用简单方法提升RAW图像目标检测，效果超主流方案。
SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements
- 引入可学习全局伽马变换，仅4参数提升特征表示
- 利用绿色通道增强局部细节，提升检测精度1.5%以上
- 轻量高效，适合实际部署，尤其对硬件敏感场景
多数视觉模型针对sRGB图像设计，而RAW数据在传感器阶段保留原始信息，跳过ISP处理可提升检测精度并优化硬件效率。然而，RAW目标检测面临训练数据少、像素分布不均和传感器噪声等挑战。为此，我们提出SimROD，一种轻量高效的RAW目标检测基线方法。引入全局伽马增强（GGE）模块，通过仅4个参数的可学习全局伽马变换，改善特征表达；同时利用绿色通道信号更丰富的特性增强局部细节，契合人眼感知与Bayer滤波器结构。在多个RAW目标检测数据集及检测器上实验表明，SimROD优于当前先进方法如RAW-Adapter和DIAP，且保持高效率。本工作凸显了RAW数据在真实场景目标检测中的潜力。代码已开源：https://ocean146.github.io/SimROD2025/
原文摘要 · Abstract (English)
Most visual models are designed for sRGB images, yet RAW data offers significant advantages for object detection by preserving sensor information before ISP processing. This enables improved detection accuracy and more efficient hardware designs by bypassing the ISP. However, RAW object detection is challenging due to limited training data, unbalanced pixel distributions, and sensor noise. To address this, we propose SimROD, a lightweight and effective approach for RAW object detection. We introduce a Global Gamma Enhancement (GGE) module, which applies a learnable global gamma transformation with only four parameters, improving feature representation while keeping the model efficient. Additionally, we leverage the green channel's richer signal to enhance local details, aligning with the human eye's sensitivity and Bayer filter design. Extensive experiments on multiple RAW object detection datasets and detectors demonstrate that SimROD outperforms state-of-the-art methods like RAW-Adapter and DIAP while maintaining efficiency. Our work highlights the potential of RAW data for real-world object detection. Code is available at https://ocean146.github.io/SimROD2025/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。