1. 基于雷达和视频融合的目标检测.
- Author
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朱勇, 黄永明, and 何幸
- Subjects
- *
OBJECT recognition (Computer vision) , *EXTREME weather , *RADAR signal processing , *MOBILE operating systems , *RADAR - Abstract
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 x 10°, 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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