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All Journal Emerging Information Science and Technology JOINCS (Journal of Informatics, Network, and Computer Science) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Huy, Le Nam Quoc
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Computer Science & IT Control & Systems Engineering

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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.
Region Enhanced Edge-Based Multi-Class Object Proposal for Self-Driving Vehicles Haq, Muhamad Amirul; Huy, Le Nam Quoc; Ridlwan, Muhammad
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4662

Abstract

On-road object detection is a fundamental element for the safety and reliability of autonomous driving systems. A primary challenge is developing object detection algorithms that are both fast and robust. This paper introduces a novel object proposal algorithm, named Region Enhanced Edge-Based (REEB) proposal, designed to accelerate object detection by significantly reducing the number of candidate regions requiring evaluation by a subsequent classification network. REEB leverages edge-map cues to score and rank initial proposals. To further enhance both detection quality and processing speed, the algorithm integrates efficient complementary techniques: image entropy is used to guide proposal generation density in relevant image regions, and road segmentation aids in refining proposal scores by differentiating road from non-road areas. Experimental evaluations on the KITTI dataset demonstrate that REEB achieves an average recall rate of 72.1% across four classes (pedestrian, cyclist, car, and truck) with an average processing time of 15 milliseconds per image. These results indicate strong performance when compared to other traditional, non-deep learning object proposal algorithms.
Co-Authors Haq, Muhamad Amirul Nuniek Fahriani, Nuniek Ridlwan, Muhammad