基于深度学习的焊缝PAUT数据智能化分析方法

朱甜甜; 宋波; 毛捷; 廉国选

doi:10.13700/j.bh.1001-5965.2020.0578

基于深度学习的焊缝PAUT数据智能化分析方法

doi: 10.13700/j.bh.1001-5965.2020.0578

朱甜甜^{1, 2},
宋波^1, ,,
毛捷^{1, 2},
廉国选¹

1.
中国科学院声学研究所声场声信息国家重点实验室, 北京 100190
2.
中国科学院大学电子电气与通信工程学院, 北京 100049

基金项目:

船舶建造焊缝质量数字化检测技术研究项目

详细信息

通讯作者:
宋波, E-mail: songbo@mail.ioa.ac.cn

中图分类号: TP399
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-12
- 录用日期: 2020-12-28
- 网络出版日期: 2022-03-20

PAUT data intelligent analysis method of welding seams based on deep learning

ZHU Tiantian^{1, 2},
SONG Bo^{1
, ,},
MAO Jie^{1, 2},
LIAN Guoxuan¹

1.
State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

Research on Digital Inspection Technology for Ship Construction Weld Quality

More Information

Corresponding author: SONG Bo, E-mail: songbo@mail.ioa.ac.cn

摘要

摘要:
超声相控阵检测技术(PAUT)凭借其突出的技术优势被广泛应用在船舶、铁路、石油石化和航空航天等诸多领域。在焊缝超声相控阵检测(PAUT)中，对检测数据缺陷的识别定位目前多采用传统的人工判读方式，判读效率较低，对检测人员的判读经验有较高要求，难以满足自动化超声检测的要求。基于深度学习中的目标检测和跟踪算法构建智能识别模型，通过对焊缝超声相控阵检测的S、B扫图特征进行融合，并结合焊缝的三维结构信息，识别并定位出缺陷在焊缝中的三维空间位置。实验结果显示: 缺陷框的平均三维IOU(预测三维缺陷框和实际三维缺陷框的平均交并比)达到0.644 9，较为接近缺陷的真实空间位置，可以实现焊缝超声相控阵检测成像结果智能识别和定位。
- 超声相控阵检测(PAUT) /
- 焊缝检测 /
- 深度学习 /
- 目标检测 /
- 跟踪算法 /
- 缺陷识别 /
- 三维定位
Abstract:
In the welding seam phased array ultrasonic testing (PAUT), the traditional manual judgment method is used to identify and locate the defects in the inspection data. However, this method has lower interpretation efficiency and higher requirements for the experience of the inspectors, and it has difficulty for meeting the requirements of automated ultrasound inspection. In this paper, combined with the features of S and B scan images of welding seam PAUT and 3D structure of weld, an intelligent recognition model based on target detection and tracking algorithm in deep learning is proposed to identify and locate the weld defect automatically. The experimental results show that the average value of 3D IOU of the defects (the average intersection ratio of the predicted and the actual 3D defect frame) reaches 0.644 9, which is close to the real defects' location. This method can realize the intelligent recognition and positioning from PAUT imaging data in welding seam.
- phased array ultrasonic testing (PAUT) /
- weld seam testing /
- deep learning /
- target detection /
- tracking algorithm /
- defect recognition /
- three-dimensional positioning

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图 1 超声相控阵检测B、C、D、S扫图显示方式

Figure 1. B-, C-, D- and S-scan images display mode of phased array ultrasonic testing

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图 2 基于深度学习的焊缝超声相控阵检测缺陷的自动识别和定位流程

Figure 2. Flowchart of automatic identification and positioning of welding seam phased array ultrasonic testing defects based on deep learning

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图 3 Faster R-CNN算法架构

Figure 3. Architecture of Faster R-CNN algorithm

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图 4 S扫图和B扫图样本的标记示意图

Figure 4. Schematic diagram of marking of S-scan and B-scan image samples

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图 5 一次波、二次波声束图及其检测焊缝缺陷示意图

Figure 5. Schematic diagram of primary and secondary acoustic beams and their detection of weld seam defects

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图 6 缺陷联合检验算法流程

Figure 6. Flowchart of defect joint inspection algorithm

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图 7 S扫图和B扫图的PR曲线

Figure 7. PR curves of S-scan and B-scan images

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图 8 S扫图的误判和B扫图的漏检示意图

Figure 8. Schematic diagram of misjudgment of S-scan images and missed detection of B-scan images

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图 9 含有伪缺陷信息的S扫图和B扫图

Figure 9. S-scan and B-scan images with false defect information

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图 10 Faster R-CNN检测的S扫图和联合缺陷检验筛选后的S扫图

Figure 10. S-scan image detected by Faster R-CNN and S-scan image after defect joint inspection algorithm detection

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图 11 S扫图初步检测和联合检测对比

Figure 11. Comparison of S-scan images preliminary detection and joint detection

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图 12 焊缝1的缺陷跟踪结果

Figure 12. Defect tracking results of No.1 weld seam

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图 13 焊缝1和焊缝2中缺陷位置的三维模型

Figure 13. Three-dimensional model of defect position in No.1 weld seam and No.2 weld seam

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表 1 焊缝样本缺陷信息

Table 1. Defect information of weld seam sample

参数	数值
缺陷总个数	56
未熔合缺陷个数	15
焊趾裂纹个数	15
根部裂纹个数	8
体积型缺陷个数	18

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表 2 Faster R-CNN在验证集上的验证结果

Table 2. Faster R-CNN verification results on verification set

显示形式	验证集样本数	平均召回率	平均准确率	AP值
S扫图	496	0.517 8	0.999 2	0.909 1
B扫图	168	0.500 4	0.998 3	0.908 1

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表 3 Faster R-CNN在测试集上的测试结果

Table 3. Faster R-CNN test results on test set

显示形式	测试集样本数	漏检样本数	误判样本数	准确率
S扫图	600	0	5	0.991 7
B扫图	62	4	0	0.935 5

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表 4 预测的三维缺陷框的位置信息

Table 4. Location information of predicted 3D defect box

焊缝	缺陷ID	缺陷框中心与焊缝中心距离/mm	缺陷框中心埋深/mm	缺陷框中心切片位置/mm	缺陷框长度/ mm	缺陷框宽度/ mm	缺陷框高度/ mm
1	1	-7.71	1.34	2.95	3.90	2.56	2.62
	3	2.60	6.42	96.95	11.90	4.02	5.19
	4	3.72	10.81	130.65	5.90	2.69	2.54
2	1	-3.54	7.47	208.80	35.60	3.45	4.29
	2	6.77	1.62	245.45	27.70	3.90	3.53
	3	6.63	1.10	270.15	3.90	2.51	2.24
	4	8.46	2.14	284.55	8.90	3.64	4.22

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表 5 实际的三维缺陷框的位置信息

Table 5. Location information of actual 3D defect box

焊缝	缺陷ID	缺陷框中心与焊缝中心距离/mm	缺陷框中心埋深/mm	缺陷框中心切片位置/mm	缺陷框长度/ mm	缺陷框宽度/ mm	缺陷框高度/ mm
焊缝1	1	7.90	1.25	3.45	4.90	2.20	2.30
	3	2.52	6.20	95.50	8.00	3.40	4.70
	4	3.95	10.6	130.15	6.90	2.30	2.80
焊缝2	1	3.60	7.70	207.80	31.60	3.60	3.80
	2	7.00	2.05	248.90	18.80	4.20	4.10
	3	6.40	1.30	270.15	3.90	2.20	2.60
	4	8.70	1.90	284.55	8.90	3.00	3.80

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表 6 三维IOU计算结果

Table 6. Calculated results of 3D IOU

焊缝	缺陷ID	IOU_V	IOU_V
焊缝1	1	0.627 5	0.644 9
	3	0.514 9
	4	0.643 3
焊缝2	1	0.760 3
	2	0.527 4
	3	0.709 2
	4	0.732 0

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