在复杂环境下直接检测RAW图像，提升目标识别准确率。
Towards RAW Object Detection in Diverse Conditions
- 构建了7785张高分辨率RAW图像数据集，覆盖9种光照天气条件。
- 相比sRGB预训练，直接在RAW上预训练显著提升恶劣环境检测性能。
- 通过知识蒸馏缓解RAW图像噪声问题，无需额外预处理模块。
现有目标检测方法多基于sRGB输入，该格式由原始RAW数据经为可视化设计的ISP压缩而来，可能丢失对检测至关重要的信息，尤其在复杂光照与天气条件下。本文提出AODRaw数据集，包含7,785张高分辨率真实RAW图像，共135,601个标注实例，涵盖62类物体，覆盖9种不同光照与天气条件下的室内外场景。基于此数据集，我们建立支持RAW与sRGB检测的综合基准，评估现有方法表现。发现sRGB预训练会因域差距限制RAW检测潜力，因此主张直接在RAW域进行预训练。然而，由于相机噪声，RAW预训练更难学习丰富表征。为此，我们利用在sRGB上预训练的现成模型进行知识蒸馏，辅助RAW预训练。结果表明，在多样且恶劣条件下，性能获得显著提升，且无需额外预处理模块。代码与数据集已开源。
原文摘要 · Abstract (English)
Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light and weather conditions. We introduce the AODRaw dataset, which offers 7,785 high-resolution real RAW images with 135,601 annotated instances spanning 62 categories, capturing a broad range of indoor and outdoor scenes under 9 distinct light and weather conditions. Based on AODRaw that supports RAW and sRGB object detection, we provide a comprehensive benchmark for evaluating current detection methods. We find that sRGB pre-training constrains the potential of RAW object detection due to the domain gap between sRGB and RAW, prompting us to directly pre-train on the RAW domain. However, it is harder for RAW pre-training to learn rich representations than sRGB pre-training due to the camera noise. To assist RAW pre-training, we distill the knowledge from an off-the-shelf model pre-trained on the sRGB domain. As a result, we achieve substantial improvements under diverse and adverse conditions without relying on extra pre-processing modules. Code and dataset are available at https://github.com/lzyhha/AODRaw.
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