TWI771594B - Training method for defect and system using same, and determent method for defect and system using same - Google Patents

Training method for defect and system using same, and determent method for defect and system using same Download PDF

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TWI771594B
TWI771594B TW108121332A TW108121332A TWI771594B TW I771594 B TWI771594 B TW I771594B TW 108121332 A TW108121332 A TW 108121332A TW 108121332 A TW108121332 A TW 108121332A TW I771594 B TWI771594 B TW I771594B
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defect
area
image
value
standard deviation
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TW202101306A (en
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吳精文
李世欣
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住華科技股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06T7/0006—Industrial image inspection using a design-rule based approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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  • Computer Vision & Pattern Recognition (AREA)
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  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
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Abstract

The training method for defect includes the following steps. Firstly, an image of an optical film is captured, the image includes a defect area. Then, a plurality of coordinate values of a plurality of boundary points of a defect boundary of the defect area along a direction is obtained. Then, an average coordinate value of the coordinate values is obtained. Then, the difference between each coordinate value and the average coordinate value is obtained. Then, based on the largest one of the differences, the area in the image as the defect determent machine learning is determined.

Description

缺陷判斷訓練方法及應用其之系統以及缺陷判斷方 法及應用其之系統 Defect judgment training method and its application system and defect judgment method Laws and systems applying them

本發明是有關於一種訓練方法及應用其之系統以及判斷方法及應用其之系統，且特別是有關於一種缺陷判斷訓練方法及應用其之系統以及缺陷判斷方法及應用其之系統。 The present invention relates to a training method and a system using the same, a judging method and a system using the same, and more particularly, a training method for judging defects and a system using the same, and a method for judging defects and a system using the same.

習知光學膜在製作完成後必須以人工肉眼觀察方式，觀察光學膜是否具有缺陷以及缺陷種類。然而，人工肉眼觀察方式容易造成誤判。因此，提出一種新的可增加判斷準確度的缺陷判斷技術是本技術領域業者努力的目標之一。 The conventional optical film must be manually observed to observe whether the optical film has defects and the types of defects after the production is completed. However, the artificial naked eye observation method is prone to misjudgment. Therefore, it is one of the goals of those skilled in the art to propose a new defect judgment technology that can increase the judgment accuracy.

本發明實施例提出一種缺陷判斷訓練方法及應用其之系統以及缺陷判斷方法及應用其之系統，可改善上述問題。 Embodiments of the present invention provide a defect judgment training method and a system using the same, as well as a defect judgment method and a system using the same, which can improve the above problems.

本發明一實施例提出一種缺陷判斷訓練方法。缺陷判斷訓練方法包括以下步驟。擷取一光學膜之一圖像，圖像包含一缺陷區；取得缺陷區的一缺陷邊界之數個邊界點沿一方向的數個座標值；取得 此些座標值之一平均座標值；取得各座標值與平均座標值之一差值；以及，依據此些差值之最大者，決定圖像中做為缺陷判斷機器學習的區域。 An embodiment of the present invention provides a defect judgment training method. The defect judgment training method includes the following steps. Capture an image of an optical film, the image includes a defect area; obtain a plurality of coordinate values along a direction of a plurality of boundary points of a defect boundary of the defect area; obtain One of these coordinate values is an average coordinate value; a difference between each coordinate value and the average coordinate value is obtained; and, according to the largest of these difference values, an area in the image to be used for defect judgment machine learning is determined.

本發明另一實施例提出一種缺陷判斷訓練系統。缺陷判斷訓練系統包括一攝像器及一缺陷判斷機器學習器。攝像器用以擷取一光學膜之一圖像，圖像包含一缺陷區。缺陷判斷機器學習器用以：取得缺陷區的一缺陷邊界之數個邊界點沿一方向的數個座標值；取得此些座標值之一平均座標值；取得各座標值與平均座標值之一差值；以及，依據此些差值之最大者，決定圖像中做為缺陷判斷機器學習的區域。 Another embodiment of the present invention provides a defect judgment training system. The defect judgment training system includes a camera and a defect judgment machine learner. The camera is used for capturing an image of an optical film, and the image includes a defect area. The defect judgment machine learner is used to: obtain a plurality of coordinate values along a direction of a plurality of boundary points of a defect boundary of a defect area; obtain an average coordinate value of these coordinate values; obtain a difference between each coordinate value and the average coordinate value value; and, according to the largest of these differences, determine the region in the image to be used for defect judgment machine learning.

本發明一實施例提出一種缺陷判斷方法。缺陷判斷方法包括以下步驟。擷取一光學膜之一圖像，圖像具有一待判定缺陷區；分析待判定缺陷區，並產生待判定缺陷區相對於數個缺陷類型之各者的一相似度分數；判斷此些相似度分數中最高者所對應的缺陷類型是否屬於此些缺陷類型之一特定者；當最高者所對應的缺陷類型屬於特定者，判斷最高者是否大於一預設值；當最高者大於預設值，判定待判定缺陷區屬於特定者；以及，當最高者不大於預設值，判定待判定缺陷區屬於此些缺陷類型之另一者。 An embodiment of the present invention provides a defect judgment method. The defect judgment method includes the following steps. Capture an image of an optical film, the image has a defect area to be determined; analyze the defect area to be determined, and generate a similarity score of the defect area to be determined relative to each of several defect types; determine the similarity Whether the defect type corresponding to the highest degree score belongs to a specific one of these defect types; when the defect type corresponding to the highest degree belongs to a specific one, determine whether the highest one is greater than a preset value; when the highest one is greater than the preset value , determine that the defect area to be determined belongs to a specific one; and, when the highest value is not greater than the preset value, determine that the defect area to be determined belongs to another of these defect types.

本發明一實施例提出一種缺陷判斷系統。缺陷判斷系統包括一攝像器及一缺陷判斷器。攝像器用以擷取一光學膜之圖像，圖像具有一待判定缺陷區。缺陷判斷器用以：分析待判定缺陷區，並產生待判定缺陷區相對於數個缺陷類型之各者的一相似度分數；判斷此 些相似度分數中最高者所對應的缺陷類型是否屬於此些缺陷類型之一特定者；當最高者所對應的缺陷類型屬於特定者，判斷最高者是否大於一預設值；當最高者大於預設值，判定待判定缺陷區屬於特定者；以及，當最高者不大於預設值，判定待判定缺陷區屬於此些缺陷類型之另一者。 An embodiment of the present invention provides a defect judgment system. The defect judging system includes a camera and a defect judging device. The camera is used for capturing an image of an optical film, and the image has a defect area to be determined. The defect judger is used to: analyze the defect area to be judged, and generate a similarity score of the defect area to be judged relative to each of several defect types; judge this Whether the defect type corresponding to the highest similarity score belongs to one of these defect types; when the defect type corresponding to the highest belongs to a specific type, determine whether the highest value is greater than a preset value; Set the value to determine that the defect area to be determined belongs to a specific one; and, when the highest value is not greater than the preset value, determine that the defect area to be determined belongs to another of these defect types.

為了對本發明之上述及其他方面有更佳的瞭解，下文特舉實施例，並配合所附圖式詳細說明如下： In order to have a better understanding of the above-mentioned and other aspects of the present invention, the following specific examples are given and described in detail in conjunction with the accompanying drawings as follows:

10:光學膜 10: Optical film

100:缺陷判斷訓練系統 100: Defect Judgment Training System

110、210:攝像器 110, 210: Camera

120:圖像裁切器 120: Image cropper

130:缺陷判斷機器學習器 130: Defect Judgment Machine Learner

200:缺陷判斷系統 200: Defect Judgment System

220:缺陷判斷器 220: Defect Judger

A、B、C:邊界點 A, B, C: boundary points

BU:上邊界 B U : upper boundary

BL:下邊界 B L : lower boundary

D1:缺陷區 D1: Defective area

L:邊界基準線 L: Boundary reference line

M:缺陷判斷模型 M: Defect Judgment Model

P:圖像 P:image

P1:局部區域 P1: local area

Pf:光學膜影像區 P f : Optical film image area

Pb:背景區 P b : background area

S:灰階值差異處 S: where the grayscale value is different

S110~S153H、S210~S270:步驟 S110~S153H, S210~S270: Steps

YA、YB、YC:座標值 Y A , Y B , Y C : Coordinate values

YAV:平均座標值 Y AV : Average coordinate value

Yp:圖像高度 Y p : image height

△YAV:差值 △Y AV : Difference

△YL:下偏移量 △Y L : Lower offset

△Y:上偏移量 △Y: Upper offset

σL:標準差下限值 σ L : lower limit of standard deviation

σ:標準差值 σ: standard deviation value

σU:標準差上限值 σ U : standard deviation upper limit

第1圖繪示依照本發明一實施例之缺陷判斷訓練系統的功能方塊圖。 FIG. 1 is a functional block diagram of a defect judgment training system according to an embodiment of the present invention.

第2A~2F圖繪示依照本發明實施例之光學膜可能發生的數種實害缺陷類型的圖像。 Figures 2A to 2F illustrate images of several types of actual damage defects that may occur in optical films according to embodiments of the present invention.

第2G~2H圖繪示依照本發明實施例之光學膜可能發生的數種非實害缺陷類型的圖像。 Figures 2G to 2H illustrate images of several types of non-defective defects that may occur in optical films according to embodiments of the present invention.

第3A~3B圖繪示第1圖之缺陷判斷訓練系統之缺陷判斷方法的流程圖。 FIGS. 3A to 3B are flowcharts of the defect judgment method of the defect judgment training system of FIG. 1 .

第4圖繪示依照本發明一實施例之缺陷判斷系統的功能方塊圖。 FIG. 4 is a functional block diagram of a defect judgment system according to an embodiment of the present invention.

第5圖繪示第4圖之缺陷判斷系統的缺陷判斷方法的流程圖。 FIG. 5 is a flowchart showing a defect judgment method of the defect judgment system of FIG. 4 .

為了對本發明之上述及其他方面有更佳的瞭解，下文特 舉實施例，並配合所附圖式詳細說明如下。 For a better understanding of the above and other aspects of the present invention, the following Examples are given and described in detail with the accompanying drawings as follows.

請參照第1及2A~2H圖，第1圖繪示依照本發明一實施例之缺陷判斷訓練系統100的功能方塊圖，而第2A~2F圖繪示依照本發明實施例之光學膜可能發生的數種實害缺陷類型的圖像，第2G~2H圖繪示依照本發明實施例之光學膜可能發生的數種非實害缺陷類型的圖像。 Please refer to FIGS. 1 and 2A~2H. FIG. 1 shows a functional block diagram of a defect judgment training system 100 according to an embodiment of the present invention, and FIGS. 2A to 2F show possible occurrences of an optical film according to an embodiment of the present invention. 2G~2H are images of several types of non-physical defects that may occur in the optical film according to the embodiment of the present invention.

缺陷判斷訓練系統100包括攝像器110、圖像裁切器120及缺陷判斷機器學習器130。攝像器110用以擷取光學膜之圖像P，圖像P包含缺陷區D1。圖像裁切器120用以分析圖像P的缺陷區D1的特徵，以決定圖像P做為輸入缺陷判斷機器學習器130的區域，其中該區域包含缺陷區D1。缺陷判斷機器學習器130用以分析所定區域，以學習缺陷區D1的特徵。由於缺陷區D1的缺陷類型為已知，因此缺陷判斷機器學習器130透過分析缺陷區D1的特徵，可提高判斷缺陷區D1之缺陷類型。 The defect judgment training system 100 includes a camera 110 , an image cutter 120 and a defect judgment machine learner 130 . The camera 110 is used to capture the image P of the optical film, and the image P includes the defect area D1. The image cutter 120 is used for analyzing the characteristics of the defect area D1 of the image P, so as to determine the image P as an input area of the defect judgment machine learning device 130 , wherein the area includes the defect area D1 . The defect judging machine learning device 130 is used to analyze the determined area to learn the characteristics of the defect area D1. Since the defect type of the defect area D1 is known, the defect judgment machine learning device 130 can improve the judgment of the defect type of the defect area D1 by analyzing the characteristics of the defect area D1.

圖像裁切器120及缺陷判斷機器學習器130例如是採用半導體製程所形成的電路結構。在一實施例中，圖像裁切器120與缺陷判斷機器學習器130可整合成單一元件或整合於一處理器(processor)中。 The image cutter 120 and the defect judgment machine learner 130 are circuit structures formed by, for example, semiconductor manufacturing processes. In one embodiment, the image cutter 120 and the defect judgment machine learning device 130 may be integrated into a single component or integrated into a processor.

光學膜10可為一單層或多層膜，包含對光學之增益、配向、補償、轉向、直交、擴散、保護、防黏、耐刮、抗眩、反射抑制、高折射率等有所助益的膜，例如，可為偏光膜、離型膜、廣視角膜、增亮膜、反射膜、保護膜、具有控制視角補 償或雙折射(birefraction)等特性的配向液晶膜、硬塗膜、抗反射膜、防黏膜、擴散膜、防眩膜等各種表面經處理的膜或上述之組合，但不限於此。 The optical film 10 can be a single-layer or multi-layer film, including optical gain, alignment, compensation, steering, orthogonality, diffusion, protection, anti-sticking, scratch resistance, anti-glare, reflection suppression, high refractive index, etc. The film, for example, can be a polarizing film, a release film, a wide viewing angle film, a brightness enhancement film, a reflective film, a protective film, a Alignment liquid crystal film, hard coat film, anti-reflection film, anti-film, diffusion film, anti-glare film and other surface-treated films with compensation or birefringence and other characteristics, or a combination of the above, but not limited to this.

第3A~3B圖繪示第1圖之缺陷判斷訓練系統之缺陷判斷方法的流程圖。 FIGS. 3A to 3B are flowcharts of the defect judgment method of the defect judgment training system of FIG. 1 .

在步驟S110中，攝像器110擷取光學膜之側面之圖像P，圖像P包含缺陷區D1。缺陷區D1係經人工判斷的已知缺陷類型。以實害缺陷來說，第2A圖所示圖像P之缺陷區D1屬於凸膜型缺陷，第2B圖所示圖像P之缺陷區D1屬於毛屑型缺陷(例如是基於切割光學膜邊緣產生的毛屑)，第2C圖所示圖像P之缺陷區D1屬於雷射起始點型缺陷(例如是基於使用雷射切割光學膜需求所產生的切割起始點缺陷)，第2D圖所示圖像P之缺陷區D1屬於氣泡型缺陷(例如是基於多層光學膜貼合介面產生之氣泡)，第2E圖所示圖像P之缺陷區D1屬於裂痕型缺陷，而第2F圖所示圖像P之缺陷區D1屬於髒污型缺陷。以非實害缺陷來說，第2G圖所示圖像P之缺陷區D1屬於標記型缺陷(基於製程需求刻意於光學膜10上形成的標記)，而第2H圖所示圖像P之缺陷區D1屬於接合型缺陷(基於兩捲光學膜接合需求所產生的接合特徵)。 In step S110, the camera 110 captures an image P of the side surface of the optical film, and the image P includes the defect area D1. The defect area D1 is a known defect type judged manually. In terms of actual damage defects, the defect area D1 of the image P shown in Figure 2A is a convex film type defect, and the defect area D1 of the image P shown in Figure 2B is a dander type defect (for example, it is based on cutting the edge of the optical film. generated dander), the defect area D1 of the image P shown in Figure 2C is a laser starting point type defect (for example, a cutting starting point defect based on the requirement of using a laser to cut optical films), Figure 2D The defect area D1 of the image P shown is a bubble-type defect (for example, based on the bubbles generated at the bonding interface of the multilayer optical film), the defect area D1 of the image P shown in Fig. 2E is a crack-type defect, and the defect area shown in Fig. 2F. The defect area D1 of the image P is shown as a contamination type defect. In terms of non-physical defects, the defect area D1 of the image P shown in FIG. 2G is a mark-type defect (a mark deliberately formed on the optical film 10 based on process requirements), while the defect of the image P shown in FIG. 2H is a mark-type defect. Region D1 is a bond-type defect (bonded feature based on the need for bonding two rolls of optical film).

本發明實施例不限於前述缺陷類型，在另一實施例中，缺陷判斷訓練系統可處理的缺陷類型可以更多，然亦可更少。 The embodiment of the present invention is not limited to the aforementioned defect types. In another embodiment, the defect judgment training system can handle more defect types, but also fewer defect types.

在步驟S120中，以第2A圖為例來說，圖像裁切器120取得缺陷區D1的缺陷邊界之數個邊界點A、B及C沿一方向Y的數 個座標值YA、YB及YC，其中方向Y例如是垂直於圖像P之光學膜影像區Pf的邊界基準線L。缺陷區D1相對邊界基準線L突出(如，第2A圖)或內陷(如，第2C圖)。 In step S120 , taking FIG. 2A as an example, the image cutter 120 obtains several coordinate values Y A , Y of several boundary points A, B and C of the defect boundary of the defect area D1 along a direction Y B and Y C , wherein the direction Y is, for example, perpendicular to the boundary reference line L of the optical film image area P f of the image P. FIG. The defect area D1 protrudes from the boundary reference line L (eg, FIG. 2A ) or sinks in (eg, FIG. 2C ).

圖像裁切器120可對圖像P進行二值化。二值化後，圖像P之光學膜影像區Pf之各像素點具有第一灰階值(第2A圖以斜線剖面表示)，而圖像P之背景區Pb之各像素點具有第二灰階值(第2A圖以點剖面表示)，其中第一灰階值與第二灰階值相異。如此，圖像裁切器120可透過灰階值差異區分出光學膜影像區Pf與背景區Pb且判斷出缺陷區D1的缺陷邊界。圖像裁切器120沿方向Y以光學膜影像區Pf與背景區Pb之灰階值差異處S中相對變化「較緩和」之處的方向X延伸做為邊界基準線L。前述「較緩和」的定義符合要件：灰階值差異處S=|Pf之第一灰階值-Pb之第二灰階值|，其中S>Pb之第二灰階值，S<Pf之第一灰階值，且S<(Pf之第一灰階值+Pb之第二灰階值)/2。邊界基準線L例如是光學膜10的外表面的輪廓線影像的延伸，即光學膜影像區Pf與背景區Pb之分界線。當圖像P無凸膜型缺陷(如第2A圖)及毛屑型缺陷(如第2B圖)時，光學膜影像區Pf之外表面輪廓線例如是直線，如第2C圖之圖像P中為水平線。 The image cropper 120 may binarize the image P. After binarization, each pixel of the optical film image area P f of the image P has the first gray-scale value (shown by the oblique cross-section in Figure 2A), and each pixel of the background area P b of the image P has the first grayscale value. Two grayscale values (represented by a dotted section in FIG. 2A ), wherein the first grayscale value is different from the second grayscale value. In this way, the image cutter 120 can distinguish the optical film image area P f and the background area P b through the difference in grayscale values, and determine the defect boundary of the defect area D1 . The image cutter 120 extends along the direction Y with the direction X where the relative change "relatively gentle" in the gray-scale value difference between the optical film image area P f and the background area P b as the boundary reference line L. The aforementioned definition of “softer” complies with the requirements: S=|the first grayscale value of P f -the second grayscale value of Pb |, where S>the second grayscale value of Pb , and S <The first gray-scale value of P f , and S<(the first gray-scale value of P f + the second gray-scale value of P b )/2. The boundary reference line L is, for example, the extension of the contour line image of the outer surface of the optical film 10 , that is, the boundary line between the optical film image area P f and the background area P b . When the image P has no convex film-type defects (such as Fig. 2A) and dander-type defects (such as Fig. 2B), the outer surface contour line of the optical film image area P f is, for example, a straight line, such as the image in Fig. 2C P is a horizontal line.

本發明實施例之邊界點的數量係以三個為例說明，然此非用以限制本發明實施例。在另一實施例中，邊界點的數量可以少於或多於三個。 The number of boundary points in the embodiment of the present invention is described by taking three as an example, which is not intended to limit the embodiment of the present invention. In another embodiment, the number of boundary points may be less or more than three.

在步驟S130中，圖像裁切器120計算此些座標值YA、YB及YC，以取得此些座標值YA、YB與YC之平均座標值YAV。 In step S130 , the image cutter 120 calculates the coordinate values Y A , Y B and Y C to obtain the average coordinate value Y AV of the coordinate values Y A , Y B and Y C .

在步驟S140中，圖像裁切器120取得各座標值YA、YB及YC與平均座標值YAV之差值△YAV。此差值△YAV例如是絕對值。 In step S140, the image cutter 120 obtains the difference value ΔY AV between each coordinate value Y A , Y B and Y C and the average coordinate value Y AV . This difference ΔY AV is, for example, an absolute value.

在步驟S150中，圖像裁切器120依據此些差值△YAV之最大者，決定圖像P中做為缺陷判斷機械學習的學習區域(學習對象)。步驟S150可採用以下步驟完成。 In step S150 , the image cutter 120 determines the learning area (learning object) in the image P as the learning area (learning object) for the defect judgment machine learning according to the largest of these differences ΔY AV . Step S150 can be completed by the following steps.

在步驟S151中，圖像裁切器120判斷此些差值△YAV之最大者是否落於預設範圍；若否，則流程進入步驟S152；若是，則流程進入步驟S153A。 In step S151 , the image cutter 120 determines whether the largest of the difference values ΔY AV falls within a preset range; if not, the process proceeds to step S152 ; if so, the process proceeds to step S153A.

以第2A圖舉例來說，圖像P之座標值YB與平均座標值YAV之差值△YAV(即，YB-YAV=△YAV)係所有差值△YAV之最大者，其超出預設範圍，其中預設範圍如第2A圖之上邊界BU與下邊界BL之間的範圍。上邊界BU係平均座標值YAV與上偏移量△YU之和值(即，YAV+△YU)，而下邊界BL係該平均座標值YAV與下偏移量△YL之差值(即，YAV-△YU)。如第2A圖所示，由於最大之差值△YAV落於預設範圍內，此表示缺陷區D1的尺寸夠大，不足以影響機械學習的準確性，因此流程進入步驟S152。 Taking Fig. 2A as an example, the difference ΔY AV between the coordinate value Y B of the image P and the average coordinate value Y AV (ie, Y B -Y AV =ΔY AV ) is the maximum value of all the difference values ΔY AV Otherwise, it exceeds the preset range, wherein the preset range is the range between the upper boundary BU and the lower boundary BL in FIG. 2A . The upper boundary B U is the sum of the average coordinate value Y AV and the upper offset ΔY U (ie, Y AV +ΔY U ), and the lower boundary BL is the average coordinate value Y AV and the lower offset Δ The difference in Y L (ie, Y AV - ΔY U ). As shown in FIG. 2A , since the maximum difference ΔY AV falls within the preset range, it indicates that the size of the defect area D1 is large enough to not affect the accuracy of the machine learning, so the process proceeds to step S152 .

在步驟S152中，由於第2A圖之缺陷區D1的尺寸夠大而不足以影響機械學習的準確性，因此不需裁切第2A圖之圖像P，圖像裁切器120直接以整張圖像P輸入缺陷判斷機器學習器130。為了加速處理速度，缺陷判斷機器學習器130可先縮小輸入影像之解析度(尺寸)，然後再進行缺陷判斷的訓練。由於第2A圖之缺陷區D1的尺寸夠大，因此即使缺陷判斷機器學習器130在縮小整張圖像P的尺 寸後進行缺陷判斷訓練，仍不影響機械學習的準確性。 In step S152, since the size of the defect area D1 in the picture 2A is large enough to affect the accuracy of the machine learning, it is not necessary to cut the image P in the picture 2A, and the image cutter 120 directly uses the entire The image P is input to the defect judgment machine learner 130 . In order to speed up the processing speed, the defect judgment machine learning device 130 may first reduce the resolution (size) of the input image, and then perform the training of defect judgment. Since the size of the defect area D1 in FIG. 2A is large enough, even if the defect judgment machine learning device 130 is reducing the size of the entire image P Defect judgment training after inch, still does not affect the accuracy of machine learning.

此外，缺陷判斷機器學習器130可採用機器學習技術進行缺陷判斷的訓練。具體的機器學習技術例如：深度神經網路(Deep Neural Networks,DNN)、支援向量機(SVM)、決策樹(decision tree)、集(ensemble)、K鄰近法(K-NN)、線性回歸(linear regression)、貝氏機率、類神經網路(neural network)、羅吉斯回歸、感知器(perceptron)或關聯向量機(relevance vector machine,RVM)等演算法。一些實施例中例如是深度神經網路(Deep Neural Networks,DNN)或支援向量機(SVM)。特徵分類技術具有運算速度快且技術成本低的優點，而機器學習分類技術具有識別正確率極高且能持續(在品檢線上)修正機器學習模型以更提升識別正確率。 In addition, the defect judgment machine learner 130 may use machine learning technology to perform defect judgment training. Specific machine learning techniques such as: deep neural network (Deep Neural Networks, DNN), support vector machine (SVM), decision tree (decision tree), ensemble (ensemble), K-neighbor method (K-NN), linear regression ( Linear regression), Bayesian probability, neural network (neural network), Logis regression, perceptron (perceptron) or correlation vector machine (relevance vector machine, RVM) and other algorithms. In some embodiments, for example, Deep Neural Networks (DNN) or Support Vector Machines (SVM). The feature classification technology has the advantages of fast operation speed and low technical cost, while the machine learning classification technology has a very high recognition accuracy rate and can continuously (on the quality inspection line) correct the machine learning model to further improve the recognition accuracy rate.

在步驟S151中，當此些差值△YAV之最大者未落於預設範圍，表示缺陷區D1的尺寸可能太小，需要進一步地放大處理。以第2D圖舉例說明，最大之差值△YAV落於預設範圍(上邊界BU與下邊界BL之間的範圍)內，表示缺陷區D1的尺寸可能太小，因此流程進入步驟S153A，圖像裁切器120進一步判斷是否要裁切圖像P以及裁切局部區域的大小(若要裁切的話)。 In step S151 , when the largest of these differences ΔY AV does not fall within the predetermined range, it means that the size of the defect area D1 may be too small, and further enlargement processing is required. Taking Fig. 2D as an example, the maximum difference ΔY AV falls within the preset range (the range between the upper boundary BU and the lower boundary BL ), indicating that the size of the defect area D1 may be too small, so the flow enters the step S153A, the image trimmer 120 further determines whether to trim the image P and the size of the trimmed local area (if it is to be trimmed).

在步驟S153A中，圖像裁切器120取得此些座標值YA、YB及YC之標準差值σ。 In step S153A, the image cutter 120 obtains the standard deviation value σ of the coordinate values Y A , Y B and Y C .

在步驟S153B中，圖像裁切器120判斷標準差值σ是否等於或小於標準差下限值σL。若是，流程進入步驟S153C；若否，流程進入步驟S153E。前述標準差下限值σL例如是圖像P之圖像高度Yp的1%，然本發明實施例不以此為限。 In step S153B, the image cutter 120 determines whether the standard deviation value σ is equal to or smaller than the standard deviation lower limit value σ L . If yes, the flow goes to step S153C; if not, the flow goes to step S153E. The aforementioned lower limit value σ L of the standard deviation is, for example, 1% of the image height Y p of the image P, but the embodiment of the present invention is not limited to this.

在步驟S153C中，圖像裁切器120裁切圖像P之局部區域，其中局部區域的面積佔圖像P的面積的比例介於第一裁切比例範圍，其中第一裁切比例範圍介於30%~60%之間。 In step S153C, the image trimmer 120 trims a local area of the image P, wherein the ratio of the area of the local area to the area of the image P is within the first cropping scale range, wherein the first cropping scale range is between between 30% and 60%.

以如第2D圖舉例來說，由於最大之差值△YAV落於預設範圍內，因此圖像裁切器120裁切第2D圖之圖像P之局部區域P1做為機械學習對象(輸入缺陷判斷機器學習器130)。局部區域P1介於圖像P的整個面積的第一裁切比例範圍R1內，其中缺陷區D1整個位於局部區域P1內。缺陷區D1佔局部區域P1的比例大於缺陷區D1佔圖像P的比例，此凸顯了局部區域P1內的缺陷區D1的特徵。換言之，相較於以整張圖像P來看，局部區域P1中的缺陷區D1如同被放大效果，凸顯了缺陷區D1的特徵。在實施例中，局部區域P1可以平均座標值YAV為中心線往上、往下或同時往上及往下的區域，其中在同時往下及往下的例子中，往上的比例可大致等於往下的比例，然亦可相異。 Taking the 2D image as an example, since the maximum difference ΔY AV falls within the preset range, the image cutter 120 cuts the local area P1 of the image P in the 2D image as the machine learning object ( Input defect judgment machine learner 130). The local area P1 is within the first crop ratio range R1 of the entire area of the image P, wherein the defect area D1 is entirely located within the local area P1. The proportion of the defective area D1 in the local area P1 is greater than the proportion of the defective area D1 in the image P, which highlights the characteristics of the defective area D1 in the local area P1. In other words, compared with the whole image P, the defect area D1 in the partial area P1 seems to be enlarged, which highlights the characteristics of the defect area D1. In an embodiment, the local area P1 may be the area with the average coordinate value Y AV as the center line up, down, or both up and down, wherein in the case of simultaneous down and down, the up ratio may be approximately It is equal to the proportion below, but it can also be different.

在步驟S153D中，圖像裁切器120以所裁切出局部區域P1輸入缺陷判斷機器學習器130。當以裁切出之局部區域P1做為學習對象(輸入缺陷判斷機器學習器130)時，即使缺陷判斷機器學習器130在縮小局部區域P1的尺寸後進行缺陷判斷訓練，仍不影響增加機器學習的準確度。 In step S153D, the image cropper 120 inputs the cropped partial region P1 to the defect judgment machine learner 130. When the cropped local area P1 is used as the learning object (input to the defect judgment machine learner 130), even if the defect judgment machine learner 130 performs defect judgment training after reducing the size of the local area P1, it does not affect the increase in machine learning. accuracy.

在步驟S153E中，圖像裁切器120判斷標準差值σ是否等於或大於標準差上限值σU。若是，表示缺陷區D1的高低起伏過大，判斷為大尺寸缺陷，不需要裁切圖像P，因此流程進入步驟S153F。 在步驟S153F中，圖像裁切器120以整張圖像P輸入缺陷判斷機器學習器130。前述標準差上限值σU例如是2%的圖像高度Yp，然本發明實施例不以此為限。 In step S153E, the image cutter 120 determines whether the standard deviation value σ is equal to or greater than the standard deviation upper limit value σ U . If it is, it means that the height fluctuation of the defect area D1 is too large, it is judged as a large size defect, and the image P does not need to be cropped, so the flow goes to step S153F. In step S153F, the image cutter 120 inputs the entire image P to the defect judgment machine learner 130. The aforementioned upper limit value σ U of the standard deviation is, for example, 2% of the image height Y p , but the embodiment of the present invention is not limited to this.

若圖像裁切器120判斷標準差值σ是否小於標準差上限值σU，表示標準差值σ介於標準差下限值σL與標準差上限值σU之間。圖像裁切器120據以判斷為缺陷區D1的高低起伏甚小，屬於極小尺寸缺陷，因此流程進入步驟S153G。 If the image cropper 120 determines whether the standard deviation value σ is smaller than the standard deviation upper limit value σ U , it means that the standard deviation value σ is between the standard deviation lower limit value σ L and the standard deviation upper limit value σ U . The image cutter 120 determines that the defect area D1 has very small fluctuations in height and is a very small size defect, so the process proceeds to step S153G.

在步驟S153G中，圖像裁切器120裁切圖像P之局部區域。 In step S153G, the image trimmer 120 trims a local area of the image P.

以第2F圖舉例來說，由於缺陷區D1的標準差值σ介於標準差下限值σL與標準差上限值σU之間，圖像裁切器120裁切第2F圖之圖像P之局部區域P1做為機器學習對象(輸入缺陷判斷機器學習器130)。局部區域P1介於圖像P的整個面積的第二裁切比例範圍R2內，其中缺陷區D1整個位於局部區域P1內。第二裁切比例範圍R2小於前述第一裁切比例範圍R1，例如，第二裁切比例範圍介於6%~20%之間。在實施例中，第2F圖之局部區域P1可以平均座標值YAV為中心線往上、往下或同時往上及往下的區域，其中在同時往下及往下的例子中，往上的比例可大致等於往下的比例。 Taking FIG. 2F as an example, since the standard deviation value σ of the defect area D1 is between the lower standard deviation value σ L and the upper standard deviation value σ U , the image cutter 120 trims the image of FIG. 2F . The local area P1 like P is used as a machine learning object (input defect judgment machine learner 130). The partial area P1 is within the second crop ratio range R2 of the entire area of the image P, wherein the defect area D1 is entirely located within the partial area P1. The second cutting ratio range R2 is smaller than the aforementioned first cutting ratio range R1, for example, the second cutting ratio range is between 6% and 20%. In an embodiment, the local area P1 of the 2F diagram may be the area with the average coordinate value Y AV as the center line up, down, or both up and down, wherein in the case of simultaneous down and down, up can be roughly equal to the scale down.

在步驟S153H中，圖像裁切器120以所裁切出局部區域P1輸入缺陷判斷機器學習器130。當以裁切出之局部區域P1做為學習對象(輸入缺陷判斷機器學習器130)時，即使缺陷判斷機器學習器130在縮小局部區域P1的尺寸後，仍不影響增加機器學習的準確 度。 In step S153H, the image cropper 120 inputs the cropped partial region P1 to the defect judgment machine learner 130. When the cropped local area P1 is used as the learning object (input to the defect judgment machine learner 130), even if the defect judgment machine learner 130 reduces the size of the local area P1, it does not affect the accuracy of the machine learning. Spend.

綜上，在採用前述步驟分析第2A~2H圖之圖像P之缺陷區D1後，當缺陷區D1的尺寸夠大時(缺陷判斷機器學習器130在縮小影像尺寸後不會負面影響機器學習準確性)，圖像裁切器120以整張圖像P進行缺陷判斷訓練(如步驟S152及S153F)。當缺陷區D1的尺寸不夠大時(缺陷判斷機器學習器130在縮小影像尺寸後可能負面影響機器學習準確性)，圖像裁切器120裁切圖像P之局部區域，使局部區域內的缺陷區D1如同被放大效果，即使缺陷判斷機器學習器130在縮小影像尺寸後進行缺陷判斷訓練，也不致過度負面影響機器學習準確性。此外，視缺陷區D1的數個座標值的標準差而定，局部區域可介於圖像P的整個面積的第一裁切比例範圍R1(步驟S153C)或第二裁切比例範圍R2(步驟S153G)。 To sum up, after analyzing the defect area D1 of the image P in Figures 2A to 2H using the aforementioned steps, when the size of the defect area D1 is large enough (the defect judgment machine learner 130 will not negatively affect the machine learning after reducing the image size). accuracy), the image cutter 120 performs defect judgment training on the entire image P (eg, steps S152 and S153F). When the size of the defect area D1 is not large enough (the defect judgment machine learner 130 may negatively affect the accuracy of machine learning after reducing the image size), the image cutter 120 cuts a local area of the image P, so that the The defect area D1 seems to be enlarged. Even if the defect judgment machine learner 130 performs defect judgment training after reducing the size of the image, the accuracy of machine learning will not be adversely affected. In addition, depending on the standard deviation of several coordinate values of the defect area D1, the local area may be within the first cropping scale range R1 (step S153C) or the second cropping scale range R2 (step S153C) of the entire area of the image P S153G).

請參照第4及5圖，第4圖繪示依照本發明一實施例之缺陷判斷系統200的功能方塊圖，而第5圖繪示第4圖之缺陷判斷系統200的缺陷判斷方法的流程圖。 Please refer to FIGS. 4 and 5. FIG. 4 shows a functional block diagram of a defect judgment system 200 according to an embodiment of the present invention, and FIG. 5 shows a flowchart of a defect judgment method of the defect judgment system 200 of FIG. 4. .

如第4圖所示，缺陷判斷系統200包括攝像器210及缺陷判斷器220。缺陷判斷器220例如是採用半導體製程所形成的電路結構。在一實施例中，缺陷判斷器220可整合於一處理器(processor)中。 As shown in FIG. 4 , the defect judgment system 200 includes a camera 210 and a defect judgment unit 220 . The defect judger 220 is, for example, a circuit structure formed by a semiconductor process. In one embodiment, the defect determiner 220 may be integrated into a processor.

攝像器210用以在製程現場，如自動光學檢查(Automated Optical Inspection,AOI)系統，擷取光學膜10之圖像。缺陷判斷機器學習器130在經過上述流程以數張圖像P進行缺陷判斷 訓練完成後，缺陷判斷機器學習器130產生一缺陷判斷模型M(繪示於第1圖)。缺陷判斷器220可依據缺陷判斷模型M，判斷攝像器210所擷取之圖像的缺陷類型。缺陷判斷器220的判斷準則如下表一所示，以下係以第5圖之流程圖進一步舉例說明。 The camera 210 is used for capturing an image of the optical film 10 at the process site, such as an automated optical inspection (Automated Optical Inspection, AOI) system. The defect judging machine learner 130 uses several images P to judge the defects through the above process. After the training is completed, the defect judgment machine learner 130 generates a defect judgment model M (shown in FIG. 1 ). The defect judging unit 220 can judge the defect type of the image captured by the camera 210 according to the defect judging model M. The judgment criteria of the defect judger 220 are shown in Table 1 below, and the following is a further example to illustrate the flow chart of FIG. 5 .

Figure 108121332-A0305-02-0014-1
Figure 108121332-A0305-02-0014-1

在步驟S210中，攝像器210於製程現場擷取光學膜10之圖像，圖像具有待判定缺陷區。 In step S210, the camera 210 captures an image of the optical film 10 at the process site, and the image has a defect area to be determined.

在步驟S220中，缺陷判斷器220分析待判定缺陷區，並產生待判定缺陷區相對於數個缺陷類型之各者的相似度分數。本實施例以待判定缺陷區相對於標記型缺陷的相似度分數為所有相似度分 數之最高者舉例來說。 In step S220, the defect determiner 220 analyzes the defect area to be determined, and generates a similarity score of the defect area to be determined with respect to each of several defect types. In this embodiment, the similarity score of the defect area to be judged relative to the mark-type defect is used as all the similarity scores. For example, the highest number.

在步驟S230中，缺陷判斷器220判斷此些相似度分數中最高者所對應的缺陷類型是否屬於此些缺陷類型之一特定者。若是，流程進入步驟S240；若否，則缺陷判斷器220直接判定待判定缺陷區屬於數個相似度分數之最高者所對應的缺陷類型。 In step S230, the defect determiner 220 determines whether the defect type corresponding to the highest similarity score belongs to a specific one of the defect types. If yes, the flow goes to step S240; if no, the defect determiner 220 directly determines that the defect area to be determined belongs to the defect type corresponding to the highest similarity score.

以特定者為標記型缺陷舉例來說，待判定缺陷區的最高相似度分數所對應的缺陷類型同樣屬於標記型缺陷，流程進入步驟S240。 Taking the specific one as a marker-type defect for example, the defect type corresponding to the highest similarity score of the defect area to be determined also belongs to the marker-type defect, and the process proceeds to step S240.

在另一實施例中，以特定者為標記型缺陷以及最高相似度分數所對應的缺陷類型為凸膜型缺陷舉例來說，由於凸膜型缺陷並非屬於該特定者(標記型缺陷)，因此流程進入步驟S270。在步驟S270中，依據上表一，缺陷判斷器220直接判定待判定缺陷區屬於最高相似度分數所對應的缺陷類型，以本例來說是凸膜型缺陷。 In another embodiment, a specific one is a marking type defect and the defect type corresponding to the highest similarity score is a convex film type defect. For example, since the convex film type defect does not belong to the specific one (marking type defect), therefore The flow proceeds to step S270. In step S270, according to the above Table 1, the defect judger 220 directly judges that the defect area to be judged belongs to the defect type corresponding to the highest similarity score, which is a convex film type defect in this example.

在步驟S240中，缺陷判斷器220判斷最高之相似度分數是否大於一預設值。以特定者為標記型缺陷且最高相似度分數所對應的缺陷類型也為標記型缺陷舉例來說，當最高相似度分數(如0.8)大於預設值(表一以0.4為例)，則流程進入步驟S250，缺陷判斷器220判定待判定缺陷區屬於該特定者，即屬於標記型缺陷。 In step S240, the defect determiner 220 determines whether the highest similarity score is greater than a predetermined value. Take a specific person as a marker defect and the defect type corresponding to the highest similarity score is also a marker defect. For example, when the highest similarity score (such as 0.8) is greater than the preset value (Table 1 takes 0.4 as an example), the process Going to step S250, the defect judger 220 judges that the defect area to be judged belongs to the specific one, that is, it belongs to the mark type defect.

當最高相似度分數(如0.8)不大於預設值(表一以0.4為例)，則流程進入步驟S260，缺陷判斷器220判定待判定缺陷區屬於此些缺陷類型中之另一者，此另一者與該特定者的缺陷特徵相似。舉例來說，由於標記型缺陷與髒污型缺陷的特徵接近，因此在判斷標記 型缺陷上，除了相對於標記型缺陷之相似度分數要最高外，其相似度分數要高於0.4，缺陷判斷機器學習器130才會判定待判缺陷區屬於非實害之標記型缺陷；若相對於標記型缺陷之相似度分數最高，但分數小於0.4，則缺陷判斷機器學習器130判定待判缺陷區屬於髒污型缺陷。相似地，如表一所示，接合型缺陷與氣泡型缺陷的特徵接近，因此也可以相似方法判定待判缺陷區屬於接合型缺陷或氣泡型缺陷。 When the highest similarity score (such as 0.8) is not greater than the preset value (0.4 is used as an example in Table 1), the process proceeds to step S260, and the defect determiner 220 determines that the defect area to be determined belongs to the other of these defect types. The other is similar to the defect characteristics of that particular one. For example, since the characteristics of mark-type defects and contamination-type defects are close, In terms of type defects, in addition to the highest similarity score relative to marked type defects, the similarity score must be higher than 0.4, and the defect judgment machine learner 130 will determine that the defect area to be judged belongs to non-actually harmful marked type defects; If the similarity score relative to the marked defect is the highest, but the score is less than 0.4, the defect judgment machine learner 130 judges that the defect area to be judged belongs to the dirty defect. Similarly, as shown in Table 1, the characteristics of bonding-type defects and bubble-type defects are similar, so it can also be determined that the defect area to be judged belongs to bonding-type defects or bubble-type defects by a similar method.

綜上可知，要判定圖像之待判定缺陷區屬於非實害缺陷類型(如標記型缺陷及接合型缺陷)的條件較為嚴格。例如，除了相對於該非實害缺陷類型之相似度分數要最高外，相似度分數必須高於預設值(具體數值不受上表一所限制)，可避免實害缺陷類型誤判成非實害缺陷類型。 To sum up, it can be seen that the conditions for judging that the to-be-determined defect area of the image belongs to the non-actual defect type (such as mark-type defects and joint-type defects) are relatively strict. For example, in addition to the highest similarity score relative to the non-actual defect type, the similarity score must be higher than the preset value (the specific value is not limited by the above table 1), which can prevent the actual defect type from being misjudged as non-actually harmful. Defect type.

綜上所述，雖然本發明已以實施例揭露如上，然其並非用以限定本發明。本發明所屬技術領域中具有通常知識者，在不脫離本發明之精神和範圍內，當可作各種之更動與潤飾。因此，本發明之保護範圍當視後附之申請專利範圍所界定者為準。 To sum up, although the present invention has been disclosed by the above embodiments, it is not intended to limit the present invention. Those skilled in the art to which the present invention pertains can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope of the appended patent application.

S110~S153D:步驟 S110~S153D: Steps

Claims (27)

一種缺陷判斷訓練方法，包括：擷取一光學膜之一側面之一圖像，該圖像包含一缺陷區及一邊界基準線，該缺陷區相對於該邊界基準線突出或內陷；取得該缺陷區的一缺陷邊界之複數個邊界點沿一方向的複數個座標值；取得該些座標值之一平均座標值；取得各該座標值與該平均座標值之一差值；依據該些差值之最大者，決定該圖像中做為缺陷判斷機器學習的區域。 A defect judgment training method, comprising: capturing an image of a side surface of an optical film, the image including a defect area and a boundary reference line, the defect area protruding or indented relative to the boundary reference line; obtaining the a plurality of coordinate values along a direction of a plurality of boundary points of a defect boundary of a defective area; obtain an average coordinate value of the coordinate values; obtain a difference between each of the coordinate values and the average coordinate value; according to the differences The largest value determines the area in the image that is used for defect judgment machine learning. 如申請專利範圍第1項所述的缺陷判斷訓練方法，其中在依據該最大者決定該圖像中做為缺陷判斷機器學習的區域之步驟包括：判斷該些差值之該最大者是否小於一預設範圍；當該最大者小於該預設範圍，裁切該圖像之一局部區域，其中該缺陷區位於該局部區域內；以及輸入該局部區域於一缺陷判斷機器學習器。 The defect judgment training method as described in claim 1, wherein the step of determining the region in the image to be learned by the defect judgment machine according to the maximum value comprises: judging whether the maximum value of the differences is less than a a preset range; when the maximum is smaller than the preset range, crop a local area of the image, wherein the defective area is located in the local area; and input the local area into a defect judgment machine learner. 如申請專利範圍第1項所述的缺陷判斷訓練方法，在依據該最大者決定該圖像中做為缺陷判斷機器學習的區域之步驟包括：判斷該些差值之該最大者是否小於一預設範圍；以及當該最大者不小於該預設範圍，輸入整個該圖像於一缺陷判斷機器學習器。 According to the defect judgment training method described in item 1 of the scope of application, the step of determining the region in the image as the defect judgment machine learning area according to the maximum value includes: judging whether the maximum value of the difference values is less than a predetermined value setting a range; and when the maximum is not less than the preset range, inputting the entire image into a defect judgment machine learner. 如申請專利範圍第2或3項所述的缺陷判斷訓練方法，其中在判斷該些差值之該最大者是否小於該預設範圍步驟中，該預設範圍介於一上邊界與一下邊界之間，該上邊界係該平均座標值與一上偏移量之和值，而該下邊界係該平均座標值與一下偏移量之差值。 The defect judgment training method described in claim 2 or 3, wherein in the step of judging whether the largest of the differences is smaller than the predetermined range, the predetermined range is between an upper boundary and a lower boundary In between, the upper boundary is the sum of the average coordinate value and an upper offset, and the lower boundary is the difference between the average coordinate and a lower offset. 如申請專利範圍第2項所述的缺陷判斷訓練方法，其中在裁切該圖像之該局部區域之步驟包括：取得該些座標值之一標準差值；判斷該標準差值是否等於或小於一標準差下限值；以及當該標準差值等於或小於該標準差下限值，裁切該圖像之該局部區域，其中該局部區域的面積佔該圖像的面積的比例介於一第一裁切比例範圍之間，且該缺陷區位於局部區域。 The defect judgment training method as described in claim 2, wherein the step of cropping the local area of the image includes: obtaining a standard deviation value of the coordinate values; judging whether the standard deviation value is equal to or less than a lower standard deviation value; and when the standard deviation value is equal to or less than the lower standard deviation value, crop the local area of the image, wherein the ratio of the area of the local area to the area of the image is between a and the defect area is located in the local area. 如申請專利範圍第5項所述的缺陷判斷訓練方法，其中該第一裁切比例範圍介於30%~60%之間。 The defect judgment training method described in item 5 of the scope of the patent application, wherein the range of the first cropping ratio is between 30% and 60%. 如申請專利範圍第2項所述的缺陷判斷訓練方法，其中在裁切該圖像之該局部區域之步驟包括：取得該些座標值之一標準差值；判斷該標準差值是否介於一標準差下限值與一標準差上限值之間；以及當該標準差值介於該標準差下限值與該標準差上限值之間，擷取該圖像之該局部區域，其中該局部區域的面積佔該圖像的面積的比例介於一第二裁切比例範圍之間，且該缺陷區位於局部區域。 The defect judgment training method as described in claim 2, wherein the step of cropping the local area of the image comprises: obtaining a standard deviation value of the coordinate values; judging whether the standard deviation value is between a between a lower standard deviation value and an upper standard deviation value; and when the standard deviation value is between the lower standard deviation value and the upper standard deviation value, capturing the local area of the image, wherein The ratio of the area of the local area to the area of the image is between a second cropping ratio range, and the defect area is located in the local area. 如申請專利範圍第7項所述的缺陷判斷訓練方法，其中該第二裁切比例範圍介於6%~20%之間。 The defect judgment training method as described in item 7 of the scope of the application, wherein the range of the second cropping ratio is between 6% and 20%. 如申請專利範圍第7項所述的缺陷判斷訓練方法，其中該標準差下限值為該圖像的一圖像高度之1%；且該標準差上限值為該圖像的該圖像高度之2%。 The defect judgment training method described in claim 7, wherein the lower standard deviation value is 1% of the height of an image of the image; and the upper standard deviation value is the image of the image 2% of height. 如申請專利範圍第1項所述的缺陷判斷訓練方法，更包括：二值化該圖像，使該圖像之光學膜影像區之各像素點具有一第一灰階值，而該圖像之一背景區之各像素點具有一第二灰階值；取該第一灰階值與該第二灰階值之差的絕對值做為一灰階值差異處；以及根據該灰階值差異處中相對變化之較緩和之處的延伸做為該邊界基準線。 The defect judgment training method described in item 1 of the claimed scope further comprises: binarizing the image so that each pixel in the optical film image area of the image has a first grayscale value, and the image Each pixel of a background area has a second grayscale value; taking the absolute value of the difference between the first grayscale value and the second grayscale value as a grayscale value difference; and according to the grayscale value The extension of the relative change in the difference is the extension of the boundary reference line. 如申請專利範圍第10項所述的缺陷判斷訓練方法，其中該較緩和之處符合以下要件：該灰階值差異處大於該第一灰階值；該灰階值差異處小於該第二灰階值；以及該灰階值差異處小於該第一灰階值與該第二灰階值的平均值。 The defect judgment training method as described in item 10 of the scope of the patent application, wherein the moderate point satisfies the following requirements: the difference between the grayscale values is greater than the first grayscale value; the difference between the grayscale values is smaller than the second grayscale value and the difference between the gray level values is smaller than the average value of the first gray level value and the second gray level value. 如申請專利範圍第2項所述的缺陷判斷訓練方法，其中在裁切該圖像之該局部區域之步驟包括：取得該些方向座標值之一標準差值；判斷該標準差值是否大於或等於一標準差上限值；以及 當該標準差值介於一標準差下限值與該標準差上限值之間，輸入整個該圖像於該缺陷判斷機器學習器。 The defect judgment training method as described in claim 2, wherein the step of cropping the local area of the image comprises: obtaining a standard deviation value of the direction coordinate values; judging whether the standard deviation value is greater than or is equal to an upper standard deviation value; and When the standard deviation value is between a lower standard deviation value and an upper standard deviation value, the entire image is input into the defect judgment machine learner. 一種缺陷判斷訓練系統，包括：一攝像器，用以擷取一光學膜之一側面之一圖像，該圖像包含一缺陷區及一邊界基準線，該缺陷區相對於該邊界基準線突出或內陷；以及一缺陷判斷機器學習器，用以：取得該缺陷區的一缺陷邊界之複數個邊界點沿一方向的複數個座標值；取得該些座標值之一平均座標值；取得各該座標值與該平均座標值之一差值；及依據該些差值之最大者，決定該圖像中做為缺陷判斷機器學習的區域。 A defect judgment training system, comprising: a camera for capturing an image of a side surface of an optical film, the image including a defect area and a boundary reference line, the defect area protruding relative to the boundary reference line or indentation; and a defect judging machine learning device for: obtaining a plurality of coordinate values along a direction of a plurality of boundary points of a defect boundary of the defect area; obtaining an average coordinate value of the coordinate values; obtaining each A difference between the coordinate value and the average coordinate value; and according to the largest of the difference values, determine an area in the image to be learned by the defect judgment machine. 如申請專利範圍第13項所述的缺陷判斷訓練系統，其中在依據該最大者決定該圖像中做為缺陷判斷機器學習的區域之步驟中，該缺陷判斷機器學習器更用以：判斷該些差值之該最大者是否小於一預設範圍；當該最大者小於該預設範圍，裁切該圖像之一局部區域，其中該缺陷區位於該局部區域內；以及輸入該局部區域於一缺陷判斷機器學習器。 The defect judgment training system as described in claim 13, wherein in the step of determining the area in the image to be learned by the defect judgment machine according to the largest one, the defect judgment machine learner is further used for: judging the whether the largest of these differences is less than a predetermined range; when the largest is less than the predetermined range, crop a partial area of the image, wherein the defective area is located in the partial area; and input the partial area in A defect judgment machine learner. 如申請專利範圍第14項所述的缺陷判斷訓練系統，其中在依據該最大者決定該圖像中做為缺陷判斷機器學習的區域之步驟，該缺陷判斷機器學習器更用以：判斷該些差值之該最大者是否小於一預設範圍；以及當該最大者不小於該預設範圍，輸入整個該圖像於該缺陷判斷機器學習器。 The defect judgment training system as described in item 14 of the scope of the application, wherein in the step of determining the area in the image to be learned by the defect judgment machine according to the largest one, the defect judgment machine learner is further used for: judging these whether the largest of the differences is less than a predetermined range; and when the largest is not less than the predetermined range, input the entire image into the defect judgment machine learner. 如申請專利範圍第14或15項所述的缺陷判斷訓練系統，其中該預設範圍介於一上邊界與一下邊界之間，該上邊界係該平均座標值與一上偏移量之和值，而該下邊界係該平均座標值與一下偏移量之差值。 The defect judgment training system according to claim 14 or 15, wherein the predetermined range is between an upper boundary and a lower boundary, and the upper boundary is the sum of the average coordinate value and an upper offset , and the lower boundary is the difference between the average coordinate value and the lower offset. 如申請專利範圍第14項所述的缺陷判斷訓練系統，其中在裁切該圖像之該局部區域之步驟中，該缺陷判斷機器學習器更用以：取得該些座標值之一標準差值；判斷該標準差值是否等於或小於一標準差下限值；以及當該標準差值等於或小於該標準差下限值，裁切該圖像之該局部區域，其中該局部區域的面積佔該圖像的面積的比例介於一第一裁切比例範圍之間，且該缺陷區位於局部區域。 The defect judgment training system as described in claim 14, wherein in the step of cropping the local area of the image, the defect judgment machine learner is further configured to: obtain a standard deviation value of the coordinate values ; determine whether the standard deviation value is equal to or less than a standard deviation lower limit value; and when the standard deviation value is equal to or less than the standard deviation lower limit value, crop the local area of the image, wherein the area of the local area accounts for The ratio of the area of the image is between a first cropping ratio range, and the defect area is located in a local area. 如申請專利範圍第17項所述的缺陷判斷訓練系統，其中其中該第一裁切比例範圍介於30%~60%之間。 The defect judgment training system according to item 17 of the scope of application, wherein the range of the first cutting ratio is between 30% and 60%. 如申請專利範圍第14項所述的缺陷判斷訓練系統，其中在裁切該圖像之該局部區域之步驟中，該缺陷判斷機器學習器更用以：取得該些座標值之一標準差值；判斷該標準差值是否介於一標準差下限值與一標準差上限值之間；以及當該標準差值介於該標準差下限值與該標準差上限值之間，擷取該圖像之該局部區域，其中該局部區域的面積佔該圖像的面積的比例介於一第二裁切比例範圍之間，且該缺陷區位於局部區域。 The defect judgment training system as described in claim 14, wherein in the step of cropping the local area of the image, the defect judgment machine learner is further configured to: obtain a standard deviation value of the coordinate values ; determine whether the standard deviation value is between a lower standard deviation value and an upper standard deviation value; and when the standard deviation value is between the lower standard deviation value and the upper standard deviation value, retrieve The partial area of the image is taken, wherein the ratio of the area of the partial area to the area of the image is between a second cropping scale range, and the defective area is located in the partial area. 如申請專利範圍第19項所述的缺陷判斷訓練系統，其中該第二裁切比例範圍介於6%~20%之間。 The defect judgment training system according to item 19 of the scope of application, wherein the second cropping ratio ranges from 6% to 20%. 如申請專利範圍第14項所述的缺陷判斷訓練系統，其中在裁切該圖像之該局部區域之步驟中，該缺陷判斷機器學習器更用以：取得該些方向座標值之一標準差值；判斷該標準差值是否大於或等於一標準差上限值；以及當該標準差值介於一標準差下限值與該標準差上限值之間，輸入整個該圖像於該缺陷判斷機器學習器。 The defect judgment training system as described in claim 14, wherein in the step of cropping the local area of the image, the defect judgment machine learner is further used for: obtaining a standard deviation of the direction coordinate values value; determine whether the standard deviation value is greater than or equal to an upper standard deviation value; and when the standard deviation value is between a lower standard deviation value and the upper standard deviation value, input the entire image into the defect Judge the machine learner. 一種缺陷判斷方法，包括：擷取一光學膜之一圖像，該圖像具有一待判定缺陷區；以及依據一缺陷判斷模型，執行以下步驟： 分析該待判定缺陷區，並產生該待判定缺陷區相對於複數個缺陷類型之各者的一相似度分數；判斷該些相似度分數中最高者所對應的該缺陷類型是否屬於該些缺陷類型之一特定者；當該最高者所對應的該缺陷類型屬於該特定者，判斷該最高者是否大於一預設值；當該最高者大於該預設值，判定該待判定缺陷區屬於該特定者；以及當該最高者不大於該預設值，判定該待判定缺陷區屬於該些缺陷類型之另一者；其中，該缺陷判斷模型係採用如申請專利範圍第1項所述之缺陷判斷訓練方法所產生。 A defect judgment method, comprising: capturing an image of an optical film, the image having a defect area to be judged; and performing the following steps according to a defect judgment model: Analyze the defect area to be determined, and generate a similarity score of the defect area to be determined relative to each of a plurality of defect types; determine whether the defect type corresponding to the highest similarity score belongs to the defect types a specific one; when the defect type corresponding to the highest one belongs to the specific one, it is determined whether the highest one is greater than a preset value; when the highest one is greater than the preset value, it is determined that the defect area to be determined belongs to the specific one and when the highest value is not greater than the preset value, it is determined that the defect area to be determined belongs to the other of the defect types; wherein, the defect determination model adopts the defect determination as described in item 1 of the scope of the patent application generated by the training method. 如申請專利範圍第22項所述的缺陷判斷方法，其中該特定者與該另一者的缺陷特徵相似。 The defect judging method as described in item 22 of the patent application scope, wherein the specific one and the other have similar defect characteristics. 如申請專利範圍第22項所述的缺陷判斷方法，更包括：當該最高者所對應的該缺陷類型不屬於該特定者，直接判定該待判定缺陷區屬於該最高者所對應之該缺陷類型。 The defect judging method described in item 22 of the scope of the application further comprises: when the defect type corresponding to the highest one does not belong to the specific one, directly determining that the defect area to be determined belongs to the defect type corresponding to the highest one . 一種缺陷判斷系統，包括：一攝像器，用以擷取一光學膜之圖像，該圖像具有一待判定缺陷區；以及一缺陷判斷器，用以依據一缺陷判斷模型，執行： 分析該待判定缺陷區，並產生該待判定缺陷區相對於複數個缺陷類型之各者的一相似度分數；判斷該些相似度分數中最高者所對應的該缺陷類型是否屬於該些缺陷類型之一特定者；當該最高者所對應的該缺陷類型屬於該特定者，判斷該最高者是否大於一預設值；當該最高者大於該預設值，判定該待判定缺陷區屬於該特定者；及當該最高者不大於該預設值，判定該待判定缺陷區屬於該些缺陷類型之另一者；其中，該缺陷判斷模型係採用如申請專利範圍第1項所述之缺陷判斷訓練方法所產生。 A defect judgment system, comprising: a camera for capturing an image of an optical film, the image having a defect area to be judged; and a defect judger for executing according to a defect judgment model: Analyze the defect area to be determined, and generate a similarity score of the defect area to be determined relative to each of a plurality of defect types; determine whether the defect type corresponding to the highest similarity score belongs to the defect types a specific one; when the defect type corresponding to the highest one belongs to the specific one, it is determined whether the highest one is greater than a preset value; when the highest one is greater than the preset value, it is determined that the defect area to be determined belongs to the specific one and when the highest value is not greater than the default value, it is determined that the defect area to be determined belongs to the other of the defect types; wherein, the defect determination model adopts the defect determination as described in item 1 of the scope of the patent application generated by the training method. 如申請專利範圍第25項所述的缺陷判斷系統，其中該特定者與該另一者的缺陷特徵相似。 The defect judging system as described in claim 25, wherein the specific one and the other one have similar defect characteristics. 如申請專利範圍第25項所述的缺陷判斷系統，其中該缺陷判斷器更用以：當該最高者所對應的該缺陷類型不屬於該特定者，直接判定該待判定缺陷區屬於該最高者所對應之該缺陷類型。 The defect judging system as described in item 25 of the scope of application, wherein the defect judging device is further used for: when the defect type corresponding to the highest one does not belong to the specific one, directly determine that the to-be-determined defective area belongs to the highest one The corresponding defect type.
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