Making Darkness Contagious: Prior-Guided Synthesis for Low-Light Object Detection

Low-light object detection is constrained by scarce annotations, while preset synthesis rules can miss the degradation combinations found in real target images. We propose Contagious Degradation Synthesis (CoDeS), which learns reusable degradation “strains” from unlabeled low-light images and transfers them to labeled normal-light scenes. CoDeS represents target appearance with seven physically motivated descriptors and trains a conditional variational autoencoder (CVAE) to learn their joint variation conditioned on discovered illumination modes. Sampled configurations guide an image-formation-inspired online renderer that generates varied low-light views while preserving source boxes and categories. We further introduce a lightweight Low-Light Feature Adapter (LFA) and jointly train it with the SAM 3 detector on these views using inherited source annotations. Our approach requires no target training annotations or paired normal-light/low-light images, and synthesis is confined to training. Experiments on ExDark, NOD, and DARK FACE show significant improvements over pretrained SAM 3. Comparisons with alternative synthesis methods further show that CoDeS generates images that more closely match the local appearance and luminance statistics of real low-light data.