A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS

Chandan Kumar, Subrahmanyam Vaddi, Aishwarya Sarkar

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • 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.
  • Keywords: Energy Efficiency, Autonomous Flying, Trail Detection
  • TL;DR: case study on optimal deep learning model for UAVs
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