Abstract: With the development of deep learning and on-board LiDAR,driverless cars have increasingly high requirements for detection,which not only need to accurately detect obstacles on the road,but also have high requirements on detection speed.In the complex road scene,there are always obstacles and small volume of some targets,which make it difficult to accurately detect some targets.To solve this problem,this paper proposes an improved 3D target detection method of Pointpillars algorithm model to make it have higher accuracy while guaranteeing the detection speed.Firstly,a variety of data-enhancing operations are introduced to increase the diversity and magnitude of the dataset and reduce the overfitting phenomenon.Then,an attention matrix is added to the point column feature extraction,and the importance of each voxel is dynamically adjusted according to different voxel positions and semantic information,so that the model can focus on more useful features for target detection tasks.Finally,the channel attention mechanism(CA) and spatial attention mechanism(SA) modules are added to the backbone network of the model successively,which enhance the response of the model to useful information,suppresse the interference of unimportant features to the detection results,and thus improve the representation of target features.The experimental results show that the detection accuracy of the improved algorithm model is improved in each category and detection difficulty.

Key words: Laser radar, 3D object detection, Point cloud, Data enhancement, Attention mechanism