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

TL;DR

This paper introduces YCDa, a biologically inspired, efficient feature processing strategy that enhances real-time camouflaged object detection by decoupling color and luminance information, significantly improving accuracy with minimal overhead.

Contribution

YCDa is a novel plug-and-play attention mechanism that separates chrominance and luminance cues, boosting detection performance in camouflaged environments.

Findings

YCDa-YOLO12s achieves 112% mAP improvement over baseline.

YCDa improves detection accuracy across multiple datasets.

The method introduces negligible computational overhead.

Abstract

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 Fig. 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.