Energy optimized YOLO: Quantized inference for real-time edge AI object detection

Wong, Yan Chiew and Min Chiam, Hwee and Sarban Singh, Ranjit Singh and Anand, T. Joseph Sahaya (2025) Energy optimized YOLO: Quantized inference for real-time edge AI object detection. Journal Of Telecommunication, Electronic And Computer Engineering (JTEC), 17 (1). pp. 19-28. ISSN 2180-1843

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Abstract

Efficient real-time object detection is a critical requirement in edge computing applications, such as smart surveillance, where resource constraints pose significant challenges. Existing deep learning methods often struggle to balance accuracy and efficiency, particularly when deployed on hardware with limited computational resources. This work focuses on developing a quantized object detection system utilizing advanced deep learning models to improve inference performance on edge devices, Zedboard and Jetson Nano. The Zedboard, an FPGA platform without GPU acceleration, executes a quantized YOLOv3-tiny model with ultra-low power consumption of 2.2W but requires over 3 seconds per inference, making it unsuitable for real-time applications. In contrast, the Jetson Nano, running an optimized YOLOv7-tiny model with FP16 quantization and GPU acceleration, achieves a processing speed of 38 FPS with mAP of 46.3%, while maintaining a low power consumption of 5.1W. Based on the results, this work presents a practical solution for real-time object detection in resource-constrained environments by demonstrating the benefits of combining quantized deep learning models with GPU acceleration. Future work could focus on fine-tuning models for specific applications, such as traffic monitoring, to improve the detection of vehicles, pedestrians, and traffic signs in dynamic environments.

Item Type:	Article
Uncontrolled Keywords:	Object Detection, Real-time, Edge Device, Quantization, FPGA, Jetson Nano, YOLO
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	21 Jul 2026 04:04
Last Modified:	21 Jul 2026 04:04
URI:	http://eprints.utem.edu.my/id/eprint/30197
Statistic Details:	View Download Statistic

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