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.

翻译：本文提出一种物联网应用，该应用利用人工智能分类器，通过帧差法实现快速目标检测。相较于端到端方法，帧差法因其处理时长更短，成为物联网系统中最具能效且最适合快速目标检测的方案。我们在三种边缘设备（AMD AlveoT M U50、Jetson Orin Nano 和 Hailo-8T M AI 加速器）上部署了该技术，并测试了包含人工神经网络和Transformer模型的四种模型。我们研究了包括鸟类、汽车、火车和飞机在内的多种目标类别。实验表明，采用帧差法时，MobileNet模型始终表现出高精度、低延迟和高能效的特点。YOLOX则始终显示出最低的精度、最低的延迟和最低的能效。与端到端方法相比，所提算法将平均精度提升了28.314%，平均能效提高了3.6倍，平均延迟降低了39.305%。在所有类别中，火车和飞机属于快速移动目标。实验显示，针对火车和飞机的检测精度百分比低于其他类别。因此，在需要快速检测和精确结果的任务中，端到端方法可能带来灾难性后果，因为它们无法有效处理快速目标检测。为提升计算效率，我们将所提方法设计为轻量级检测算法。该算法非常适用于物联网系统，特别是那些需要快速移动目标检测和更高精度的应用场景。