Object Detection under Low-light Conditions via Degradation Learning Driven by Foundation Models

The previous approach tackled object detection challenges in low-light scenes by training on images captured in such conditions. However, the limited availability of annotated data in such environments has impeded the development of specialized detectors. As a result, current detectors continue to struggle with images degraded by low illumination. To overcome these limitations, a novel framework for low-light image generation is proposed. These generated images alter only the illumination conditions while preserving the original content, thereby enabling fine-tuning of object detectors. The framework incorporates a trainable degradation module and integrates two frozen Foundation Models: CLIP and a low-light image enhancement network. Under the guidance of CLIP, the degradation module transfers low-light characteristics from textual descriptions to well-lit images by aligning embeddings in a shared vision-language space. This process effectively simulates realistic low-light conditions. With the assistance of the low-light enhancement network, the generated low-light images are recovered. By enforcing similarity between these enhanced versions and the original well-lit counterparts in both the spatial and frequency domains, the content of the generated low-light images is effectively preserved. Extensive experiments on real-world low-light datasets show that fine-tuning detectors using the generated low-light images significantly improves detection performance, even when only well-lit images are available. This validates the effectiveness of the proposed method.