Energy-Efficient Fast Object Detection on Edge Devices for IoT Systems

2025-06-01

IEEE Internet of Things Journal

AI Summary

This work proposes an efficient object detection framework tailored for high-speed moving objects in IoT systems, addressing the critical trade-off among accuracy, latency, and energy efficiency. By integrating frame differencing with a lightweight neural network architecture that combines MobileNet, YOLOX, and Transformer components, the method achieves real-time performance while maintaining high precision. The framework is deployed and evaluated on edge devices including the AMD Alveo U50, Jetson Orin Nano, and Hailo-8. Experimental results demonstrate that, compared to conventional end-to-end approaches, the proposed solution improves average precision by 28.3%, enhances energy efficiency by 3.6×, and reduces latency by 39.3%, thereby achieving a superior balance among accuracy, energy consumption, and real-time responsiveness in high-speed scenarios.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This article presents an Internet of Things (IoT) application that utilizes an AI classifier for fast-object detection using the frame difference method. This method, with its shorter duration, is the most efficient and suitable for fast-object detection in IoT systems, which require energy-efficient applications compared to end-to-end methods. We have implemented this technique on three edge devices: 1) AMD AlveoTMU50; 2) Jetson Orin Nano; and 3) Hailo- $8^{TM}$ AI Accelerator, and four models with artificial neural networks and transformer models. We examined various classes, including birds, cars, trains, and airplanes. Using the frame difference method, the MobileNet model consistently has high accuracy, low latency, and is highly energy-efficient. YOLOX consistently shows the lowest accuracy, lowest latency, and lowest efficiency. The experimental results show that the proposed algorithm has improved the average accuracy gain by 28.314%, the average efficiency gain by 3.6 times, and the average latency reduction by 39.305% compared to the end-to-end method. Of all these classes, the faster objects are trains and airplanes. Experiments show that the accuracy percentage for trains and airplanes is lower than other categories. So, in tasks that require fast detection and accurate results, end-to-end methods can be a disaster because they cannot handle fast object detection. To improve computational efficiency, we designed our proposed method as a lightweight detection algorithm. It is well suited for applications in IoT systems, especially those that require fast-moving object detection and higher accuracy.

Problem

• fast object detection
• energy efficiency
• edge devices
• IoT systems
• real-time accuracy

Innovation

• frame difference method
• edge AI
• energy-efficient detection
• fast object detection
• lightweight algorithm