ISBN/ISSN
2169-3536
Publisher
IEEE
Volume
9
Peer Reviewed
true
Keywords
Streaming media, Object segmentation, Image segmentation, Image color analysis, Biomedical imaging, Training, Object recognition, Camouflaged object segmentation, bio-inspired network
Sponsoring Agency
The authors gratefully acknowledge the support of NVIDIA with the donation of GPU used for this research.
eCommons Citation
Yan, Jinnan; Le, Trung-Nghia; Nguyen, Khanh-Duy; Tran, Minh-Triet; Do, Thanh-Toan; and Nguyen, Tam V., "MirrorNet: Bio-Inspired Camouflaged Object Segmentation" (2021). Computer Science Faculty Publications. 216.
https://ecommons.udayton.edu/cps_fac_pub/216
Comments
Article is licensed under the Creative Commons Attribution License (CC-BY)