Camouflaged object detection based on context-aware and boundary refinement

Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning models in recent years. However, the context and boundary information have not been efficiently used simultaneously in the existing COD methods, leading to inferior detection for large camouflaged objects, occluded objects, multiple and small objects, and objects with rich boundaries. Therefore, to effectively enhance the performance of COD, we propose a novel camouflaged object detection model, i.e., context-aware and boundary refinement (CABR). Specifically, CABR mainly consists of three modules: the global context information enhanced module (GCIEM), the attention-inducing neighbor fusion module (AINFM), and the boundary refinement module (BRM). GCIEM is designed to fully capture the long-range dependencies to obtain rich global context information to completely detect large objects and occluded objects. AINFM is capable of adaptively fusing adjacent layers to focus on the global and local context information simultaneously to improve the detection performance of multiple and small camouflaged objects effectively. BRM can refine the boundaries by utilizing the spatial information in low-level features and suppressing the non-camouflage factors to detect camouflaged objects with rich boundaries effectively. Quantitative and qualitative experiments are conducted on four benchmark datasets, and the experimental results demonstrate the effectiveness of our CABR with competitive performance to existing state-of-the-art methods according to most evaluation metrics.