The 3D point cloud object detection algorithm based on deep learning is prone to issues such as inability to maintain network performance and poor transferability when changing scenes or devices.To address this issue,this article proposes an Accurate,Flexible,and highly transferable two-stage 3D point cloud object detection algorithm(AF3D).In the first stage of the AF3D detection algorithm,a segmented fitting algorithm is used to remove the road surface from the collected laser point cloud,then DBSCAN algorithm is used to cluster non-ground point clouds and obtain several clustering clusters.In the second stage of the AF3D detection algorithm,a point cloud fully connected network PFC-Net is established,and features are extracted and classified.Through experiments,it has been proven that this algorithm can achieve good detection performance on public KITTI datasets,and the detection accuracy for cars,pedestrians,and cyclists on real vehicle datasets is 69.74％,41.25％,and 54.33％,respectively,indicating good transferability.
关键词
智能交通/无人驾驶/深度学习/目标检测/激光点云
Key words
Intelligent transportation/Unmanned vehicle/Deep learning/Object detection/Laser point cloud
维普
万方数据