基于自注意力机制的两阶段三维目标检测方法.

In order to accurately identify the target obstacle in the traffic scene, considering the complexity of the real road scene and the importance of road safety, taking the sparsely embedded convolutional detection( SECOND) model as the basic model, the point cloud representation ability was enhanced by using the self-attention mechanism to obtain global semantic information, and the region of interest (RoI) detection head was used to optimize the 3D suggestion box generated by the candidate region to improve its detection accuracy. Then, two-stage 3D object detection method based on self-attention mechanism named SAR-SECOND was proposed. The results show that compared with the existing advanced 3D object detection methods, SAR-SECOND overall detection accuracy of car is 82. 28%, the overall accuracy of pedestrians is 51. 45%, and the overall accuracy of the rider is 72. 41% . It can be seen that the effectiveness of this method is effective. [ABSTRACT FROM AUTHOR]