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Improving AI object detection in fire scenes through data augmentation.

Fatima Lois Suarez1, Yi-Lin Chen1, Ray Hsienho Chang2

  • 1Department of Computer Science, Chengchi University, Taipei City, Taiwan.

Journal of Occupational and Environmental Hygiene
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

Image enhancement techniques like CLAHE and Zero-DCE improve Artificial Intelligence (AI) object detection accuracy for firefighters and firetrucks in disaster response. This boosts AI

Keywords:
Artificial intelligence (AI)data argumentationfirefighter safetyobject detectionoccupational safety and Health

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Firefighting Technology

Background:

  • Artificial Intelligence (AI) is increasingly used in disaster response to enhance efficiency, particularly in fireground operations.
  • A key challenge for AI in firefighting is the poor image quality (brightness, resolution) of fire scene visuals.
  • Accurate identification of firefighters and apparatus is crucial for effective AI-driven incident management.

Purpose of the Study:

  • To evaluate the impact of image enhancement techniques on AI object detection accuracy in fire scenes.
  • To compare the effectiveness of Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Zero-reference Deep Curve Estimation (Zero-DCE) for AI training data augmentation.

Main Methods:

  • Trained an AI object detector using images from various fire scenes.
  • Augmented the training dataset with images processed by CLAHE and Zero-DCE.
  • Evaluated detector performance on enhanced and unenhanced test images.

Main Results:

  • AI detector achieved high precision for firefighters (0.827) and firetrucks (0.945) after data augmentation.
  • CLAHE improved mean average precision (mAP) by 8% and recall by 7% compared to the baseline.
  • Zero-DCE excelled in low-light conditions, achieving the highest firetruck precision (0.945).

Conclusions:

  • Image enhancement significantly improves AI model generalizability and accuracy for fireground operations.
  • CLAHE and Zero-DCE are effective methods for enhancing visual data for AI in firefighting.
  • Further research can advance AI recognition for improved disaster response and fireground management.