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YOLO Based Object Detection Using Drone

Shiva Kumar R Naik1 , Kushal A2 , Lakshmi Narayan S3 , Sreeraam V Chatrapathi4 , Sagar T5

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-14 , Page no. 181-184, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si14.181184

Online published on May 15, 2019

Copyright © Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T, “YOLO Based Object Detection Using Drone,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.181-184, 2019.

MLA Style Citation: Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T "YOLO Based Object Detection Using Drone." International Journal of Computer Sciences and Engineering 07.14 (2019): 181-184.

APA Style Citation: Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T, (2019). YOLO Based Object Detection Using Drone. International Journal of Computer Sciences and Engineering, 07(14), 181-184.

BibTex Style Citation:
@article{Naik_2019,
author = {Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T},
title = {YOLO Based Object Detection Using Drone},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {14},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {181-184},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1116},
doi = {https://doi.org/10.26438/ijcse/v7i14.181184}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i14.181184}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1116
TI - YOLO Based Object Detection Using Drone
T2 - International Journal of Computer Sciences and Engineering
AU - Shiva Kumar R Naik, Kushal A, Lakshmi Narayan S, Sreeraam V Chatrapathi, Sagar T
PY - 2019
DA - 2019/05/15
PB - IJCSE, Indore, INDIA
SP - 181-184
IS - 14
VL - 07
SN - 2347-2693
ER -

           

Abstract

The headway of convolutional neural systems (CNNs) and Deep learning (DL) in the previous decade brought about significant upgrades in computer vision. One of the recipients of these advances is the task of object detection, where the goal is to distinguish and locate real-world objects inside pictures or videos. Real-time object tracking on a drone under a dynamic situation has been a difficult issue for a long time, with existing methodologies utilizing off-line calculation or powerful computation units on board. This paper displays lightweight real-time on board object tracking methodology, which varies, from basic image classification in that the AI demonstrate needs to distinguish numerous objects in a single frame, and figure out where these objects are found. The advances in procedures, joined with the improved PC equipment, put real-time object detection well inside the capacities of present day processors. Real-time object recognition is essential for some utilization of Unmanned Aerial Vehicles (UAVs), for example, observation and reconnaissance, search-and-rescue, and foundation assessment. In the previous couple of years, Convolutional Neural Networks (CNNs) have ascended as an unbelievable class of models for recognizing picture content, and are seen as the standard strategy for generally issues.

Key-Words / Index Term

Detection, Drone, Pattern Matching, Privacy Preserving, Security Vulnerabilities, Sensitive Items, Yolo

References

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