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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]

With the acceleration of urbanization, the intelligent transportation has received more and more attention. Among them, the use of depth completion technology to extract the depth information of objects plays an important role in the realization of vehicle target tracking, distance calculation between targets and other tasks. However, multi-source depth completion data collected in practice often have correlation bias, resulting in knotty errors. In this regard, this study studies the depth completion technology based on multi-source data association fusion. The proposed technology enhances the depth map by calculating multi-channel confidence, and performs more accurate point-by-point correlation between the image and the millimeter-wave radar point cloud data. By designing a multi-scale attention fusion module, the adaptive fusion of multi-granularity associated data is realized to generate high-quality depth maps. In this study, a large number of experiments have been carried out in the public nuScenes data set. The experimental results show that the mean absolute error of our method is 1.142 m, which is lower than the 1.472 m of the existing benchmark method. [ABSTRACT FROM AUTHOR]

To meet the demand of large number and high integration of filters in the IF filter modules of radar receivers, a miniaturization LC filter based on the 3D integration technology is proposed in this study. The application and integrating process of 3D LC filter in modules are studied and analyzed. The 3D LC filter adopts two substrates, and uses 3D integration process and BGA technology to realize the three-dimensional integrated assembly of the two substrates. High-density layout can be realized by placing capacitive elements on the bottom substrate. Inductive elements are placed on the top substrate and separated from the capacitive elements, and signal communication is achieved through solder balls. The use of a three-dimensional integration scheme can reduce the size of the LC filter circuit by nearly 50%, and the filter index is consistent with the performance of the planar filter circuit. Compared with the traditional IF filter component design method, this solution is more conducive to achieve high integration and miniaturization. [ABSTRACT FROM AUTHOR]