Vision-Inspired Boundary Perception Network for Lightweight Camouflaged Object Detection

Abstract

Lightweight camouflaged object detection (COD) has garnered increasing attention due to its wide range of real-world applications and its efficiency on mobile devices. Existing lightweight COD methods typically attempt to utilize multi-scale fusion, frequency cues, and texture information to enhance the representation ability of lightweight backbone features. However, they still fall short in detecting precise and continuous object boundaries. To address this issue, we observe that two types of cells in the human visual system make great contributions to boundary perception. Motivated by this, we propose a boundary perception module (BPM) to enhance features with the awareness of fine-grained boundary, by mimicking the boundary perception process of aforementioned cells. In addition, we propose a bidirectional semantic enhancement module (BSEM) to effectively decode multi-level features in a lightweight manner. With BPM and BSEM, our proposed vision-inspired boundary perception network (BPNet) achieves superior performance against state-of-the-art methods and surpasses lightweight COD models by a large margin with the least parameters (3.64 M) and fastest speed (168FPS for the input size of 384 × 384).