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

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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	Collaborative work international with UIN Sunan Gunung Djati
Uncontrolled Keywords:	Drone, UAV, detection, classification, Tiny YOLOv3, YOLO v5s
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	23 Dec 2022 16:08
Last Modified:	23 Dec 2022 16:08
URI:	http://irep.iium.edu.my/id/eprint/101864

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