Steel surface defect detection based on deep learning 3D reconstruction

被引：1
作者：
Lan H. [1 ]
Yu J.-B. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Tongji University, Shanghai
[2] Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 03期
关键词：
3D reconstruction; deep learning; defect location; point cloud segmentation; surface defect detection;
D O I：
10.3785/j.issn.1008-973X.2023.03.004
中图分类号：
学科分类号：
摘要：
A new 3D reconstruction network was proposed in order to resolve the difficulty of 2D detection method to detect defects with depth information. CasMVSNet with multiscale feature enhancement (MFE-CasMVSNet) was combined with the technology of point cloud processing for steel plate surface defect detection. In order to improve the accuracy of 3D reconstruction, a position-oriented feature enhancement module (PFEM) and a multiscale feature adaptive fusion module (MFAFM) were proposed to effectively extract features and reduce information loss. A density clustering method, curvature-sparse-guided density-based spatial clustering of applications with noise (CS-DBSCAN), was proposed for accurately extracting defects in different parts, and the 3D detection box was introduced to locate and visualize defects. Experimental results show that compared with the reconstruction method based on images, MFE-CasMVSNet can realize the 3D reconstruction of steel plate surface more accurately and quickly. Compared with 2D detection, 3D visual defect detection can accurately obtain the 3D shape information of defects and realize the multi-dimensional detection of steel plate surface defects. © 2023 Zhejiang University. All rights reserved.
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页码：466 / 476+561
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