Human vision exhibits remarkable adaptability in perceiving objects under camouflage. When color cues become unreliable, the visual system instinctively shifts its reliance from chrominance (color) to luminance (brightness and texture), enabling more robust perception in visually confusing environments. Drawing inspiration from this biological mechanism, we propose YCDa, an efficient early-stage feature processing strategy that embeds this “chrominance–luminance decoupling and dynamic attention” principle into modern real-time detectors. Specifically, YCDa separates color and luminance information in the input stage and dynamically allocates attention across channels to amplify discriminative cues while suppressing misleading color noise. The strategy is plug-and-play and can be integrated into existing detectors by simply replacing the first downsampling layer. Extensive experiments on multiple baselines demonstrate that YCDa consistently improves performance with negligible overhead as shown in the figure above. Notably, YCDa-YOLO12s achieves a 112% improvement in mAP over the baseline on COD10K-D and sets new state-of-the-art results for real-time camouflaged object detection across COD-D datasets.

Table: Performance comparison between YCDa-enhanced models and baselines on COD10K-D, NC4K-D, and CAMO-D datasets. Bold indicates the best-performing method within each baseline group.

cd YCDa/
python -m venv YCDa
source YCDa/bin/activate
pip install -e .

yolo detect train model=yolo12s-YCDa.yaml data=coco.yaml epochs=60 batch=32  device=0

yolo detect train model= data=yolo12s-YCDa.yaml epochs=300 pretrained=YCDa-12s-cocoPretrain.pt batch=16 patience=50 device=0

yolo detect val model=YCDa-12s-COD10K-D.pt data=COD10K-D.yaml device=0 split=test

🔗 Download YCDa-12s-COD10K-D Checkpoints

If you find YCDa useful in your research, please consider citing our work.