Evaluations of thermal imaging technology for automotive use cases
Date
2022-06-10Author
Farooq, Muhammad Ali
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Abstract
Thermal imaging has been widely used in high-end applications for instance industrial and
military applications as it provides superior and effective results in challenging environments
and weather conditions such that in low lighting scenarios and has aggregate immunity to visual
limitations thus providing increased situational awareness. This research is about exploring the
potential of thermal imaging for smart vehicular systems including both in-cabin and out-cabin
applications using uncooled LWIR thermal imaging technology. Novel thermal datasets are
collected in indoor and road-side environments using an especially designed low-cost, yet
effective prototype thermal camera module developed under the Heliaus project.
The collected data along with public datasets are further used for generating large-scale
thermal synthetic data using the composite structure of advanced machine learning algorithms.
The next phase of this work focuses on designing AI-based smart imaging pipelines which
include driver gender classification system and object detection in the thermal spectrum. The
performance of these systems is evaluated using various quantitative metrics which include
overall accuracy, sensitivity, specificity, precision, recall curve, mean average precision, and
frames per second.
Furthermore, the trained and fine-tuned neural architectures on thermal data are
deployed on Edge-GPU embedded devices for real-time onboard feasibility validation tests.
This is accomplished by performing optimal optimization of successfully converged deep
learning models on thermal data using SoA neural accelerators to achieve a reduced amount of
inference time and a higher FPS rate.