The intrinsic similarity between camouflaged objects and background environment makes camouflaged object detection (COD) task more challenging than traditional object detection task. Since the boundary of the camouflage object is difficult to determine, the existing COD method often cannot accurately identify the boundary details and complete structure of camouflage object. To solve these challenges, we propose a novel attention and boundary guided feature refinement network (ABNet) for improving the performance of COD. Specifically, ABNet mainly includes three main modules: multi-resolution feature enhancement module (MFEM), attention-induced edge-aware module (AIEM), and boundary-guide feature interaction module (BFIM). The MFEM is introduced to enhance the single-layer feature and maintain high-quality detailed information. Additionally, the AIEM is designed to model edge features effectively from the enhanced feature. Finally, the BFIM is incorporated to focus on the structural details of camouflaged objects, which aims to explore multi-level features between global and local contextual information simultaneously for facilitating more complete detection. Extensive experiments have proved the effectiveness of the proposed model, and our model demonstrates competitive performance compared to existing state-of-the-art models on four benchmark datasets.