Design of anti-noise point cloud recognition network based on deep learning
Authors: Zhang, G., Tang, W., Wan, T. and Xue, T.
Journal: Fangzhi Gaoxiao Jichukexue Xuebao
Volume: 33
Issue: 3
Pages: 113-120
ISSN: 1006-8341
DOI: 10.13338/j.issn.1006-8341.2020.03.018
Abstract:In order to improve the anti-noise ability of the point cloud recognition network and re- duce the pressure of the neural network on the processor in the spatial model operation, a light- weight and anti-noise point cloud recognition network was designed. By introduceing the Point Cloud Library, adding a random downsampling module and a StatisticalOutlierRemoval filter module before the input data of the multi-layer perceptron, the outliers in complex point cloud scenese were effectively filtered out. By optimizing the hierarchical structure of multi-layer per- ception modules and fully connected module, the redundant parameters of network are reduced. The experimental results show that compared with seven kinds of networks of the same type, this network has stronger robustness to the random noise in the data and has faster recognition speed while the accuracy rate of model recognition is maintained at 84.2%.
Source: Scopus