论文标题
在低成本MCU上使用超低分辨率热成像仪的人检测
Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCU
论文作者
论文摘要
通过神经网络检测图像或视频中的人是文学中的一个良好研究主题。但是，这样的工作通常假设具有体面分辨率的摄像头和高性能处理器或GPU来运行检测算法，从而大大增加了完整检测系统的成本。但是，许多应用需要低成本的解决方案，该解决方案由廉价的传感器和简单的微控制器组成。在本文中，我们证明，即使在这样的硬件上，我们也不会谴责简单的经典图像处理技术。我们提出了一种新型的超轻质CNN基于CNN的人检测器，该探测器从低成本32x24像素静态成像器中处理热视频。在我们自己记录的数据集中受过训练和压缩，我们的模型可达到高达91.62％的精度（F1得分），其参数少于10K，并且在低成本微控制器STM32F407和STM32F746上的运行速度高达87ms和46ms。
Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively.
