基于注意力机制的PointPillars+三维目标检测.

To accurately recognize and locate the surrounding vehicles and pedestrians, an attention-based PointPillars+ 3D target detection algorithm was proposed. The entire space was uniformly divided into pillars with a given resolution, and a pseudo-image was generated by extracting point-based features from all pillars. Two attention modules were introduced to highlight and restrain the information in the pseudo-image. A convolution neural network was used to process the output of the attention module, and the single shot multibox detector（SSD） was used for 3D object detection. The evaluation results show that the parallel attention-based PointPillars achieves good performance. Compared with the traditional PointPillars, the mAPm is increased from 66.19 to 69.95 in the bird′s eye view， and the car mAP is increased from 86.10 to 87.73. In the 3D mode, the mAPm is increased from 59.20 to 62.55, and the car mAP is increased from 74.99 to 76.25. [ABSTRACT FROM AUTHOR]