Open AccessEffective Backbone Network for 3D Object Detection in Point Cloud
Author(s) -
Jun Xu,
Yanxin Ma,
Shiwen He
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/711/1/012084
Subject(s) - point cloud , backbone network , object (grammar) , object detection , computer science , artificial intelligence , computer vision , residual , point (geometry) , cloud computing , pattern recognition (psychology) , computer network , algorithm , mathematics , geometry , operating system
Three-dimensional (3D) object detection is composed of object classification and object localization, and has been used in many applications, such autonomous driving and mobile robot. However, the accuracy of classification and localization is greatly affected by the depth of the network. Shallow networks tend to cause poor classification, but as the depth of the network increases, the network degradation will become more obvious, which is not conducive to the training of network. To solve this problem, a novel Backbone Network is proposed in this paper for 3D object detection in point cloud, which consists of multiple residual modules. Experimental results on the KITTI 3D object detection benchmarks show that Backbone Network proposed can effectively improve the accuracy of 3D object detection.