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

## TL;DR

This paper introduces a framework to improve safety-critical computer vision models by optimizing detection reliability through hyperparameter tuning and multitask learning.

## Contribution

The novel contribution is a systematic approach combining hyperparameter tuning and multitask learning to reduce false negatives in safety-critical vision systems.

## Key findings

- The optimized model achieves 90% detection accuracy with 92% precision.
- A 37.5% reduction in false negatives is achieved through loss function weight tuning.
- The framework is demonstrated using thermographic images of imitation explosives for humanitarian demining.

## Abstract

This paper presents a methodological framework for selectively optimizing computer vision models for safety-critical applications. Through systematic processes of hyperparameter tuning alongside multitask learning, we attempt to create a highly interpretable system to better assess the dangers of models intended for safety operations and intentionally select their trade-offs. Using thermographic images of a specific imitation explosive, we create a case study for the viability of humanitarian demining operations. We hope to demonstrate how this approach provides a developmental framework for creating humanitarian AI systems that optimize safety verification in real-world scenarios. By employing a comprehensive grid search across 64 model configurations to evaluate how loss function weights impact detection reliability, with particular focus on minimizing false negative rates due to their operational impact. The optimized configuration achieves a 37.5% reduction in false negatives while improving precision by 2.8%, resulting in 90% detection accuracy with 92% precision. However, to expand the generalizability of this model, we hope to call institutions to openly share their data to increase the breadth of imitation landmines and terrain data to train models from.