LLVIP
A visible-infrared paired dataset for low-light vision tasks like pedestrian detection, image fusion, and image-to-image translation.
What is LLVIP?
LLVIP is a visible-infrared paired dataset designed for low-light vision research, containing over 30,000 aligned image pairs with pedestrian annotations. It addresses the challenge of poor visibility in dark environments by providing thermal infrared data that highlights human subjects, enabling tasks like pedestrian detection, image fusion, and cross-modal translation. The dataset serves as a benchmark for developing and evaluating algorithms that leverage multimodal imaging.
Computer vision researchers and practitioners working on low-light applications, multimodal learning, pedestrian detection, or image fusion. It's particularly relevant for those developing algorithms for surveillance, autonomous driving, or night-vision systems.
LLVIP offers a unique large-scale collection of precisely aligned visible-infrared pairs with clean annotations, filling a gap in publicly available low-light datasets. Its inclusion of baseline implementations and tools lowers the barrier to entry, while the Kaggle competition fosters community engagement and benchmarking.
Overview
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
Use Cases
Best For
- Training pedestrian detection models for low-light surveillance systems
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