用合成低光图像提升行人检测评估精度
Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
- 通过模拟传感器噪声生成逼真低光合成数据
- 模型在真实与合成低光数据上表现相似
- 适合自动驾驶安全评估与模型验证
真实世界中AI视觉模型的训练与测试受限于数据稀缺与分布不均，尤其在低光照区域样本稀少，难以进行有效评估。本文聚焦自动驾驶中黑暗环境下的行人检测，提出一种合成RAW图像增强技术，生成符合相机传感器噪声模型的低光样本。该方法能更连续地覆盖输入空间，提升基准测试的数据覆盖率。实验表明，模型在真实与合成低光数据上的性能指标相近，说明合成数据具有良好的真实性，可有效用于评估先进目标检测模型在低照度条件下的细粒度表现。
原文摘要 · Abstract (English)
Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and the low number of samples in low density regions makes it hard to run evaluations. Synthetic data can fill these gaps, providing us with a way to sample the input space more continuously and improve data coverage for benchmarks. Focusing on the autonomous driving safety-critical case of pedestrian detection in the dark, we show how synthetic low-light samples can be used to better characterize the performance of a state-of-the-art object detection model as a function of the scene illumination. We use a synthetic RAW image augmentation technique to generate low-light samples that match the noise model of the camera sensor. Performance metrics on real and synthetic low-light data are similar, indicating that the AI model finds it hard to distinguish between them.
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