Bi-directional Boundary-object interaction and refinement network for Camouflaged Object Detection
Jicheng Yang, Qing Zhang, Yilin Zhao, Yuetong Li, Zeming Liu · 2024
Due to the high intrinsic similarity between the camouflaged objects and the background, the predicted edge cue might be inaccurate or even erroneous. This will degrade the detection performance when such edge cues are directly integrated with the camouflaged object features. To solve this issue, we propose a bi-directional camouflaged object detection network to progressive complement and correct the boundary feature and the object feature in an interactive learning manner, thereby achieving prediction with fine structures. Specifically, we design a bi-directional transmission and correction (BTC) module, which contains the boundary and object interaction (BOI) module and the Feature Calibration Fusion (FCF) module, to explore the correlation between the boundary feature and the object feature and then provide the important complementary cues to refine each other, thereby progressively compensating the deficiencies and correcting the mistakes. Experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our network. Our code is publicly available at: https://github.com/Jcogito/BIRNet.