基于雷达和视频融合的目标检测

Research on Object Detection Based on Radar and Video Fusion

基于视频的目标检测在恶劣天气情况下识别效果较差,故需弥补视频缺陷、提高检测框架的鲁棒性。针对此问题,文中设计了一个基于雷达和视频融合的目标检测框架,利用YOLOv5(You Only Look Once version 5)网络获得图片特征图与图片检测框,利用基于密度的聚类获得雷达检测框,并将雷达数据进行编码,得到基于雷达信息的目标检测结果。最后将两者的检测框叠加得到新ROI(Region of Interest),并得到融合雷达信息后的分类向量,提高了在极端天气下检测的准确率。实验结果表明,该框架的mAP(mean Average Precision)达到了60.07%,且参数量仅为7.64×10^(6),表明该框架具有轻量级、计算速度快、鲁棒性高等特点,可以被广泛应用于嵌入式与移动端平台。

The object detection based on video has the problem of poor recognition effect in bad weather,so it is necessary to make up for video defects and improve the robustness of detection framework.In view of this problem,this study designs an object detection framework based on radar and video fusion.YOLOv5(You Only Look Once version 5)network is used to obtain image feature map and image detection frame,density-based clustering is used to obtain radar detection frame,and radar data is encoded to get object detection results based on radar information.Finally,the detection boxes of the two are superimposed to obtain a new ROI(Region of Interest),and the classification vector after fusion radar information is obtained,which improves the detection accuracy in extreme weather.The experimental results show that the mAP(mean Average Precision)of the framework reaches 60.07%,and the parameter number is only 7.64×10^(6),which indicates that the framework has the characteristics of lightweight,fast computing speed and high robustness,and can be widely used in embedded and mobile platforms.