Intrinsic Feature Decomposition for Domain Adaptive Low-Light Object Detection

Object detection under low-light conditions remains underexplored due to the challenge of learning discriminative representations across extreme illumination variations. We propose a domain-adaptive pretraining framework that learns noise-robust, illumination-invariant feature representations directly within the detector via feature-level decomposition. Inspired by Retinex theory, our approach treats the first-layer backbone feature as intrinsic structure and employs an auxiliary encoder-decoder during pretraining to factor features into intrinsic, illumination, and noise components. The auxiliary branch is discarded after pretraining, incurring no inference overhead. Using paired normal-dark images synthesized from COCO, we jointly optimize detection and decomposition losses to enforce intrinsic consistency. When fine-tuned on real-world low-light datasets, our pretraining consistently improves the COCO-pretrained baseline, with gains of 7.9 mAP on LOD, 4.2 on NOD, 3.7 on NuImages, 2.7 on BDD100K, 11.7 on ExDark, and 5.9 on DARK Face. These gains persist across detector architectures, including YOLO26-m and RT-DETR. Overall, these results show that embedding Retinex principles at the feature level effectively bridges the illumination gap for low-light detection, scales across model sizes and detector architectures, and preserves runtime efficiency. Our code will be publicly available.