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Article

Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices

Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indianapolis, IN 46202, USA
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Author to whom correspondence should be addressed.
Academic Editors: Nicu Bizon and Mihai Oproescu
J. Low Power Electron. Appl. 2022, 12(2), 21; https://doi.org/10.3390/jlpea12020021
Received: 15 February 2022 / Revised: 24 March 2022 / Accepted: 31 March 2022 / Published: 13 April 2022
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time. View Full-Text
Keywords: neural networks; YOLOv5; deep learning; ROS 2; CNN; object detection; NVIDIA; NVIDIA Jetson Xavier NX; ROS; PyTorch neural networks; YOLOv5; deep learning; ROS 2; CNN; object detection; NVIDIA; NVIDIA Jetson Xavier NX; ROS; PyTorch
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MDPI and ACS Style

Ravi, N.; El-Sharkawy, M. Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices. J. Low Power Electron. Appl. 2022, 12, 21. https://doi.org/10.3390/jlpea12020021

AMA Style

Ravi N, El-Sharkawy M. Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices. Journal of Low Power Electronics and Applications. 2022; 12(2):21. https://doi.org/10.3390/jlpea12020021

Chicago/Turabian Style

Ravi, Niranjan, and Mohamed El-Sharkawy. 2022. "Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices" Journal of Low Power Electronics and Applications 12, no. 2: 21. https://doi.org/10.3390/jlpea12020021

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