1. CenRadfusion: fusing image center detection and millimeter wave radar for 3D object detection.
- Author
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Shi, Peicheng, Jiang, Tong, Yang, Aixi, and Liu, Zhiqiang
- Abstract
The fusion of visual and millimeter-wave radar data has emerged as a prominent solution for precise 3D object detection. This paper focuses on the fusion of visual and mmWave radar information and presents an enhanced fusion method called CenRadfusion. This method represents an evolution and improvement over the classic CenterFusion network by leveraging the fused features from mmWave radar and camera data to achieve accurate 3D object detection. The key features of this method are as follows:To ensure the integrity of the fusion architecture, mmWave radar point clouds are initially projected onto the image plane and added as an additional channel to the input of the CenterNet image detection network. This process forms preliminary 3D detection boxes.Subsequently, mmWave radar point clouds are subjected to density-based clustering, which results in the acquisition of labels and the elimination of irrelevant point clouds and white noise. This step enhances data quality and the reliability of object detection.Finally, an attention module, known as the Squeeze-and-Excitation Networks, is incorporated to weight each feature channel, thereby enhancing the importance of crucial features in the network.Experimental results demonstrate that compared to the original CenterFusion algorithm, the detection Average Precision (AP) values for cars, trucks, and motorcycles have improved by 7.8%, 5.5%, and 5.4%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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