WO2026020906A1 - 一款基于偏振光的柔性oled面板缺陷检测装置及检测方法 - Google Patents

一款基于偏振光的柔性oled面板缺陷检测装置及检测方法

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Publication number
WO2026020906A1
WO2026020906A1 PCT/CN2025/090782 CN2025090782W WO2026020906A1 WO 2026020906 A1 WO2026020906 A1 WO 2026020906A1 CN 2025090782 W CN2025090782 W CN 2025090782W WO 2026020906 A1 WO2026020906 A1 WO 2026020906A1
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Prior art keywords
flexible oled
angle
oled panel
defect
light
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PCT/CN2025/090782
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English (en)
French (fr)
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林斌
管安琪
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Zhejiang University
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Zhejiang University
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/0207—Details of measuring devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/0242—Testing optical properties by measuring geometrical properties or aberrations
    • G01M11/0257—Testing optical properties by measuring geometrical properties or aberrations by analyzing the image formed by the object to be tested
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
    • G01M11/02—Testing optical properties
    • G01M11/0242—Testing optical properties by measuring geometrical properties or aberrations
    • G01M11/0278—Detecting defects of the object to be tested, e.g. scratches or dust
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30121—CRT, LCD or plasma display

Definitions

  • This invention relates to the field of workpiece manufacturing and processing, specifically to a defect detection device and method for flexible OLED panels based on polarized light.
  • Flexible OLEDs consist of a flexible substrate, flexible electrodes, a transport layer, and light-emitting materials.
  • the complex manufacturing process easily leads to various display defects. These defects can react with organic materials, causing rapid device aging and significantly impacting the performance of the flexible screen. Different defects in different layers require differentiated detection, and different solutions can be implemented based on the type and location of the defect. Therefore, high-quality defect detection can effectively eliminate defective products, provide a basis for repairing repairable defects, and trace the causes of defects, thereby promoting improvements in the manufacturing process and increasing the yield rate.
  • Machine vision inspection utilizes optical devices such as machines to replace human eyes for measurement and judgment. It typically uses image acquisition equipment to convert the object under study into corresponding digital signals, extracts and analyzes the features of the converted signals, and then uses the analysis results to guide further equipment operation.
  • machine vision technology has matured and is widely used in manufacturing.
  • machine vision-based defect detection methods can achieve rapid, assembly-line defect detection, reducing costs, freeing up labor, and improving accuracy and efficiency.
  • many experts and scholars both domestically and internationally have conducted extensive research on machine vision-based defect detection and achieved significant results. However, there is still considerable room for improvement in the accuracy and speed of defect detection in flexible OLED panels. Research on defect detection in flexible OLED displays not only has promising application prospects but can also further promote the development of the flexible OLED display panel industry.
  • This invention achieves defect detection by building an optical system to acquire images of flexible OLED panel samples on a production line, designing algorithms based on various parameters of polarized light, and training a classifier algorithm.
  • This invention discloses a defect detection device for flexible OLED panels based on polarized light, comprising:
  • Sample transfer device includes a conveyor belt, used to transfer the flexible OLED panel sample to be tested to the collection area, and after the test is completed, transfer the flexible OLED panel sample to the subsequent work station;
  • the Acquisition device includes detector and light source.
  • the light source is used to illuminate the surface of the flexible OLED panel sample under test, and the detector is used to acquire the light intensity and power values of each point on the flexible OLED panel sample under test.
  • Polarization device including analyzer, second quarter wave plate, polarizer, and first quarter wave plate, used to acquire polarization information of the flexible OLED panel sample under test;
  • Angle transformation device includes an electric rotary table and a scale.
  • the electric rotary table is used to control the angle transformation of the polarization device, and the scale is used to control the angle transformation of the acquisition device.
  • Computer Used to synchronously control the acquisition speed of the acquisition device and the movement speed of the sample transfer device, and to acquire the light intensity and power values of each point of the tested flexible OLED panel sample through the acquisition device at different times; used to control the angle transformation device to change the angle at different times to obtain multiple sets of light intensity and power values, and to calculate different parameters; used to control the switching of the usage status of the No. 1 1/4 wave plate and the No. 2 1/4 wave plate and the switching of the light source type, so as to acquire different parameters of defect type and defect level location.
  • the dial includes dial support A and dial support B. From top to bottom, dial support A is fixed with a detector, analyzer and second 1/4 wave plate. From top to bottom, dial support B is fixed with a light source, polarizer and first 1/4 wave plate.
  • the computer is connected to the electric rotary table, conveyor belt, dial and light source.
  • the sampling area is the flexible OLED panel sample to be tested, which is simultaneously within the field of view of the detector and the illumination range of the light source.
  • the rotation of the angles of the analyzer, the second 1/4 wave plate, the polarizer, and the first 1/4 wave plate is achieved by the rotation of the electric rotary table located below them; the rotation of the incident light angle is achieved by the rotation of the scale shaft B; the rotation of the scattered light angle is achieved by the rotation of the scale shaft A; and the rotation of the electric rotary table, scale shaft A, and scale shaft B is achieved by computer control.
  • This invention also includes a method for detecting defects in flexible OLED panels based on polarized light, comprising the following steps:
  • the Mueller matrix parameters of the defective and non-defective regions are obtained by calculating the Stokes vector.
  • the optimal angle state is selected from multiple angles, and the Mueller matrix parameter values under the optimal angle state are used to train the classifier to obtain the trained classifier.
  • the layer position of the flexible OLED panel is fused as supplementary information into the result of defect type and pixel location to obtain the comprehensive result of defect detection as the conclusion output.
  • the multiple sets of experimental angles are: the incident light angle is in the range of 0° to 80°, and every 10° is a set of candidate incident light angles; the PSG angle is in the range of 0° to 180°, and every 10° is a set of candidate PSG angles.
  • step 5 the incident light angle is fixed at 60°, and the scattered light receiving angle ranges from 36° to 72°.
  • steps 1) and 3) of this invention are as follows:
  • the M parameter is a four-row, four-column matrix. The value of the first row and the first column is taken as the parameter value as the input data for training the classifier.
  • step 2) of the present invention is as follows:
  • the classifier training step in step 2) of the present invention is as follows:
  • step 6 the calculation step for the change trend of the polarization factor in step 6) of the present invention is as follows:
  • the criteria for determining whether the defect location in step 6) of this invention belongs to the surface above, subsurface, or micro-rough surface are as follows:
  • BRDF pp has a minimum value near the scattering azimuth angle of 36°, and BRDF pp varies with the scattering azimuth angle in other ranges, it indicates that the defect is on a slightly rough surface.
  • BRDF pp has a minimum value near the scattering azimuth angle of 72°, and BRDF pp varies with the scattering azimuth angle in other ranges, it indicates that the defect is on the subsurface.
  • This invention can quickly scan flexible OLED panel samples to be tested, perform comprehensive testing on the flexible OLED panel samples to be tested, and significantly improve testing efficiency;
  • This invention combines advanced image processing algorithms to automatically identify and classify various defects in flexible OLED panel samples under test, reducing labor and time costs.
  • This invention distinguishes different defects by calculating and comparing the differences in the Mueller matrix, which can identify a variety of defects on the surface of flexible OLED panels and broaden the defect detection range.
  • this invention can accurately locate the layer where the defect is located in the flexible OLED panel sample under test.
  • This invention uses a visual inspection scheme, which obtains defect information by calculating polarization information through a computer, thereby avoiding damage to the flexible OLED panel sample under test during the inspection process.
  • This invention combines the type of defect, pixel location, and layer location to provide a more detailed and comprehensive defect description method, thereby improving the quality of defect detection.
  • the detection algorithm of this invention has self-learning capability, and the defect classifier can be continuously improved as the sample data is expanded, thereby further improving the detection accuracy.
  • This invention can display the detection results in real time, is easy to operate, and has a user-friendly interface.
  • the present invention has a high accuracy rate of over 98% in detecting defects in flexible OLED panels.
  • FIG. 1 is a mechanical structure diagram of the system hardware, including a detector 1, an analyzer 2, a second 1/4 wave plate 3, a light source 4, a polarizer 5, a first 1/4 wave plate 6, a conveyor belt 7, a dial 8, an electric rotary table 9, a computer 10, a flexible OLED panel sample to be tested 11, a dial support B12, and a dial support A13. It can be divided into:
  • Sample transfer device includes conveyor belt 7, used to transfer the flexible OLED panel sample 11 to be tested to the collection area, and after the test is completed, transfer the flexible OLED panel sample to the subsequent station;
  • Acquisition device includes detector 1 and light source 4.
  • the light source is used to illuminate the surface of the flexible OLED panel sample under test, and the detector is used to acquire the light intensity value and power value of each point of the flexible OLED panel sample under test.
  • Polarization device including analyzer 2, second quarter wave plate 3, polarizer 5, and first quarter wave plate 6, used to acquire polarization information of the flexible OLED panel sample under test;
  • Angle transformation device includes an electric rotary table 9 and a scale 8.
  • the electric rotary table is used to control the angle transformation of the polarization device, and the scale is used to control the angle transformation of the acquisition device.
  • Computer Used to synchronously control the acquisition speed of the acquisition device and the movement speed of the sample transfer device, and to acquire the light intensity and power values of each point of the tested flexible OLED panel sample through the acquisition device at different times; used to control the angle transformation device to change the angle at different times to obtain multiple sets of light intensity and power values, and to calculate different parameters; used to control the switching of the usage status of the No. 1 1/4 wave plate 6 and the No. 2 1/4 wave plate 3 and the switching of the light source type, to acquire different parameters of defect type and defect level position;
  • the dial includes a dial support A13 and a dial support B12.
  • the dial support A has a detector 1, a polarizer 2 and a second 1/4 wave plate 3 fixed from top to bottom.
  • the dial support B has a light source 4, a polarizer 5 and a first 1/4 wave plate 6 fixed from top to bottom.
  • the computer is connected to the rotary table 9, the conveyor belt 7, the dial 8 and the light source 4.
  • the acquisition area is the flexible OLED panel sample 11 to be tested, which is simultaneously located within the field of view of the detector and the illumination range of the light source 4.
  • the rotation of the polarizer 2, the second 1/4 wave plate 3, the polarizer 5, and the first 1/4 wave plate 6 is achieved by the rotation of the electric rotary table 9 located below them; the rotation of the incident light angle is achieved by the rotation of the scale shaft B; the rotation of the scattered light angle is achieved by the rotation of the scale shaft A; and the rotation of the electric rotary table 9, the scale shaft A13, and the scale shaft B12 is achieved by computer control.
  • FIG. 1 is a flowchart of the detection process of the device of this invention, as shown in the figure:
  • Step 1 After the system starts, switch light source 4 to white light source, control the conveyor belt to move until the standard sample is placed in the measurement range, and obtain the Stokes vectors of the defective area and the non-defective area under multiple experimental angles.
  • the multiple experimental angles are as follows: the incident light angle is in the range of 0° to 80°, and every 10° is a set of candidate incident light angles; the PSG angle is in the range of 0° to 180°, and every 10° is a set of candidate PSG angles.
  • Step 2 Calculate the Mueller matrix parameters of the defective and non-defective regions using Stokes vectors, select the optimal angle state from multiple angles, and use the Mueller matrix parameter values under the optimal angle state to train the classifier, thus obtaining the trained classifier.
  • the Mueller matrix parameters are calculated as follows: Let the incident light Stokes vector be S and the outgoing light Stokes vector be S′.
  • M is a four-row, four-column matrix. The value of the first row and the first column is taken as the parameter value as the input data for training the classifier.
  • the training steps for the classifier are as follows:
  • Step 3 Adjust the angles of the scale shaft bracket B12, the polarizer 5, and the first 1/4 wave plate 6 to the optimal angle state, start the conveyor belt 7, control the conveyor belt to move until the flexible OLED panel sample to be tested is placed in the measurement range, control the electric rotary table 9, and obtain the Stokes vector of each pixel of the flexible OLED panel sample to be tested.
  • Step 4 Calculate the Mueller matrix parameters of each pixel in the flexible OLED panel sample under test using the Stokes vector of each pixel, input the parameters into the trained classifier for identification and detection, and obtain the defect type and the location of the pixel.
  • Step 5 Control the light source 4 to switch to a laser, switch the first 1/4 wave plate 6 and the second 1/4 wave plate 3 to non-use status, adjust the incident light angle of the scale bracket B12 to a fixed value, and adjust the scattered light receiving angle of the scale bracket A13 within the angle range to obtain the power value measured by the detector 1.
  • the incident light angle is fixed at 60°, and the scattered light receiving angle ranges from 36° to 72°.
  • Step 6 Calculate the variation trend of polarization factor of each pixel in the flexible OLED panel sample under test by power value, analyze whether the defect location belongs to the upper surface, subsurface or rough surface, and obtain the layer position of the defect in the flexible OLED panel.
  • the criteria for determining whether a defect is located on the upper surface, subsurface, or slightly rough surface are as follows:
  • BRDF pp has a minimum value near the scattering azimuth angle of 72°, and BRDF part changes with the scattering azimuth angle in other ranges, it indicates that the defect is on the subsurface.
  • Step 7 Integrate the layer position of the flexible OLED panel as supplementary information into the conclusion of defect type and pixel location, and output the comprehensive result of defect detection as the conclusion.

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Abstract

一种基于偏振光的柔性OLED面板缺陷检测装置，包括：样品传送装置：包括传送带（7）；采集装置：包括探测器（1）和光源（4）；偏振装置：包括检偏器（2）、二号1/4波片（3）、起偏器（5）和一号1/4波片（6），用于获取被测柔性OLED面板样品（11）的偏振信息；角度变换装置：包括电动旋转台（9）和刻度盘（8）；计算机（10）：用于在不同时刻通过采集装置采集被测柔性OLED面板样品（11）的各点光强值和功率值，在不同时刻变换角度获取多组光强值和功率值，实现不同参数的计算以及实现缺陷种类和缺陷层级位置的不同参数获取。还公开了基于偏振光的柔性OLED面板缺陷检测方法。

Description

一款基于偏振光的柔性OLED面板缺陷检测装置及检测方法 技术领域
本发明涉及工件制造加工领域，具体地说，是一种基于偏振光的柔性OLED面板缺陷检测装置及检测方法。
背景技术
柔性OLED由柔性衬底、柔性电极、传输层和发光材料构成，复杂的生产工艺过程易造成各类显示缺陷的产生，各类缺陷易与有机材料发生反应导致器件迅速老化，对柔性屏的性能产生较大影响。不同层的不同缺陷需要进行区别检测，根据缺陷种类和所在位置可进行对应的不同解决措施。因此对缺陷进行高质量检测可有效淘汰不合格品、为可修复缺陷提供修复依据、回溯分析缺陷产生原因，进而促进生产工艺的改善、良品率的提升。
传统的人工检测效率较低，不能满足流水线生产的需求，而且还会受疲劳、主观意识等因素的影响，造成错检、漏检等不足。机器视觉检测就是利用机器等光学设备代替人眼进行测量和判断，通常利用图像采集设备将研究对象转换为相对应的数字信号，对转换后的信号特征进行提取和判别，基于分析结果来进行下一步的设备操作。在过去几十年里机器视觉技术逐渐成熟，被广泛应用于制造业中。相比人工检测的方法，依托机器视觉的缺陷检测方法可以实现流水线式的快速缺陷检测，能够降低成本，解放劳动力，提高检测精确度和效率。目前，国内外许多专家学者做了基于机器视觉的缺陷检测的大量研究并取得了不错的成果，但是在柔性OLED面板缺陷检测的精确度和速度方面，仍然有较大的研究空间。研究柔性OLED显示屏缺陷检测不仅具有一定的应用前景，还可以进一步推动柔性OLED显示面板产业的发展。
在对表面透明度较高的柔性OLED面板进行缺陷检测时常伴随光的折射和散射带来的畸变影响，偏振光可以削弱来自反射表面的眩光或亮斑的影响，增强图像对比度，消除强反光。另外，需要对柔性OLED面板不同层产生的缺陷都进行检测，不同层出现的缺陷需要进行不同的处理，采用灰度强度图进行特征提取往往无法分辨缺陷在层之间的位置，而根据p偏振入射光引起的p偏振散射光的双向反射分布函数可以实现对缺陷所在层位置判定。确定缺陷种类、所在位置和所在层位置可以更好地为生产工艺提升提供依据。
本发明内容
本发明通过搭建光学系统，采集流水线上的柔性OLED面板样品图像，基于偏振光的各类参数设计算法，训练分类器算法实现缺陷检测。
本发明是通过以下技术方案来实现的：
本发明公开了一种基于偏振光的柔性OLED面板缺陷检测装置，包括：
样品传送装置：包括传送带，用于将待测柔性OLED面板样品传送至采集区域，检测结束后将被测柔性OLED面板样品传送至后续工位；
采集装置：包括探测器、光源，光源用于在被测柔性OLED面板样品表面打光，探测器用于获取被测柔性OLED面板样品各点的光强值和功率值；
偏振装置：包括检偏器、二号1/4波片、起偏器、一号1/4波片，用于获取被测柔性OLED面板样品的偏振信息；
角度变换装置：包括电动旋转台、刻度盘，电动旋转台用于控制偏振装置的角度变换，刻度盘用于控制采集装置的角度变换；
计算机：用于同步控制采集装置的采集速度和样品传送装置的运动速度，在不同时刻通过采集装置采集被测柔性OLED面板样品的各点光强值和功率值；用于控制角度变换装置的角度变换，在不同时刻变换角度获取多组光强值和功率值，实现不同参数的计算；用于控制一号1/4波片和二号1/4波片的使用状态切换和光源种类的切换，实现缺陷种类和缺陷层级位置的不同参数获取；
刻度盘包括刻度盘轴架A和刻度盘轴架B，刻度盘轴架A从顶端至下分别固定有探测器、检偏器和二号1/4波片，刻度盘轴架B从顶端至下分别固定有光源、起偏器和一号1/4波片，计算机分别与电动旋转台、传送带、刻度盘、光源相连。
采集区域为待测柔性OLED面板样品同时位于探测器的视野范围和光源的照射范围。
作为进一步地改进，本发明所述的装置中检偏器、二号1/4波片、起偏器、一号1/4波片角度的转动由位于其下方的电动旋转台的旋转实现，入射光角度的转动由刻度盘轴架B的旋转实现，散射光角度的转动由刻度盘轴架A的旋转实现，电动旋转台、刻度盘轴架A和刻度盘轴架B的旋转由计算机控制实现。
本发明还包括一种基于偏振光的柔性OLED面板缺陷检测方法，包括如下步骤：
1)切换光源为白光光源，控制传送带运动直至将标准样件置于测量范围，在多组实验角度下获得缺陷区域和无缺陷区域的斯托克斯矢量；
2)通过斯托克斯矢量计算得到缺陷区域和无缺陷区域的穆勒矩阵参数，从多组角度中选取最优角度状态，并用最优角度状态下的穆勒矩阵参数值进行分类器的训练，得到训练好的分类器；
3)调整刻度盘轴架B、起偏器和一号1/4波片角度为最优角度状态，启动传送带，控制传送带运动直至将待测柔性OLED面板样品置于测量范围，控制电动旋转台，获得待测柔性OLED面板样品各像素点的斯托克斯矢量；
4)通过各像素点的斯托克斯矢量计算待测柔性OLED面板样品各像素点的穆勒矩阵参数，输入训练好的分类器进行识别检测，得到缺陷种类和所在像素点位置；
5)控制光源切换为激光器，一号1/4波片和二号1/4波片切换为非使用状态，刻度盘轴架B调整入射光角度为固定值，刻度盘轴架A在角度范围内调整散射光接收角度，获得探测器测量的功率值；
6)通过功率值计算待测柔性OLED面板样品各像素点偏振因子的变化趋势，分析缺陷位置属于表面上方、亚表面或是粗糙表面，得到缺陷所在柔性OLED面板层级位置；
7)将柔性OLED面板层级位置作为补充信息融合至缺陷种类和所在像素点位置结果中，得到缺陷检测的综合结果作为结论输出。
作为进一步地改进，本发明所述的步骤1)中的多组实验角度为，入射光角度在0°到80°范围内每隔10°为一组待选入射光角度；PSG角度在0°到180°范围内每隔10°为一组待选PSG角度。
步骤5)中入射光角度的固定值为60°，散射光接收角度范围为36°～72°。
作为进一步地改进，本发明所述的步骤1)和步骤3)中对斯托克斯矢量的求取步骤为：
1)对PSA在(0°,0°)、(0°,45°)、(45°,45°)、(90°,90°)、(0°,135°)、(135°,135°)六种组合下记录光强值，记六组角度组合条件下得到的各像素点光强值分别为I1、I2、I3、I4、I5、I6；
2)通过各像素点光强值结合公式A1＝(I1+I4)、A2＝(I1-I4)、A3＝(I3-I6)、A4＝(I5-I2)，计算得到各像素点的斯托克斯矢量S＝[A1,A2,A3,A4]。
作为进一步地改进，本发明所述的步骤2)中穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S'，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值作为参数值作为训练分类器的输入数据。
步骤4)中穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S'，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值计算与标准样件同等角度状态下无缺陷区域的参数值的偏差作为检测分类器的输入数据。
作为进一步地改进，本发明所述的步骤2)中最优角度状态的选择步骤为：
1)计算各入射光角度和各PSG角度下各类缺陷区域的穆勒矩阵参数和无缺陷区域的穆勒矩阵参数；
2)以无缺陷区域的穆勒矩阵参数为标准，计算各类缺陷区域相较于无缺陷区域的偏差百分比；
3)设定偏差百分比等于30％为缺陷区域和无缺陷区域之间可区分的参考线，50％为不同缺陷区域之间可区分的参考线，选择符合区分标准的入射光角度和PSG角度，若符合标准的角度有多组，选择偏差最大的一组作为最优角度状态，若符合标准的角度只有一组，则选择该组为最优角度状态。
作为进一步地改进，本发明所述的步骤2)中分类器的训练步骤为：
1)将SVM支持向量机分类器调整为需要区别检测的缺陷种类数量加一的分类器(即，缺陷种类为N种，则SVM支持向量机调整为(N+1)分类器)；
2)在调整后的分类器中对最优角度状态进行多组重复性实验，计算多组穆勒矩阵参数偏差值作为输入数据集进行分类器的训练，保存训练完成的分类器模型用以后续使用。
作为进一步地改进，本发明所述的步骤6)中偏振因子的变化趋势计算步骤为：
1)将功率值转化为BRDFpp；
2)绘制BRDFpp变化曲线，得到BRDFpp随散射光角度变化而变化的趋势。
作为进一步地改进，本发明所述的步骤6)中缺陷位置属于表面上方、亚表面或是微粗糙表面的判断依据为：
1)若BRDFpp随着散射方位角的增大而增大，说明该处缺陷在表面上方；
2)若BRDFpp在散射方位角为36°附近出现极小值，其他范围内BRDFpp随散射方位角的变化而变化，说明该处缺陷在微粗糙表面；
3)若BRDFpp在散射方位角为72°附近出现极小值，其他范围内BRDFpp随散射方位角的变化而变化，说明该处缺陷在亚表面。
本发明的有益效果如下：
1.本发明可以快速扫描待测柔性OLED面板样品，对待测柔性OLED面板样品进行全面检测，显著提高检测效率；
2.本发明结合先进的图像处理算法，能够自动识别并分类待测柔性OLED面板样品的各缺陷，减少人力成本和时间成本。
3.本发明通过计算对比不同缺陷的穆勒矩阵差异进行区分，可以识别柔性OLED面板表面多种缺陷，拓宽缺陷检测范围。
4.本发明通过计算各像素点偏振因子的变化趋势，可以准确定位待测柔性OLED面板样品缺陷所在层的位置。
5.本发明使用视觉检测方案，通过计算机对偏振信息的计算得到缺陷信息，避免检测过程中对待测柔性OLED面板样品的损害。
6.本发明结合缺陷的种类、像素点位置和所在层级位置，提供更为细致和全面的缺陷描述方法，提高缺陷检测质量。
7.本发明的检测算法具备自学习能力，缺陷分类器可以随着样品数据的扩充而不断完善，进一步提高检测准确率。
8.本发明可实时显示检测结果，操作难度低，用户交互性友好。
9.本发明对柔性OLED面板缺陷检测的准确率很高，达到98％以上。
附图说明
图1装置结构示意图；
图2装置检测流程图。
具体实施方式
本发明公开了一种基于偏振光的柔性OLED面板缺陷检测装置，图1是系统硬件部分的机械结构图，包括探测器1、检偏器2、二号1/4波片3、光源4、起偏器5、一号1/4波片6、传送带7、刻度盘8、电动旋转台9、计算机10、待测柔性OLED面板样品11、刻度盘轴架B12和刻度盘轴架A13。可分为：
样品传送装置：包括传送带7，用于将待测柔性OLED面板样品11传送至采集区域，检测结束后将被测柔性OLED面板样品传送至后续工位；
采集装置：包括探测器1、光源4，所述的光源用于在被测柔性OLED面板样品表面打光，所述的探测器用于获取被测柔性OLED面板样品各点的光强值和功率值；
偏振装置：包括检偏器2、二号1/4波片3、起偏器5、一号1/4波片6，用于获取被测柔性OLED面板样品的偏振信息；
角度变换装置：包括电动旋转台9、刻度盘8，电动旋转台用于控制偏振装置的角度变换，刻度盘用于控制采集装置的角度变换；
计算机：用于同步控制采集装置的采集速度和样品传送装置的运动速度，在不同时刻通过采集装置采集被测柔性OLED面板样品的各点光强值和功率值；用于控制角度变换装置的角度变换，在不同时刻变换角度获取多组光强值和功率值，实现不同参数的计算；用于控制一号1/4波片6和二号1/4波片3的使用状态切换和光源种类的切换，实现缺陷种类和缺陷层级位置的不同参数获取；
所述的刻度盘包括刻度盘轴架A13和刻度盘轴架B12，所述的刻度盘轴架A从顶端至下分别固定有探测器1、检偏器2和二号1/4波片3，所述的刻度盘轴架B从顶端至下分别固定有光源4、起偏器5和一号1/4波片6，所述的计算机分别与旋转台9、传送带7、刻度盘8、光源4相连。
所述的采集区域为待测柔性OLED面板样品11同时位于探测器的视野范围和光源4的照射范围。
所述的装置中检偏器2、二号1/4波片3、起偏器5、一号1/4波片6角度的转动由位于其下方的电动旋转台9的旋转实现，入射光角度的转动由刻度盘轴架B的旋转实现，散射光角度的转动由刻度盘轴架A的旋转实现，电动旋转台9、刻度盘轴架A13和刻度盘轴架B12的旋转由计算机控制实现。
本发明还公开了一种基于偏振光的柔性OLED面板缺陷检测装置及检测方法，图2是本发明的装置检测流程图，如图所示：
步骤一：系统启动后，切换光源4为白光光源，控制传送带运动直至将标准样件置于测量范围，在多组实验角度下获得缺陷区域和无缺陷区域的斯托克斯矢量；
多组实验角度为，入射光角度在0°到80°范围内每隔10°为一组待选入射光角度；PSG角度在0°到180°范围内每隔10°为一组待选PSG角度。
对斯托克斯矢量的求取步骤为：
1)对PSA在(0°,0°)、(0°,45°)、(45°,45°)、(90°,90°)、(0°,135°)、(135°,135°)六种组合下记录光强值，记六组角度组合条件下得到的各像素点光强值分别为I1、I2、I3、I4、I5、I6；
2)通过各像素点光强值结合公式A1＝(I1+I4)、A2＝(I1-I4)、A3＝(I3-I6)、A4＝(I5-I2)，计算得到各像素点的斯托克斯矢量S＝[A1,A2,A3,A4]。
步骤二：通过斯托克斯矢量计算得到缺陷区域和无缺陷区域的穆勒矩阵参数，从多组角度中选取最优角度状态，并用最优角度状态下的穆勒矩阵参数值进行分类器的训练，得到训练好的分类器；
穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S′，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值作为参数值作为训练分类器的输入数据。
最优角度状态的选择步骤为：
1)计算各入射光角度和各PSG角度下各类缺陷区域的穆勒矩阵参数和无缺陷区域的穆勒矩阵参数；
2)以无缺陷区域的穆勒矩阵参数为标准，计算各类缺陷区域相较于无缺陷区域的偏差百分比；
3)设定偏差百分比等于30％为缺陷区域和无缺陷区域之间可区分的参考线，50％为不同缺陷区域之间可区分的参考线，选择符合区分标准的入射光角度和PSG角度，若符合标准的角度有多组，选择偏差最大的一组作为最优角度状态，若符合标准的角度只有一组，则选择该组为最优角度状态。
分类器的训练步骤为：
1)将SVM支持向量机分类器调整为需要区别检测的缺陷种类数量加一的分类器(即，缺陷种类为N种，则SVM支持向量机调整为(N+1)分类器)；
2)在调整后的分类器中对最优角度状态进行多组重复性实验，计算多组穆勒矩阵参数偏差值作为输入数据集进行分类器的训练，保存训练完成的分类器模型用以后续使用。
步骤三：调整刻度盘轴架B12、起偏器5和一号1/4波片6角度为最优角度状态，启动传送带7，控制传送带运动直至将待测柔性OLED面板样品置于测量范围，控制电动旋转台9，获得待测柔性OLED面板样品各像素点的斯托克斯矢量；
对斯托克斯矢量的求取步骤为：
1)对PSA在(0°,0°)、(0°,45°)、(45°,45°)、(90°,90°)、(0°,135°)、(135°,135°)六种组合下记录光强值，记六组角度组合条件下得到的各像素点光强值分别为I1、I2、I3、I4、I5、I6；
2)通过各像素点光强值结合公式A1＝(I1+I4)、A2＝(I1-I4)、A3＝(I3-I6)、A4＝(I5-I2)，计算得到各像素点的斯托克斯矢量S＝[A1,A2,A3,A4]。
步骤四：通过各像素点的斯托克斯矢量计算待测柔性OLED面板样品各像素点的穆勒矩阵参数，输入训练好的分类器进行识别检测，得到缺陷种类和所在像素点位置；
穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S′，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值计算与标准样件同等角度状态下无缺陷区域的参数值的偏差作为检测分类器的输入数据。
步骤五：控制光源4切换为激光器，一号1/4波片6和二号1/4波片3切换为非使用状态，刻度盘轴架B12调整入射光角度为固定值，刻度盘轴架A13在角度范围内调整散射光接收角度，获得探测器1测量的功率值；
入射光角度的固定值为60°，散射光接收角度范围为36°～72°。
步骤六：通过功率值计算待测柔性OLED面板样品各像素点偏振因子的变化趋势，分析缺陷位置属于表面上方、亚表面或是粗糙表面，得到缺陷所在柔性OLED面板层级位置；
偏振因子的变化趋势计算步骤为：
1)将功率值转化为BRDFpp；
2)绘制BRDFpp变化曲线，得到BRDFpp随散射光角度变化而变化的趋势。
对不同位置的BRDFpp表达式计算以及随散射光角度变化情况分类讨论如下所述。
1)若缺陷在表面上方，则其s光和p光的菲涅尔反射系数计算公式为：
偏振系数计算公式为：
偏振双向反射分布函数计算公式为：
2)若缺陷在表面上方，则其偏振系数计算公式为：
偏振双向反射分布函数计算公式为：
3)若缺陷在微粗糙表面，则其偏振系数计算公式为：
偏振双向反射分布函数计算公式为：
综合比较表面上方、亚表面和微粗糙表面的BRDF的偏振系数qss、qsp、qps、qpp的表达式，其中qss、qsp、qps三项每一项都存在着一个相同的函数项，那么在散射测量时三种散射机理就会发生叠加，则不能对这三种散射机理进行有效分离。qpp不存在单独的共同项，三者各自的表达式明显不同，可利用BRDFpp与散射角之间关系的不同对三种情况下的缺陷位置进行有效区分。
缺陷位置属于表面上方、亚表面或是微粗糙表面的判断依据为：
1)若BRDFpp随着散射方位角的增大而增大，说明该处缺陷在表面上方；
2)若BRDFpp在散射方位角为36°附近出现极小值，其他范围内BRDFpart随散射方位角的变化而变化，说明该处缺陷在微粗糙表面；
3)若BRDFpp在散射方位角为72°附近出现极小值，其他范围内BRDFpart随散射方位角的变化而变化，说明该处缺陷在亚表面。
步骤七：将柔性OLED面板层级位置作为补充信息融合至缺陷种类和所在像素点位置结论中，得到缺陷检测的综合结果作为结论输出。
最后，还需要注意的是，以上列举的仅是本发明的具体实施例。显然，本发明不限于以上实施例，还可以有许多变形。本领域的普通技术人员能从本发明公开的内容直接导出或联想到的所有变形，均应认为是本发明的保护范围。

Claims (10)

  1. 一种基于偏振光的柔性OLED面板缺陷检测装置，其特征在于，包括：
    样品传送装置：包括传送带，用于将待测柔性OLED面板样品传送至采集区域，检测结束后将被测柔性OLED面板样品传送至后续工位；
    采集装置：包括探测器、光源，所述的光源用于在被测柔性OLED面板样品表面打光，所述的探测器用于获取被测柔性OLED面板样品各点的光强值和功率值；
    偏振装置：包括检偏器、二号1/4波片、起偏器、一号1/4波片，用于获取被测柔性OLED面板样品的偏振信息；
    角度变换装置：包括电动旋转台、刻度盘，电动旋转台用于控制偏振装置的角度变换，刻度盘用于控制采集装置的角度变换；
    计算机：用于同步控制采集装置的采集速度和样品传送装置的运动速度，在不同时刻通过采集装置采集被测柔性OLED面板样品的各点光强值和功率值；用于控制角度变换装置的角度变换，在不同时刻变换角度获取多组光强值和功率值，实现不同参数的计算；用于控制一号1/4波片和二号1/4波片的使用状态切换和光源种类的切换，实现缺陷种类和缺陷层级位置的不同参数获取；
    所述的刻度盘包括刻度盘轴架A和刻度盘轴架B，所述的刻度盘轴架A从顶端至下分别固定有探测器、检偏器和二号1/4波片，所述的刻度盘轴架B从顶端至下分别固定有光源、起偏器和一号1/4波片，所述的计算机分别与电动旋转台、传送带、刻度盘、光源相连。
    所述的采集区域为待测柔性OLED面板样品同时位于探测器的视野范围和光源的照射范围。
  2. 根据权利要求1所述的基于偏振光的柔性OLED面板缺陷检测装置，其特征在于，所述的装置中检偏器、二号1/4波片、起偏器、一号1/4波片角度的转动由位于其下方的电动旋转台的旋转实现，入射光角度的转动由刻度盘轴架B的旋转实现，散射光角度的转动由刻度盘轴架A的旋转实现，电动旋转台、刻度盘轴架A和刻度盘轴架B的旋转由计算机控制实现。
  3. 一种基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，包括如下步骤：
    1)切换光源为白光光源，控制传送带运动直至将标准样件置于测量范围，在多组实验角度下获得缺陷区域和无缺陷区域的斯托克斯矢量；
    2)通过斯托克斯矢量计算得到缺陷区域和无缺陷区域的穆勒矩阵参数，从多组角度中选取最优角度状态，并用最优角度状态下的穆勒矩阵参数值进行分类器的训练，得到训练好的分类器；
    3)调整刻度盘轴架B、起偏器和一号1/4波片角度为最优角度状态，启动传送带，控制传送带运动直至将待测柔性OLED面板样品置于测量范围，控制电动旋转台，获得待测柔性OLED面板样品各像素点的斯托克斯矢量；
    4)通过各像素点的斯托克斯矢量计算待测柔性OLED面板样品各像素点的穆勒矩阵参数，输入训练好的分类器进行识别检测，得到缺陷种类和所在像素点位置；
    5)控制光源切换为激光器，一号1/4波片和二号1/4波片切换为非使用状态，刻度盘轴架B调整入射光角度为固定值，刻度盘轴架A在角度范围内调整散射光接收角度，获得探测器测量的功率值；
    6)通过功率值计算待测柔性OLED面板样品各像素点偏振因子的变化趋势，分析缺陷位置属于表面上方、亚表面或是粗糙表面，得到缺陷所在柔性OLED面板层级位置；
    7)将柔性OLED面板层级位置作为补充信息融合至缺陷种类和所在像素点位置结果中，得到缺陷检测的综合结果作为结论输出。
  4. 根据权利要求3所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤1)中的多组实验角度为，入射光角度在0°到80°范围内每隔10°为一组待选入射光角度；PSG角度在0°到180°范围内每隔10°为一组待选PSG角度。
    所述的步骤5)中入射光角度的固定值为60°，散射光接收角度范围为36°～72°。
  5. 根据权利要求3所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤1)和步骤3)中对斯托克斯矢量的求取步骤为：
    1)对PSA在(0°,0°)、(0°,45°)、(45°,45°)、(90°,90°)、(0°,135°)、(135°,135°)六种组合下记录光强值，记六组角度组合条件下得到的各像素点光强值分别为I1、I2、I3、I4、I5、I6；
    2)通过各像素点光强值结合公式A1＝(I1+I4)、A2＝(I1-I4)、A3＝(I3-I6)、A4＝(I5-I2)，计算得到各像素点的斯托克斯矢量S＝[A1,A2,A3,A4]。
  6. 根据权利要求3或4所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤2)中穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S'，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值作为参数值作为训练分类器的输入数据。
    所述的步骤4)中穆勒矩阵参数的计算为：记入射光斯托克斯矢量为S，出射光斯托克斯矢量为S'，根据公式S'＝M·S可计算得到各像素点的穆勒矩阵参数M，M参数是一个四行四列的矩阵，取第一行第一列的值计算与标准样件同等角度状态下无缺陷区域的参数值的偏差作为检测分类器的输入数据。
  7. 根据权利要求6所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤2)中最优角度状态的选择步骤为：
    1)计算各入射光角度和各PSG角度下各类缺陷区域的穆勒矩阵参数和无缺陷区域的穆勒矩阵参数；
    2)以无缺陷区域的穆勒矩阵参数为标准，计算各类缺陷区域相较于无缺陷区域的偏差百分比；
    3)设定偏差百分比等于30％为缺陷区域和无缺陷区域之间可区分的参考线，50％为不同缺陷区域之间可区分的参考线，选择符合区分标准的入射光角度和PSG角度，若符合标准的角度有多组，选择偏差最大的一组作为最优角度状态，若符合标准的角度只有一组，则选择该组为最优角度状态。
  8. 根据权利要求3或4或5或7所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤2)中分类器的训练步骤为：
    1)将SVM支持向量机分类器调整为需要区别检测的缺陷种类数量加一的分类器(即，缺陷种类为N种，则SVM支持向量机调整为(N+1)分类器)；
    2)在调整后的分类器中对最优角度状态进行多组重复性实验，计算多组穆勒矩阵参数偏差值作为输入数据集进行分类器的训练，保存训练完成的分类器模型用以后续使用。
  9. 根据权利要求8所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤6)中偏振因子的变化趋势计算步骤为：
    1)将功率值转化为BRDFpp；
    2)绘制BRDFpp变化曲线，得到BRDFpp随散射光角度变化而变化的趋势。
  10. 根据权利要求9所述的基于偏振光的柔性OLED面板缺陷检测方法，其特征在于，所述的步骤6)中缺陷位置属于表面上方、亚表面或是微粗糙表面的判断依据为：
    1)若BRDFpp随着散射方位角的增大而增大，说明该处缺陷在表面上方；
    2)若BRDFpp在散射方位角为36°附近出现极小值，其他范围内BRDFpp随散射方位角的变化而变化，说明该处缺陷在微粗糙表面；
    3)若BRDFpp在散射方位角为72°附近出现极小值，其他范围内BRDFpp随散射方位角的变化而变化，说明该处缺陷在亚表面。
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