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/.

翻译：大多数视觉模型专为sRGB图像设计，然而RAW数据在目标检测中具有显著优势，因其保留了传感器在ISP处理前的原始信息。通过绕过ISP，这不仅能提升检测精度，还能实现更高效的硬件设计。但RAW目标检测面临训练数据有限、像素分布不平衡及传感器噪声等挑战。为此，我们提出SimROD，一种轻量且高效的RAW目标检测方法。我们引入了全局伽马增强（GGE）模块，该模块仅用四个参数实现可学习的全局伽马变换，在提升特征表示的同时保持模型高效性。此外，我们利用绿色通道更丰富的信号来增强局部细节，这与人类视觉敏感性及拜耳滤波器设计相契合。在多个RAW目标检测数据集和检测器上的大量实验表明，SimROD在保持高效的同时，性能优于RAW-Adapter和DIAP等先进方法。我们的工作凸显了RAW数据在实际目标检测中的应用潜力。代码发布于https://ocean146.github.io/SimROD2025/。