B2Net: Camouflaged Object Detection via Boundary Aware and Boundary Fusion
This work improves detection accuracy for camouflaged objects in images, which is important for applications like surveillance and wildlife monitoring, but it is incremental as it builds on existing boundary-guided approaches.
The paper tackles the problem of camouflaged object detection by addressing inaccurate edge priors in existing methods, proposing B2Net with boundary-aware modules to enhance boundary accuracy, and it outperforms 15 state-of-the-art methods on three benchmark datasets.
Camouflaged object detection (COD) aims to identify objects in images that are well hidden in the environment due to their high similarity to the background in terms of texture and color. However, existing most boundary-guided camouflage object detection algorithms tend to generate object boundaries early in the network, and inaccurate edge priors often introduce noises in object detection. Address on this issue, we propose a novel network named B2Net aiming to enhance the accuracy of obtained boundaries by reusing boundary-aware modules at different stages of the network. Specifically, we present a Residual Feature Enhanced Module (RFEM) with the goal of integrating more discriminative feature representations to enhance detection accuracy and reliability. After that, the Boundary Aware Module (BAM) is introduced to explore edge cues twice by integrating spatial information from low-level features and semantic information from high-level features. Finally, we design the Cross-scale Boundary Fusion Module(CBFM) that integrate information across different scales in a top-down manner, merging boundary features with object features to obtain a comprehensive feature representation incorporating boundary information. Extensive experimental results on three challenging benchmark datasets demonstrate that our proposed method B2Net outperforms 15 state-of-art methods under widely used evaluation metrics. Code will be made publicly available.