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Performance comparison of various YOLO architectures on object detection of UAV images

Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs v...

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Main Authors: Gunawan, Teddy Surya, Mahmoud Ismail, Islam Mohamed, Kartiwi, Mira, Ismail, Nanang
Format: Conference or Workshop Item
Language:English
English
English
Published: IEEE 2022
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/101864/7/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images.pdf
http://irep.iium.edu.my/101864/8/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images_SCOPUS.pdf
http://irep.iium.edu.my/101864/1/TeddyUAV-YOLOv2PDFexpress.pdf
http://irep.iium.edu.my/101864/
https://icsima.ieeemy-ims.org/22/program-schedule/
https://doi.org/10.1109/ICSIMA55652.2022.9928938
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spelling my.iium.irep.1018642022-12-23T08:08:46Z http://irep.iium.edu.my/101864/ Performance comparison of various YOLO architectures on object detection of UAV images Gunawan, Teddy Surya Mahmoud Ismail, Islam Mohamed Kartiwi, Mira Ismail, Nanang TK7885 Computer engineering Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs vital and efficient in this industry. Current Unmanned Aerial Vehicles (UAVs) applications in transportation systems encourage the development of object detection methods to collect real-time traffic data using UAVs. Due to the versatility and portability of UAVs, particularly drones, individuals require systems that operate with UAVs to identify objects in real-time for military, safety observation, and protection. The culmination of the evolution of computer vision technology is the development of sophisticated algorithms centered on extensive training and testing datasets. This research aims to compare the performance of object detection of UAV images using various YOLO architectures. Tiny YOLOv3 and YOLOv5s models were implemented to extract the object’s features and classify them into the dataset’s multiple classes. This paper selected the VisDrone2019 dataset for its various object classes: pedestrian, person, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor. Results demonstrated that YOLOv5s have acceptable precision and processing speed. IEEE 2022-10-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/101864/7/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images.pdf application/pdf en http://irep.iium.edu.my/101864/8/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/101864/1/TeddyUAV-YOLOv2PDFexpress.pdf Gunawan, Teddy Surya and Mahmoud Ismail, Islam Mohamed and Kartiwi, Mira and Ismail, Nanang (2022) Performance comparison of various YOLO architectures on object detection of UAV images. In: 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 26-28 September 2022, Melaka. https://icsima.ieeemy-ims.org/22/program-schedule/ https://doi.org/10.1109/ICSIMA55652.2022.9928938
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Gunawan, Teddy Surya
Mahmoud Ismail, Islam Mohamed
Kartiwi, Mira
Ismail, Nanang
Performance comparison of various YOLO architectures on object detection of UAV images
description Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs vital and efficient in this industry. Current Unmanned Aerial Vehicles (UAVs) applications in transportation systems encourage the development of object detection methods to collect real-time traffic data using UAVs. Due to the versatility and portability of UAVs, particularly drones, individuals require systems that operate with UAVs to identify objects in real-time for military, safety observation, and protection. The culmination of the evolution of computer vision technology is the development of sophisticated algorithms centered on extensive training and testing datasets. This research aims to compare the performance of object detection of UAV images using various YOLO architectures. Tiny YOLOv3 and YOLOv5s models were implemented to extract the object’s features and classify them into the dataset’s multiple classes. This paper selected the VisDrone2019 dataset for its various object classes: pedestrian, person, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor. Results demonstrated that YOLOv5s have acceptable precision and processing speed.
format Conference or Workshop Item
author Gunawan, Teddy Surya
Mahmoud Ismail, Islam Mohamed
Kartiwi, Mira
Ismail, Nanang
author_facet Gunawan, Teddy Surya
Mahmoud Ismail, Islam Mohamed
Kartiwi, Mira
Ismail, Nanang
author_sort Gunawan, Teddy Surya
title Performance comparison of various YOLO architectures on object detection of UAV images
title_short Performance comparison of various YOLO architectures on object detection of UAV images
title_full Performance comparison of various YOLO architectures on object detection of UAV images
title_fullStr Performance comparison of various YOLO architectures on object detection of UAV images
title_full_unstemmed Performance comparison of various YOLO architectures on object detection of UAV images
title_sort performance comparison of various yolo architectures on object detection of uav images
publisher IEEE
publishDate 2022
url http://irep.iium.edu.my/101864/7/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images.pdf
http://irep.iium.edu.my/101864/8/101864_Performance%20comparison%20of%20various%20YOLO%20Architectures%20on%20Object%20Detection%20of%20UAV%20images_SCOPUS.pdf
http://irep.iium.edu.my/101864/1/TeddyUAV-YOLOv2PDFexpress.pdf
http://irep.iium.edu.my/101864/
https://icsima.ieeemy-ims.org/22/program-schedule/
https://doi.org/10.1109/ICSIMA55652.2022.9928938
_version_ 1753788182595371008
score 13.211869

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