YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection

Researchers propose YCDa, a new AI strategy for real-time camouflaged object detection that mimics human vision by separating color and brightness information. The method achieves 112% improvement in detection accuracy and can be easily integrated into existing AI detection systems with minimal computational overhead.

• →YCDa mimics human vision by decoupling color (chrominance) and brightness (luminance) information for better camouflaged object detection.
• →The strategy is plug-and-play and can replace the first downsampling layer in existing real-time detectors.
• →YCDa-YOLO12s achieved 112% improvement in mAP over baseline performance on COD10K-D dataset.
• →The method sets new state-of-the-art results for real-time camouflaged object detection across multiple datasets.
• →Implementation adds negligible computational overhead while consistently improving detection performance.