Vehicle Detection Based on Structure Perception in Point Cloud

被引：0
|
作者：
Li Z. [1 ,2 ]
Yao C. [1 ]
Liu Y. [1 ]
Li H. [3 ,4 ]
机构：
[1] College of Computer Science and Technology, China University of Petroleum, Qingdao
[2] Shengli College of China University of Petroleum, Dongying
[3] Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
[4] University of Chinese Academy of Sciences, Beijing
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2021年 / 33卷 / 03期
关键词：
3D point cloud object detection; Region proposal network; Structure feature;
D O I：
10.3724/SP.J.1089.2021.18368
中图分类号：
学科分类号：
摘要：
In the field of automatic driving, computer perception and understanding of the surrounding environment is essential. Compared with 2D object detection, 3D point cloud object detection can provide the three-dimensional information of the object that the 2D object detection does not have. In order to solve the problem of large disparity between the original input point cloud and the detection result in 3D object detection, a region proposal generation module based on structure awareness is proposed, in which the structural features of each point are defined, and the supervision information provided by the 3D point cloud object detection dataset is fully utilized. The network can learn more discriminative features to improve the quality of proposals. Secondly, the feature is added to the proposal fine-tuning stage to enrich the context features and local features of point cloud. Evaluated on KITTI 3D object detection dataset, in the region proposal generation stage, under the IoU threshold of 0.7, using 50 proposals, there is a more than 13% increase in the recall rate compared to previous results. In the proposal fine-tuning stage, the detection results of the 3 difficulty levels objects is obviously improved, indicating the effectiveness of the proposed method for 3D point cloud object detection. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：405 / 412
页数：7
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