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Volume 3107, Issue 1
2 May 2024
PROCEEDINGS ON SMART AND SUSTAINABLE DEVELOPMENTS IN ENGINEERING AND TECHNOLOGY: (PICET 2023)
5–6 May 2023
Vadodara, India
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Research Article| May 02 2024
Parmar Rahulkumar;
Parmar Rahulkumar a)
1
Department of Aeronautical Engineering, Aditya Silver Oak Institute of Technology
, Ahmedabad,
India
a)Corresponding Author: rahulparmarh5@gmail.com
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Parmar Yashwantkumar;
Parmar Yashwantkumar b)
1
Department of Aeronautical Engineering, Aditya Silver Oak Institute of Technology
, Ahmedabad,
India
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Dhruvi Shrimali;
Dhruvi Shrimali c)
2
Department of Electronics Engineering, Government Engineering College
, Modasa,
India
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Keval Bhavsar;
Keval Bhavsar d)
3
Department of Mechanical Engineering, Aditya Silver Oak Institute of Technology
, Ahmedabad,
India
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Umang Parmar
Umang Parmar e)
3
Department of Mechanical Engineering, Aditya Silver Oak Institute of Technology
, Ahmedabad,
India
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Author & Article Information
a)Corresponding Author: rahulparmarh5@gmail.com
b)
dhruvi.shrimali1509@gmail.com
c)
yashwantparmar98@gmail.com
d)
umangparmar138@gmail.com
e)
kevalbhavsar42@gmail.com
AIP Conf. Proc. 3107, 070002 (2024)
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Citation
Parmar Rahulkumar, Parmar Yashwantkumar, Dhruvi Shrimali, Keval Bhavsar, Umang Parmar; Drone detection and tracking using electro optics. AIP Conf. Proc. 2 May 2024; 3107 (1): 070002. https://doi.org/10.1063/5.0208395
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Drones are being utilized for a variety of functions, including military operations, agriculture, aerial photography, surveillance, and remote sensing. The widespread misuse of drones or UAVs, results in security, technological and public safety concerns that must be addressed and controlled. Security organizations are always looking for technology and sophisticated systems that can identify drones and secure the region around them. The present study deals a drone detection technique which is developed using Convolution Neural Networks. YOLOv4 and a convolution neural network were used to train the model for object detection. A small rocket-assisted missile can be used to capture the detected drone. Two processing units are in charge of the proposed missile. The first processing unit will employ image processing in real-time, while the remainder will be handle by the second processing unit, including missile control, monitoring, and targeting. Computer vision algorithms and an electro-optical sensor (a camera) allow the missile to track the target after it leaves the missile launcher. Tracking has been done to determine the precise location and speed of the target. It can be carried out by changing where the missile is and where it is going after it leaves the launcher.
Topics
Sensors, Remote sensing, Image processing, Artificial intelligence, Artificial neural networks, Ballistics, Electro-optics, Optical imaging
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