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Related Experiment Video

Updated: Sep 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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UrOAC: Urban objects in any-light conditions.

Francisco Gomez-Donoso1, Marcos Moreno-Martinez1, Miguel Cazorla1

  • 1University Institute for Computer Research, University of Alicante. PO Box 99, Alicante 03080, Spain.

Data in Brief
|May 5, 2022
PubMed
Summary

This study introduces UrOAC, a new dataset for urban object detection under diverse lighting. It enables AI systems to better detect traffic lights and crosswalks in low-light and nighttime conditions.

Keywords:
Low-light conditionsObject recognitionUrban environments

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Existing urban object detection methods fail in low-light conditions.
  • Current datasets lack diversity in lighting, limiting AI performance at night.
  • Visually impaired assistance and autonomous vehicles require robust nighttime object recognition.

Purpose of the Study:

  • Introduce UrOAC, a novel dataset for urban object detection.
  • Capture urban scenes across various lighting, from daylight to nighttime.
  • Provide annotated data for pedestrian crosswalks, and red/green traffic lights.

Main Methods:

  • Collected images of urban objects under diverse lighting conditions.
  • Annotated objects with categories and bounding-boxes.
  • Developed the UrOAC dataset for training and evaluating AI models.

Main Results:

  • UrOAC dataset features urban objects in varied lighting, including nighttime.
  • Annotations include object category and precise bounding-boxes.
  • The dataset addresses the gap in low-light urban scene understanding.

Conclusions:

  • UrOAC dataset can significantly improve nighttime and low-light performance of vision-based systems.
  • Enhances object detection for visually impaired assistance devices.
  • Supports the development of safer self-driving and intelligent vehicles.