SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements
Haiyang Xie, Xi Shen, Shihua Huang, Qirui Wang, Zheng Wang
Abstract
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
BibTeX
@inproceedings{aaai2026_simrodasimplebas,
title = {SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements},
author = {Haiyang Xie and Xi Shen and Shihua Huang and Qirui Wang and Zheng Wang},
booktitle = {AAAI 2026},
year = {2026}
}