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浙江大学学报(工学版)  2023, Vol. 57 Issue (3): 466-476    DOI: 10.3785/j.issn.1008-973X.2023.03.004
计算机与控制工程     
基于深度学习三维成型的钢板表面缺陷检测
兰欢1(),余建波1,2,*()
1. 同济大学 机械与能源工程学院,上海 201804
2. 上海市大型构件智能制造机器人技术协同创新中心, 上海 201620
Steel surface defect detection based on deep learning 3D reconstruction
Huan LAN1(),Jian-bo YU1,2,*()
1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
2. Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai 201620, China
 全文: PDF(5141 KB)   HTML
摘要:

为了解决二维检测方法难以检测带有深度信息的缺陷问题,提出全新的三维重建网络. 提出基于多尺度特征增强的级联式三维重建网络(MFE-CasMVSNet),并与点云数据处理技术结合,用于钢板表面缺陷检测. 为了提高三维重建的精度,提出位置导向的特征增强模块(PFEM)和多尺度特征自适应融合模块(MFAFM),对特征进行有效提取并减少信息丢失. 提出基于曲率稀疏化的密度聚类方法(CS-DBSCAN),用于精确识别不同部位的缺陷. 引入三维检测框,实现对缺陷的定位与检测可视化. 实验结果表明,相较于图像几何的重建方法,MFE-CasMVSNet能够更加精确、快速地实现钢板表面的三维重建. 相较于二维检测,三维缺陷检测能够精确获取缺陷的三维形状信息,实现对钢板表面缺陷的多维度检测.

关键词: 表面缺陷检测深度学习三维重建点云分割缺陷定位    
Abstract:

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.

Key words: surface defect detection    deep learning    3D reconstruction    point cloud segmentation    defect location
收稿日期: 2022-03-27 出版日期: 2023-03-31
CLC:  TH 164  
基金资助: 国家自然科学基金资助项目 (92167107,71777173);中央高校基本业务经费资助项目
通讯作者: 余建波     E-mail: 13120521883@163.com;jbyu@tongji.edu.cn
作者简介: 兰欢(1998—),男,硕士生,从事三维重建、缺陷检测研究. orcid.org /0000-0001-5657-5581. E-mail: 13120521883@163.com
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引用本文:

兰欢,余建波. 基于深度学习三维成型的钢板表面缺陷检测[J]. 浙江大学学报(工学版), 2023, 57(3): 466-476.

Huan LAN,Jian-bo YU. Steel surface defect detection based on deep learning 3D reconstruction. Journal of ZheJiang University (Engineering Science), 2023, 57(3): 466-476.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.03.004        https://www.zjujournals.com/eng/CN/Y2023/V57/I3/466

图 1  本研究所提网络的钢板表面缺陷三维检测流程
图 2  多尺度特征增强的级联式三维重建网络结构
图 3  位置引导的特征增强模块结构
图 4  多尺度特征自适应融合模块结构
图 5  平面扫描法
图 6  深度图细化
图 7  带有深度信息的钢板常见表面缺陷
图 8  待检测缺陷钢板
缺陷类型 ld wd hd
割痕缺陷1 40.0 3.0 2.0
割痕缺陷2 33.0 3.0 1.2
焊点缺陷 9.1 6.0 1.3
钻痕缺陷 7.0 7.0 2.5
结疤缺陷 4.4 4.1 0.7
表 1  钢板缺陷的尺寸
方法 Acc/mm Com/mm OR/mm GPU /MB tR/s
COLMAP 0.400 0.664 0.532
Tola 0.342 1.190 0.766
Gipuma 0.283 0.873 0.578
MVSNet 0.456 0.646 0.551 10823 1.210
RMVSNet 0.383 0.452 0.417 7577 1.280
CasMVSNet 0.325 0.385 0.355 5360 0.496
本研究 0.317 0.376 0.347 6272 0.532
表 2  不同方法在DTU数据集上重建效果的比较
添加模块 Acc /mm Com /mm OR /mm GPU /MB tR/s
0.325 0.385 0.355 5374 0.494
PFEM 0.315 0.381 0.348 5613 0.512
MFAFM 0.320 0.382 0.351 5529 0.509
PFEM+MFAFM 0.317 0.376 0.347 6272 0.532
表 3  添加不同模块的CasMVSNet消融实验结果
图 9  钢板的部分图像集和重建效果
图 10  钢板缺陷的重建效果
图 11  不同方法的钢板重建效果对比
方法 tr /s 方法 tr /s
COLMAP 579 RMVSNet 76
VisualSFM 263 CasMVSNet 43
OpenMVG+MVS 345 本研究 49
表 4  不同方法的重建时间比较
图 12  基于点云的三维缺陷检测流程
图 13  不同点云处理方法的缺陷聚类效果比较
图 14  基于三维检测框的缺陷检测可视化
缺陷类型 ${b_1}/{\rm{mm}}$ ${b_2}/{\rm{mm}}$ ${{\varepsilon }}/{\text{% }}$
割痕缺陷1 40.0 33.88 15.3
3.0 3.13 4.3
2.0 1.85 7.5
割痕缺陷2 33.0 27.13 17.8
3.0 3.17 5.7
1.2 1.10 8.3
焊点缺陷 9.1 9.59 5.4
6.0 5.74 4.3
1.3 1.39 7.0
钻痕缺陷 7.0 6.69 4.4
7.0 6.68 4.6
2.5 2.26 9.6
结疤缺陷 4.4 4.64 5.5
4.1 4.30 4.9
0.7 0.76 8.6
表 5  多尺度特征增强的级联式三维重建网络对钢板表面缺陷的三维检测精度
检测步骤 $t/s$
割痕 焊痕 钻痕 结疤
滤波,降采样 1.78 1.98 1.46 1.34
点云平面分割 0.54 0.59 0.72 0.84
点云缺陷聚类 0.66 0.53 0.65 0.49
三维可视化 0.45 0.17 0.21 0.11
合计 3.43 3.27 3.04 2.78
表 6  基于所提点云数据处理流程的钢板表面缺陷检测各步骤耗时
图 15  基于多尺度特征增强的级联式三维重建网络的钢板表面缺陷三维检测结果
图 16  基于RetinaNet的钢板缺陷误检结果
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