Title: A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS. Abstract: Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially
Autonomous flying drones grabbed a lot of attention in Artificial Intelligence.
Since electronic technology is getting smaller, cheaper and more efficient, huge
advancement in the study of UAVs has been observed recently. From monitoring
floods, discerning the spread of algae in water bodies to detecting forest trail, their
application is far and wide. Our work is mainly focused on autonomous flying
drones where we establish a case study towards efficiency, robustness and accuracy
of UAVs where we showed our results well supported through experiments.
We provide details of the software and hardware architecture used in the study. We
further discuss about our implementation algorithms and present experiments that
provide a comparison between three different state-of-the-art algorithms namely
TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power
consumption and inference time. In our study, we have shown that MobileNet has
produced better results with very less computational requirement and power consumption.
We have also reported the challenges we have faced during our work
as well as a brief discussion on our future work to improve safety features and
performance.