IDAM-RAW: a sensor-aware RAW imaging framework for low-light perception via illumination decoupling and adaptive feature modulation

Abstract

Low-light object detection remains fundamentally challenging due to the intrinsic misalignment between physical imaging characteristics and detection-oriented feature representations under extreme illumination degradation. This misalignment originates from the inconsistency between sensor-level signal formation and downstream representation learning, leading to unstable feature distributions and degraded detection performance. Existing approaches either rely on enhancement in the sRGB domain or directly learn from RAW data, yet both struggle to effectively bridge this gap. In this paper, we propose IDAM-RAW, a unified RAW-domain framework that bridges physical imaging processes and detection-oriented representation learning through task-driven end-to-end optimization. Specifically, DetISP maps RAW measurements to detection-friendly features without relying on fixed hardware ISP processing. The Residual Illumination Decoupling Module progressively reduces illumination-related variations in feature space and stabilizes optimization, whereas the Adaptive Feature Modulation Module suppresses interference propagation and enhances target-related responses across multiscale features. To support evaluation, we construct CR7-RAW, a real-world low-light bimodal dataset with spatially paired RAW and RGB observations, providing a new benchmark for RAW-based perception tasks. Extensive experiments on LOD, CR7-RAW, and BDD-Night demonstrate that IDAM-RAW achieves consistent performance gains across the evaluated datasets. Averaged over three independent random seeds, IDAM-RAW improves mAP@50 from 40.3 to 72.7 on LOD while maintaining consistent improvements on CR7-RAW and BDD-Night, supporting its effectiveness and cross-dataset robustness.