Boundary-Aware Camouflaged Object Detection via Spatial-Frequency Domain Supervision
Penglin Wang, Yaochi Zhao, Zhuhua Hu · Electronics · 2025
Camouflaged object detection (COD) aims to detect objects that seamlessly integrate with their surrounding environment and are thereby intractable to distinguish from the background. Existing approaches face difficulties in dynamically adapting to scenarios where the foreground closely resembles the background. Additionally, these methods primarily rely on single-domain boundary supervision while overlooking multi-dimensional constraints, leading to indistinct object boundaries. Inspired by the hawk’s visual predation mechanism, namely, global perception and local refinement, we design an innovative two-stage boundary-aware network, namely, SFNet, which relies on supervision in the spatial-frequency domains. In detail, to simulate the global perception mechanism, we design a multi-scale dynamic attention module to capture contextual relationships between camouflaged objects and surroundings and to enhance key feature representation. In the local refinement stage, we introduce a dual-domain boundary supervision mechanism that jointly optimizes boundaries in frequency and spatial domains, along with an adaptive gated boundary guided module to maintain global semantic consistency. Extensive experiments on four camouflaged object detection datasets demonstrate that SFNet surpasses state-of-the-art methods by 4.1%, with lower computational overhead and memory costs.