Camouflaged object detection (COD) is a promising yet challenging task that aims to segment objects concealed within intricate surroundings, a capability crucial for modern industrial applications. Current COD methods primarily focus on the direct fusion of high-level and low-level information, without considering their differences and inconsistencies. Consequently, accurately segmenting highly camouflaged objects in challenging scenarios presents a considerable problem. To mitigate this concern, we propose a novel framework called visual and semantic separation network (VSSNet), which separately extracts low-level visual and high-level semantic cues and adaptively combines them for accurate predictions. Specifically, it features the information extractor module for capturing dimension-aware visual or semantic information from various perspectives. The complementary information-selected module leverages the complementary nature of visual and semantic information for adaptive selection and fusion. In addition, the region disparity weighting strategy encourages the model to prioritize the boundaries of highly camouflaged and difficult-to-predict objects. Experimental results on benchmark datasets show the VSSNet significantly outperforms State-of-the-Art COD approaches without data augmentations and multiscale training techniques. Furthermore, our method demonstrates satisfactory cross-domain generalization performance in real-world industrial environments.

Detecting camouflaged objects is challenging due to their high visual similarity to surrounding environments in texture, color, and shape. Traditional Camouflaged Object Detection (COD) methods heavily rely on pixel-level annotations, which are costly and time-consuming. Scribble-Supervised COD (SSCOD) has emerged as a more efficient alternative by using sparse scribble annotations. However, it faces two critical challenges: sparse annotations, compounded by the extreme similarity between foreground and background, cause entangled feature representations and inaccurate predictions in unlabeled regions, and existing SSCOD methods lack robustness to scale variations, resulting in inconsistent predictions across scales. To alleviate these challenges, we propose the Mutual Iterative Refinement Network (MIR-Net), which introduces a cross-branch mutual refinement mechanism to disentangle and enhance foreground and background features. MIR-Net incorporates two novel modules: Background-driven Foreground Feature Enhancement (BFFE) and Foreground-driven Background Feature Enhancement (FBFE), which dynamically suppress irrelevant cues and amplify relevant features. Additionally, we introduce a Scale-Invariant Consistency (SIC) loss that enforces stable and accurate predictions across scales, improving the model’s robustness to scale variations. Comprehensive experiments on CAMO, COD10K, and NC4K datasets demonstrate that MIR-Net achieves state-of-the-art performance among SSCOD methods, surpassing all fully supervised CNN-based models and demonstrating competitive performance with fully supervised Transformer-based approaches. These results highlight MIR-Net’s potential to advance COD under weak supervision.