  This study is focused on evaluating the real-time performance of thermal
object detection for smart and safe vehicular systems by deploying the trained
networks on GPU & single-board EDGE-GPU computing platforms for onboard
automotive sensor suite testing. A novel large-scale thermal dataset comprising
of > 35,000 distinct frames is acquired, processed, and open-sourced in
challenging weather and environmental scenarios. The dataset is a recorded from
lost-cost yet effective uncooled LWIR thermal camera, mounted stand-alone and
on an electric vehicle to minimize mechanical vibrations. State-of-the-art
YOLO-V5 networks variants are trained using four different public datasets as
well newly acquired local dataset for optimal generalization of DNN by
employing SGD optimizer. The effectiveness of trained networks is validated on
extensive test data using various quantitative metrics which include precision,
recall curve, mean average precision, and frames per second. The smaller
network variant of YOLO is further optimized using TensorRT inference
accelerator to explicitly boost the frames per second rate. Optimized network
engine increases the frames per second rate by 3.5 times when testing on low
power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on
Nvidia Xavier NX development boards.
