PENGEMBANGAN SISTEM PENGAWASAN UNTUK DETEKSI DRONE DALAM LINGKUNGAN TERSAMAR
The widespread use of drones or UAVs has reached various sectors; however, drones are often operated by irresponsible individuals in restricted areas, such as around airports. This phenomenon poses significant risks to airport operations. Moreover, drones can also infiltrate hidden areas, such as...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/77970 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The widespread use of drones or UAVs has reached various sectors; however,
drones are often operated by irresponsible individuals in restricted areas, such as
around airports. This phenomenon poses significant risks to airport operations.
Moreover, drones can also infiltrate hidden areas, such as among trees or buildings,
making detection challenging. In this study, Writer proposes a drone detection
approach using the YOLO method along with supporting algorithms.
The writer developed a surveillance system that combines the YOLO model with
the DeepSORT, Region of Interest and Background Subtraction algorithms. The
training dataset includes drone images and video clips. The selection of the YOLO
model is based on its efficient real-time performance.
The experimental results show that the proposed surveillance system successfully
detects drones in ambiguous areas, including among trees and buildings. However,
one limitation of the system is that it sometimes misidentifies birds as drones.
In conclusion, the proposed surveillance system has the potential to effectively
detect drones in challenging detection conditions. While there are still some aspects
to be improved, this research can contribute to the development of surveillance
systems for drone detection in restricted areas and advancements in security
technology.
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