纸膜双层缺陷检测的视觉成像光场设计 -

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纸膜双层缺陷检测的视觉成像光场设计

doi:10.37188/CO.2023-0134

蒋仕飞^{1, 2,},
张兆国^{1, 3},
王法安^3,,,
解开婷^{1, 3},
王成琳³,
李治²

1.
昆明理工大学机电工程学院, 云南昆明 650500
2.
云南烟草机械有限责任公司, 云南昆明 650000
3.
昆明理工大学现代农业工程学院, 云南昆明 650500

基金项目:国家重点研发计划项目（No. 2022YFD2002004）；云南省院士（专家）工作站项目（No. 202105AF150030）；云南中烟重点项目（No. 2022ZK05）

详细信息

作者简介:
蒋仕飞（1987—），男，云南昭通人，博士研究生，工程师，2014年于昆明理工大学获得硕士学位，主要从事机器视觉及自动检测方面的研究。E-mail：20211103008@stu.kust.edu.cn
王法安（1990—），男，河南信阳人，博士，硕士生导师，2014年于安阳工学院获得学士学位，2017年于昆明理工大学获得硕士学位，2022年于东南大学获得博士学位，主要从事智能机械装备设计制造研究等方面的研究。E-mail：wfa@kust.edu.cn

中图分类号:TB811;TB858.2
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出版历程
- 收稿日期:2023-08-09
- 修回日期:2023-09-12
- 网络出版日期:2023-12-05

Design of optical field of vision imaging for defect detection of paper and transparent film

1.
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2.
Yunnan Tobacco Machinery Co., Ltd., Kunming 650000, China
3.
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China

Funds:Supported by National Key R & D Program of China (No. 2022YFD2002004); Yunnan Province Academician (Expert) Workstation Project (No. 202105AF150030); Key Project of China Tobacco Industry in Yunnan (No. 2022ZK05)

More Information

Corresponding author:wfa@kust.edu.cn

摘要

摘要:
为实现包装盒纸质基底层和透明膜层缺陷的同步检测，开展了对纸和膜缺陷的同步成像研究。首先，分别建立了标准球面积分光场、椭球面积分光场和弧面积分光场模型，并利用COMSOL Multiphysics 5.6对3类光场进行射线仿真，对比分析了球面积分光场下射线角度均匀性及辐照均匀性，通过正交仿真优化椭球面积分光场参数；其次，在椭球面积分光场环境、亮场前打光环境、暗场前打光环境下对包装盒成像。与此同时，对包装盒的油污、抵触、开口、泡皱、破损5项常见缺陷依次进行物理检测和机器视觉检测，验证缺陷成像的有效性。试验结果表明，在椭球面积分光场下成像，图像对纸质基底层缺陷特征、透明膜层缺陷特征均有较好的呈现效果，图像上油污、抵触、开口、泡皱、破损的物理检出率分别为96.2%、92.5%、100%、95%、92%，异常检出率分别为98.6%、97.5%、100%、100%、98.4%，缺陷类别检出率分别为97.6%、96%、100%、97%、96%。研究结果表明，椭球面积分光场光路角度和辐照强度均匀，覆透明膜包装盒的缺陷特征呈现清晰，满足工业生产的检测要求。
- 光路仿真/
- 透明膜成像/
- 球面积分光场/
- 缺陷特征成像/
- 视觉检测/
- COMSOL Multiphysics 5.6射线仿真
Abstract:
To achieve synchronous detection of defects in the paper and transparent film layers of packaging boxes, we studied the synchronous imaging of the paper and film defect. Firstly, we established models for a standard sphere integral light field, an ellipsoidal integral light field, and an arc integral light field. We then simulated three different light fields using COMSOL Multiphysics 5.6 and compared their ray angle uniformity and irradiation uniformity. The parameters of ellipsoidal area integral light field are optimized by orthogonal simulation. Secondly, the packaging box was imaged using the ellipsoidal integral light field, the bright and dark field forward lighting. Physical detection and machine vision were used to detect five common defects in the packaging box, including oil stains, pressure marks, openings, bubble wrinkles, and breakages, to verify the effectiveness of defect imaging. The results show that the images can clearly present defect characteristics in the paper base and transparent film layers under ellipsoidal integral light field. The physical detection rates for oil stains, pressure marks, openings, bubble wrinkles, and breakages were 96.2%, 92.5%, 100%, 95%, and 92%, respectively. Anomaly detection rates were 98.6%, 97.5%, 100%, 100%, 98.4%, respectively. Detection rates of defects were 97.6%, 96%, 100%, 97%, and 96%, respectively. This study indicates that the consistent optical path angle and irradiation intensity result in a uniform ellipsoidal integral light field. Consequently, transparent film imaging of the packaging box shows clear defect characteristics that satisfy the standards for industrial detection application.
- optical path simulation/
- transparent film imaging/
- spherical integrated light field/
- defect feature imaging/
- visual inspection/
- COMSOL Multiphysics 5.6 ray simulation

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图 1亮场前打光方式对纸质基底层的成像

Figure 1.Imaging of paper in bright field forward lighting

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图 2暗场前打光方式对透明膜的成像

Figure 2.Imaging of transparent film in dark field forward lighting

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图 3曲面反射光路图

Figure 3.Optical path diagram of curved surface reflection

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图 4标准球面积分光场模型

Figure 4.Spherical integral light field model

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图 5椭球面内球面积分光场模型

Figure 5.Ellipsoidal integral light field model

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图 6弧面积分光场模型

Figure 6.Arc-area integral light field model

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图 7射线以（a）30°、（b）45°、（c）60°向上释放的球面光场光路

Figure 7.Optical paths of a spherical light field with rays emitted upward at (a) 30°, (b) 45°, (c) 60°

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图 8在长半轴端射线以（a）30°、（b）45°、（c）60°向上释放的椭球面光场光路

Figure 8.Optical paths of an ellipsoidal light field with rays emitted upward at the end of semi-major axis at (a) 30°, (b) 45°, (c) 60°

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图 9在短半球端射线以（a）30°、（b）45°、（c）60°向上释放的椭球面光场光路

Figure 9.Optical paths of an ellipsoidal light field with rays emitted upward at the end of short haff-shaft at (a) 30°, (b) 45°, (c) 60°

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图 10弧面积分光场仿真结果

Figure 10.Simulation results of arc-area integral light field

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图 11半径比与投射口距离对辐照比的响应曲面

Figure 11.Response surface plotted by three coefficients, ie., radius radio, projection port distance, and irradiation ratio

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图 12半径比与辐照面距离对辐照比的响应曲面

Figure 12.Response surface plotted by three coefficients, i.e., radius ratio, irradiation surface distance, and irradiation ratio

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图 13成像系统结构设计图

Figure 13.Structural design graph of imaging system

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图 14试验设备

Figure 14.Experiment equipment

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图 15前打光环境成像装置

Figure 15.Imaging device of forward lighting field

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图 16包装盒缺陷图像

Figure 16.Defect images of packaging box

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图 17不同光环境下成像缺陷比例

Figure 17.Presentation rates of imaging defects under different light fields

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图 18PaDiM异常检测的patch 嵌入向量与定位分割

Figure 18.Patch embedding vector and location segmentation of PaDiM abnormal detection

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图 19缺陷的异常检测和类别检测结果

Figure 19.Abnormal detection and classification detection results of defects

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图 20缺陷检出率雷达图

Figure 20.Radar map of defects detection rate

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表 13种光场主要参数

Table 1.Main parameters of the three types of integrated light fields

光场类型	光罩尺寸A ×B×H/mm	半径R或R₁×R₂×R₃/mm	投射口距离S/mm	辐照面距离C/mm
标准球面积分光场	180×180×130	85	35	20
椭球面积分光场1	180×180×110	85×65×65	35	20
椭球面积分光场2	180×180×110	65×85×65	35	20
弧面积分光场	180×130×130	85	35	20

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表 2因素水平表

Table 2.Coding table of experimental factors and levels

水平	因素
水平	半径比（R₁/R₂）	投射口距离S/（mm）	辐照面距离C/( mm)
1	1.1	30	5
0	1.3	35	17.5
-1	1.5	40	30

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表 3试验方案与结果

Table 3.Experimental scheme and results

序号	因素			辐照比Q/%
序号	R₁/R₂	S/（mm）	C/( mm)	辐照比Q/%
1	1.3	40	30	80
2	1.1	35	5	76
3	1.5	35	5	74
4	1.3	35	17.5	92
5	1.1	30	17.5	76
6	1.3	30	30	82
7	1.5	35	30	70
8	1.3	35	17.5	90
9	1.3	35	17.5	90
10	1.5	40	17.5	70
11	1.3	30	5	86
12	1.3	40	5	84
13	1.3	35	17.5	92
14	1.3	35	17.5	92
15	1.1	40	17.5	74
16	1.1	35	30	72
17	1.5	30	17.5	72

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表 4方差分析

Table 4.Variance analysis

变异来源	平方和	自由度	均方	F	P
模型	1104.73	9	122.75	126.36	< 0.0001***
R₁:R₂	18.00	1	18.00	18.53	0.0035***
S	8.00	1	8.00	8.24	0.0240**
C	32.00	1	32.00	32.94	0.0007***
残差	6.80	7	0.9714
失拟	2.00	3	0.6667	0.5556	0.6716
误差	4.80	4	1.20
总和	1111.53	16
注：表示影响显著（P<0.05），*表示影响极显著（P<0.01）

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表 5缺陷检出数量统计

Table 5.Statistics on detected defects

缺陷类别	缺陷数量	椭球面积分光场环境		亮场前打光环境		暗场前打光环境
缺陷类别	缺陷数量	缺陷检出数量	缺陷检出率/%	缺陷检出数量	缺陷检出率/%	缺陷检出数量	缺陷检出率/%
油污	213	205	96.2	206	96.7	138	64.8
抵触	80	74	92.5	76	95	54	67.5
开口	50	50	100	50	100	38	76
泡皱	98	93	95	64	65	93	95
破损	125	115	92	74	59	116	92.8

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表 6图像缺陷的机器视觉检测结果

Table 6.Machine Vision detection results of image defects

缺陷类别	缺陷数量	椭球面积分光场环境				亮场前打光环境				暗场前打光环境
缺陷类别	缺陷数量	异常检出数量	异常检出率/%	类别检出数量	类别检出率/%	异常检出数量	异常检出率/%	类别检出数量	类别检出率/%	异常检出数量	异常检出率/%	类别检出数量	类别检出率/%
油污	213	210	98.6	208	97.6	210	98.6	210	98.6	140	65.7	140	65.7
抵触	80	78	97.5	75	96	79	98.7	78	97.5	60	75	62	77.5
开口	50	50	100	50	100	50	100	50	100	38	76	40	80
泡皱	98	98	100	95	97	70	71.4	67	68	95	97	95	97
破损	125	123	98.4	120	96	82	65.5	80	64	120	96	122	97.6

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