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Dataset assetOpen Source CommunityImage ProcessingLow‑Light Object Detection

Low-light Object Detection (LOD) Dataset

This is a dataset for object detection under very low‑light conditions, containing various image types (RGB‑normal, RGB‑dark, RAW‑normal, RAW‑dark) and corresponding annotation files. It is used for research on object detection in extremely low‑light environments.

Source
github
Created
Nov 5, 2021
Updated
Dec 10, 2021
Signals
584 views
Availability
Linked source ready
Overview

Dataset description and usage context

Dataset Overview

Dataset Name

  • Low‑light Object Detection (LOD) Dataset

Dataset Source

  • Proposed by Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu at BMVC 2021.

Dataset Content

  1. Image Types

    • RGB‑normal: long‑exposure normal‑lighting sRGB images.
    • RGB‑dark: short‑exposure low‑lighting sRGB images.
    • RAW‑normal: long‑exposure normal‑lighting RAW images.
    • RAW‑dark: short‑exposure low‑lighting RAW images.

  2. Annotation Files

    • Each image is accompanied by an .xml annotation file (for all four image types).
    • Updated Dec 2021: an additional .xml file based on RAW‑normal annotations is provided.

Dataset Download

  • All files are available via Baidu Cloud; extraction code: “2021”.

Dataset Characteristics

  • Every short‑exposure image has a corresponding long‑exposure image as ground truth.
  • Captured with a Canon EOS 5D Mark IV; raw sensor data is provided.
  • Filenames are pure numbers; short‑exposure filenames equal long‑exposure filenames + 1.

Citation

  • If you use this dataset, please cite:
    • Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu, “Crafting Object Detection in Very Low Light”, BMVC 2021.
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