Evaluations of thermal imaging technology for automotive use cases
Farooq, Muhammad Ali
Farooq, Muhammad Ali
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Publication Date
2022-06-10
Type
Thesis
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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.
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NUI Galway