MirrorNet: Bio-Inspired Camouflaged Object Segmentation

• Main Authors: Jinnan Yan, Trung-Nghia Le, Khanh-Duy Nguyen, Minh-Triet Tran, Thanh-Toan Do, Tam V. Nguyen
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Camouflaged object segmentation; bio-inspired network
• Online Access: https://ieeexplore.ieee.org/document/9371667/

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.