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

arXiv: https://arxiv.org/abs/2602.09515 · HTML: https://mlsanity.com/paper/2602.09515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers](https://mlsanity.com/paper/2205.03436/) (2022)
- [DLAU: A Scalable Deep Learning Accelerator Unit on FPGA](https://mlsanity.com/paper/1605.06894/) (2016)
- [A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications](https://mlsanity.com/paper/2107.06835/) (2021)
- [E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2308.04774/) (2023)
- [Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System](https://mlsanity.com/paper/2307.16834/) (2023)

## Built on by (most cited first, 1 of 1)

- [MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers](https://mlsanity.com/paper/2605.10148/) (2026)

## Abstract (arXiv)

This paper 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: AMD AlveoT M U50, Jetson Orin Nano, and Hailo-8T M 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.