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.
authors: Chandan Kumar Subrahmanyam Vaddi Aishwarya Sarkar
hIndex: Subrahmanyam Vaddi 1