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All Journal Emerging Information Science and Technology JOINCS (Journal of Informatics, Network, and Computer Science)
Huy, Le Nam Quoc
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Computer Science & IT

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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.
Cascaded Context-Aware Instance Segmentation with Transformer-Encoder for Adverse Weather Condition: Segmentasi Instansi Berbasis Konteks Bertingkat dengan Transformer-Encoder untuk Kondisi Cuaca Buruk Haq, Muhamad Amirul; Huy, Le Nam Quoc
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 7 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v7i2.1654

Abstract

Localizing objects from an image has been a vital part in autonomous driving since object localization performance directly correlate with the safety of the passenger. Robust and accurate object localization that can adapt to any driving environment has always been improved to ensure a safe and reliable system. In this work, we propose CBNet, a two-stage instance segmentation network for an autonomous driving environment. The network leverages a powerful transformer network as the feature extractor to improve performance. In addition, our proposed network utilizes a cascade design for both the object proposal network and the region-of-interests classifier. The cascade design addresses the issue of degrading detections over a high detection threshold. Moreover, we implement shape and edge-aware losses for the segmentation mask and end-to-end knowledge distillation strategy during training to improve the robustness of the network in extreme conditions. Our proposed network achieves 6.5 AP and 5.7 mIoU improvement from the prior methods in Cityscapes driving dataset. Furthermore, we evaluate our network in Foggy Cityscapes dataset to ensure the robustness of our network in extreme conditions. CBNet is able to improve the performance of prior methods by 7.7 AP and 6.7 mIoU in Foggy Cityscapes dataset.
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 Muhamad Amirul Haq Nuniek Fahriani, Nuniek