  In this research work, we have proposed a thermal tiny-YOLO multi-class
object detection (TTYMOD) system as a smart forward sensing system that should
remain effective in all weather and harsh environmental conditions using an
end-to-end YOLO deep learning framework. It provides enhanced safety and
improved awareness features for driver assistance. The system is trained on
large-scale thermal public datasets as well as newly gathered novel
open-sourced dataset comprising of more than 35,000 distinct thermal frames.
For optimal training and convergence of YOLO-v5 tiny network variant on thermal
data, we have employed different optimizers which include stochastic decent
gradient (SGD), Adam, and its variant AdamW which has an improved
implementation of weight decay. The performance of thermally tuned tiny
architecture is further evaluated on the public as well as locally gathered
test data in diversified and challenging weather and environmental conditions.
The efficacy of a thermally tuned nano network is quantified using various
qualitative metrics which include mean average precision, frames per second
rate, and average inference time. Experimental outcomes show that the network
achieved the best mAP of 56.4% with an average inference time/ frame of 4
milliseconds. The study further incorporates optimization of tiny network
variant using the TensorFlow Lite quantization tool this is beneficial for the
deployment of deep learning architectures on the edge and mobile devices. For
this study, we have used a raspberry pi 4 computing board for evaluating the
real-time feasibility performance of an optimized version of the thermal object
detection network for the automotive sensor suite. The source code, trained and
optimized models and complete validation/ testing results are publicly
available at
https://github.com/MAli-Farooq/Thermal-YOLO-And-Model-Optimization-Using-TensorFlowLite.
