Efficient and accurate object detection for 3D point clouds in intelligent visual internet of things

Abstract

Visual perception is a key technology in the Intelligent Visual Internet of Things. The research of object detection methods is of great significance for improving the safety and efficiency of unmanned driving technology and intelligent visual Internet of Things. 3D point clouds object detection of deep learning can not only use the deep network to automatically learn characteristics of the multi-layer abstract structure, improve calculation efficiency and detection accuracy of the model, but also have better performance in dealing with object occlusion, absence and data sparsity with obtained high-dimensional point clouds information. However, the current review of object detection methods for 3D point clouds based on deep learning is scarce. In order to provide a more comprehensive understanding and understanding of the security and efficiency development of driverless technology, this paper is divided into the monocular camera, RGB-D image and LiDAR point cloud, according to the main data of the network model, and further subdivides according to the different use methods of the model. Analyze the performance of various model detection methods. This article also summarizes current commonly used 3D point clouds datasets of object detection, organizes and describes detection metrics of commonly used 3D point clouds, and discusses research challenges and development trends. The real-time performance of 3D point cloud object detection under the intelligent vision Internet of Things needs to be improved.