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基于Pix2Pix-YOLOv7毫米波雷达的新方法用于目标检测和分类

Mohamed Lamane^1,2, Mohamed Tabaa², Abdessamad Klilou¹

¹MiSET Team, Faculty of Sciences and Technics FST, Sultan Moulay Slimane University, Beni Mellal 23030, Morocco.

Sensors (Basel, Switzerland)

|December 9, 2023

概括

这项研究通过结合YOLOv7和Pix2Pix来增强使用频率调制连续波 (FMCW) 雷达的对象检测. 综合方法显著提高了毫米波雷达应用的分类准确性.

科学领域:

* 雷达系统工程雷达系统工程
* 机器学习用于传感器融合.
* 计算机视觉用于对象检测

背景情况:

*频率调制连续波 (FMCW) 雷达对于自动驾驶汽车和采矿中的物体检测至关重要.
*毫米波雷达系统需要提高分类准确性以提高性能.
*现有的检测方法面临着噪声和分类精度的挑战.

研究的目的:

* 提高毫米波雷达检测到的物体的分类精度.
* 开发和评估一种新的方法,集成FMCW雷达,YOLOv7和Pix2Pix.
* 为了减少雷达生成的热图中的噪声,以便更精确地识别对象.

主要方法:

* 开发一个结合FMCW雷达,YOLOv7物体检测和Pix2Pix降噪的系统.
*创建一个包含5个对象类的4125个注释热图的数据集.
*培训和评估14个模型,包括YOLOv7-PM和Pix2Pix增强版本,使用mAP指标.

主要成果:

*YOLOv7-PM模型获得了90.1%的mAP_0.5和49.51%的mAP_0.5:0.95.
*使用Pix2Pix架构来清理热图进一步提高了性能.
* Pix2Pix + YOLOv7-PM 模型的 mAP_0.5 达到 91.82% 的 mAP_0.5 和 52.59% 的 mAP_0.5:0.95.

结论:

* Pix2Pix与YOLOv7-PM的集成显著提高了毫米波雷达对象分类的准确性.
*使用Pix2Pix的降噪对于提高基于雷达的检测系统的性能至关重要.
* 拟议的方法提供了一个强大的解决方案,用于在苛刻的环境中高精度的物体检测.

关键词:

在FMCWW中使用.皮克斯2皮克斯 (Pix2Pix) 是一个这就是YOLOv7的意义.这是分类分类的分类.检测检测检测检测检测毫米波的波浪.雷达雷达雷达雷达是一个

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