Open AccessDetection of Surface Defects in Logs Using Point Cloud Data and Deep Learning
Author(s) -
Shengbo Liu,
Peizhen Fu,
Yan Lei,
Jian Wu,
Yandong Zhao
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.67
Subject(s) - point cloud , knot (papermaking) , computer science , robustness (evolution) , deep learning , artificial intelligence , cloud computing , pattern recognition (psychology) , data mining , algorithm , engineering , biochemistry , chemistry , chemical engineering , gene , operating system
Deep learning classification based on 3D point clouds has gained considerable research interest in recent years.The classification and quantitative analysis of wood defects are of great significance to the wood processing industry. In order to solve the problems of slow processing and low robustness of 3D data. This paper proposes an improvement based on littlepoint CNN lightweight deep learning network, adding BN layer. And based on the data set made by ourselves, the test is carried out. The new network bnlittlepoint CNN has been improved in speed and recognition rate. The correct rate of recognition for non defect log, non defect log and defect log as well as defect knot and dead knot can reach 95.6%.Finally, the "dead knot" and "loose knot" are quantitatively analyzed based on the "integral" idea, and the volume and surface area of the defect are obtained to a certain extent,the error is not more than 1.5% and the defect surface reconstruction is completed based on the triangulation idea.