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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Two-stage object detection in low-light environments using deep learning image enhancement.

Ghaith Al-Refai1, Hisham Elmoaqet1, Abdullah Al-Refai2

  • 1Department of Mechatronics Engineering, German Jordanian University, Amman, Jordan.

Peerj. Computer Science
|June 26, 2025
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Summary

This study introduces a two-stage system for object detection in low-light images. The system significantly boosts detection accuracy by first enhancing image quality using deep learning before applying object detection algorithms.

Keywords:
AICNNComputer visionImage enhancementLow-light visionTwo-stage object detectionYOLO

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Processing

Background:

  • Object detection in low-light conditions presents significant challenges due to poor image quality.
  • Existing methods often struggle to accurately identify objects in environments with insufficient illumination.

Purpose of the Study:

  • To develop and evaluate a two-stage object detection system optimized for low-light environments.
  • To assess the impact of different image enhancement techniques on subsequent object detection performance.

Main Methods:

  • A two-stage approach combining supervised deep learning for image enhancement and a computer vision algorithm for object detection.
  • Evaluation of three enhancement algorithms (ZeroDCE++, Gladnet, TBEFN) and YOLOv7 for detection on the ExDark dataset.
  • Assessment using no-reference image quality evaluators and object detection metrics (recall, mean average precision).

Main Results:

  • The two-stage system with the TBEFN enhancement achieved a mean average precision (mAP) of 0.574, outperforming YOLOv7 alone (mAP 0.49).
  • The No-Reference Image Quality Evaluator (NIQE) showed a strong correlation with object detection performance (mAP).

Conclusions:

  • A two-stage object detection system significantly enhances performance in low-light conditions.
  • Image quality evaluation metrics like NIQE can predict and inform improvements in computer vision tasks.