Drones have taken a major technological leap in today’s industries, such as surveying, delivery, photography, geographic mapping, wildlife monitoring, search and rescue. A wide range of applications have drawn the attention of researchers as well as commoners. In particular, to survey remote location, it requires fast response and adaptive control. Human errors can lead to loss of drone and data. In the past, researches had to rely upon their talents to operate drone in an unknown environment and navigation in an unknown terrain requires special skills. Drones being a very costly equipment, need professional handling skills. Using deep learning techniques, drones can be trained to search and track specific targets. This eliminates the requirement of experts to operate the drones. In previous attempts made on this research, the authors described by separating foreground with background to use the foreground for navigation. In this paper, image-based navigation techniques are being implemented for identifying a mark instead of a complete background, thus reducing the computational requirements for drone navigation. In conclusion, deep learning-based drone which can recognize objects instead of whole background has a greater response for an isolated system. As a result, the electronic components on the drone can be reduced to effectively use the battery and compress the size of the drone making it more maneuverable.


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    Title :

    Deep Learning-Based Autonomous Drone(s) Assistance


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Automation, Signal Processing, Instrumentation and Control ; 2020 February 27, 2020 - February 28, 2020



    Publication date :

    2021-03-05


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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