mlsanityData to 7 Oct 2026

SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements

arXiv 2503.07101 ↗First posted 10 Mar 2025cs.CVcited by 2

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Abstract (arXiv)

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/.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

34 of 52 references.

References · 34

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  11. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
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22 more
  1. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  2. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  3. 2018Learning to See in the Darkcited by 459
  4. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  5. 2018Unprocessing Images for Learned Raw Denoisingcited by 250
  6. 2021Invertible Image Signal Processingcited by 94
  7. 2019Recent Advances in Deep Learning for Object Detectioncited by 50
  8. 2021Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervisioncited by 43
  9. 2017Dirty Pixels: Towards End-to-End Image Processing and Perceptioncited by 42
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  11. 2023RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Imagecited by 31
  12. 2021ReconfigISP: Reconfigurable Camera Image Processing Pipelinecited by 30
  13. 2022DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognitioncited by 23
  14. 2022GenISP: Neural ISP for Low-Light Machine Cognitioncited by 22
  15. 2019Automatic ISP image quality tuning using non-linear optimizationcited by 20
  16. 2024RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Imagescited by 19
  17. 2021Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Imagescited by 18
  18. 2022Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environmentscited by 18
  19. 2022Efficient Visual Computing with Camera RAW Snapshotscited by 18
  20. 2023Raw or Cooked? Object Detection on RAW Imagescited by 14
  21. 2023HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Posescited by 12
  22. 2024You Think, You ACT: The New Task of Arbitrary Text to Motion Generationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modelingcited by 3
  2. 2026FreqAdapt: Frequency-Adaptive Processing for RAW Object Detectioncited by 0