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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Robotics and Control (JRC) Kontribusia : Research Dissemination for Community Development Emerging Information Science and Technology Jurnal Komputer, Informasi dan Teknologi Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Computing Insight: Journal of Computer Science
Nuniek Fahriani, Nuniek
Universitas Muhammadiyah Surabaya
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Journal : Emerging Information Science and Technology

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Improving YOLO Object Detection Performance on Single-Board Computer using Virtual Machine Haq, Muhamad Amirul; Huy, Le Nam Quoc; Fahriani, Nuniek
Emerging Information Science and Technology Vol 5, No 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22486

Abstract

Single-board computers have gained popularity in the recent decade, largely due to the immense advancements in deep learning. Deep learning involves complex computational processes that are beyond the capabilities of regular microcontrollers, thus necessitating the use of single-board computers. However, single-board computers are primarily designed to operate efficiently in low-power environments. Therefore, optimization is crucial for running deep learning algorithms effectively on single-board computers. In this work, we explore the impact of utilizing the DeepStream framework to run deep learning algorithms, specifically the YOLO algorithm, on NVIDIA Jetson single-board computers. The DeepStream framework can be executed in virtual machines, notably Docker, to improve the performance and portability of the model. Additionally, deploying the Docker virtual machine from removable disks can further enhance its portability and even increase the algorithm's speed. Our benchmarks indicate that real-time streaming of the YOLO algorithm can operate up to 8.5 times faster when deployed from a Docker virtual machine.
Co-Authors Ary Mazharuddin Shiddiqi Ashr Hafiizh Tantri Aswin Rosadi Azmi, Muhammad Ulul Darmawan Aditama Darwaman, Mochammad Ilham Fachrur Rozi Fachrur Rozi Faikul Umam Fiqri, Muhammad Firnanda Ali Haq, Muhamad Amirul Harunur Rosyid Haryanti, Tining Hidayatulloh, Muhammad Alfinda Huy, Le Nam Quoc Indah Kurniawati Inria Isby Istiqlalia Istiqlalia, Inria Isby Maulana, Fahmi Muhammad Alfinda Hidayatulloh Muhammad Firnanda Ali Fiqri Muhammad Fuad Muhammad Guntur Salasa Muhammad Ulul Azmi Nurwahyudi, Ilham Sabri Balafif Sabri Balafif Salasa, Muhammad Guntur Sri Wahyuni Supeno Djanali Tantri, Ashr Hafiizh Tining Haryanti Tining Haryanti Triuli Novianti Triuli Novianti, Triuli Winarno Winarno Winarno Winarno