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Related Experiment Videos

Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with

Waun Broderick1, Sabine McConnell1

  • 1Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

This study optimizes computer vision models for safety using hyperparameter tuning and multitask learning. The developed framework enhances AI safety verification, significantly reducing false negatives in detecting imitation explosives for demining.

Keywords:
computer visiondeep learninghumanitarian demininghyperparameter tuninglandmine detectionmulti-task learningobject detectionsafety-critical systemsthermal imagingtransfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Safety-critical applications require highly reliable computer vision models.
  • Current models often lack interpretability, hindering safety assessment.
  • Humanitarian demining operations necessitate advanced detection technologies.

Purpose of the Study:

  • To present a methodological framework for optimizing computer vision models for safety-critical applications.
  • To enhance the interpretability of AI systems for safety operations.
  • To develop a case study for humanitarian demining using thermographic imaging.

Main Methods:

  • Systematic hyperparameter tuning and multitask learning were employed.
  • A comprehensive grid search evaluated 64 model configurations.
  • Loss function weights were optimized to minimize false negative rates.

Main Results:

  • The optimized model achieved a 37.5% reduction in false negatives.
  • Precision improved by 2.8%, reaching 92% with 90% detection accuracy.
  • The framework demonstrated viability for humanitarian demining operations.

Conclusions:

  • The proposed framework effectively optimizes computer vision models for safety.
  • Enhanced interpretability aids in assessing AI model risks and trade-offs.
  • Open data sharing is crucial for improving model generalizability in demining AI.