CN104574408A - Industry transparent film package detecting method and device based on shape feature extraction - Google Patents

Abstract

Translated fromChinese

本发明涉及一种基于形状特征提取的工业透明薄膜包装检测方法和装置。方法包括：对获取的包装产品图像进行预处理；对预处理后的图像进行超像素的分割；利用七个Hu不变矩定义形状特征实现形状特征提取，并引入新的参数；对多变量参数矩阵进行处理，得到主成分；使用最小二乘支持向量机根据形状特征进行训练，再对超像素块进行分类。装置包括传送带模块、图片拍摄模块、算法模块和结果分析模块。本发明能够减少检测时间，并且同时保持较高的分类性能。

The invention relates to an industrial transparent film packaging detection method and device based on shape feature extraction. The method includes: preprocessing the obtained packaging product image; performing superpixel segmentation on the preprocessed image; using seven Hu invariant moments to define shape features to realize shape feature extraction, and introducing new parameters; The matrix is processed to obtain the principal components; the least squares support vector machine is used to train according to the shape features, and then the superpixel blocks are classified. The device includes a conveyor belt module, a picture shooting module, an algorithm module and a result analysis module. The invention can reduce detection time while maintaining high classification performance.

Description

Translated fromChinese

基于形状特征提取的工业透明薄膜包装检测方法及装置Detection method and device for industrial transparent film packaging based on shape feature extraction

技术领域technical field

本发明涉及透明薄膜包装检测技术领域，特别是涉及一种基于形状特征提取的工业透明薄膜包装检测方法及装置。The invention relates to the technical field of transparent film packaging detection, in particular to an industrial transparent film packaging detection method and device based on shape feature extraction.

背景技术Background technique

产品外包装薄膜常存在封装气泡、褶皱、松弛等缺陷，严重影响产品的形象。由于包装薄膜透光率高，现多采用人工肉眼检测，检测效果不理想，操作者的经验和技能影响较大，在自动化生产线上无法实现对产品外包装薄膜的热封缺陷进行自动化检测。The outer packaging film of the product often has defects such as packaging bubbles, wrinkles, and slack, which seriously affect the image of the product. Due to the high light transmittance of the packaging film, manual visual inspection is mostly used at present, and the detection effect is not ideal. The experience and skills of the operator are greatly affected. It is impossible to automatically detect the heat-sealing defects of the outer packaging film of the product on the automatic production line.

发明内容Contents of the invention

本发明所要解决的技术问题是提供一种基于形状特征提取的工业透明薄膜包装检测方法及装置，能够减少检测时间，并且同时保持较高的分类性能。The technical problem to be solved by the present invention is to provide an industrial transparent film packaging detection method and device based on shape feature extraction, which can reduce the detection time while maintaining high classification performance.

本发明解决其技术问题所采用的技术方案是：提供一种基于形状特征提取的工业透明薄膜包装检测方法，包括以下步骤：The technical solution adopted by the present invention to solve the technical problem is to provide a detection method for industrial transparent film packaging based on shape feature extraction, comprising the following steps:

(1)对获取的包装产品图像进行预处理；(1) Preprocessing the obtained packaging product image;

(2)对预处理后的图像进行超像素的分割；(2) Carry out superpixel segmentation to the preprocessed image;

(3)利用七个Hu不变矩定义形状特征实现形状特征提取，并引入新的参数；(3) Use seven Hu invariant moments to define shape features to realize shape feature extraction, and introduce new parameters;

(4)对多变量参数矩阵进行处理，得到主成分；(4) Process the multivariate parameter matrix to obtain the principal components;

(5)使用最小二乘支持向量机根据形状特征进行训练，再对超像素块进行分类。(5) Use the least squares support vector machine to train according to the shape features, and then classify the superpixel blocks.

所述包装产品图像运用碗状光源和广角镜头进行获取。The image of the packaged product is captured using a bowl-shaped light source and a wide-angle lens.

所述步骤(1)中采用先利用Canny边缘检测，再利用膨胀操作对获取的包装产品图像进行预处理。In the step (1), the Canny edge detection is used first, and then the expansion operation is used to preprocess the obtained packaging product image.

所述步骤(2)采用简单的线性迭代聚类的方式进行超像素分割。The step (2) adopts a simple linear iterative clustering method to perform superpixel segmentation.

所述步骤(3)中七个Hu不变矩分别为：In the described step (3), seven Hu invariant moments are respectively:

φ₁＝η₂₀+η₀₂；φ₁ =η₂₀ +η₀₂ ;

${\mathrm{<span><span>\&phi;</span>\&phi;</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>=</span>=</span>{<span><span>(</span>(</span>{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>20</span>20</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>02</span>02</span>}}<span><span>)</span>)</span>}^{\mathrm{<span><span>2</span>2</span>}}<span><span>+</span>+</span>\mathrm{<span><span>4</span>4</span>}{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>11</span>11</span>}}^{\mathrm{<span><span>2</span>2</span>}}<span><span>;</span>;</span>$

φ₃＝(η₃₀-3η₁₂)²+(3η₂₁-η₀₃)²；φ₃ =(η₃₀ -3η₁₂ )² +(3η₂₁ -η₀₃ )² ;

φ₄＝(η₃₀+η₁₂)²+(η₂₁+η₀₃)²；φ₄ =(η₃₀ +η₁₂ )² +(η₂₁ +η₀₃ )² ;

φ₅＝(η₃₀-3η₁₂)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]φ₅ =(η₃₀ -3η₁₂ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]

+(3η₂₁-η₀₃)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]；+(3η₂₁ -η₀₃ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ];

φ₆＝(η₂₀-η₀₂)[(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]+4η₁₁(η₃₀+η₁₂)(η₂₁+η₀₃)；φ₆ =(η₂₀ -η₀₂ )[(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ]+4η₁₁ (η₃₀ +η₁₂ )(η₂₁ +η₀₃ );

φ₇＝(3η₂₁-η₀₃)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]+φ₇ ＝(3η₂₁ -η₀₃ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]+

(η₀₃-η₁₂)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]；(η₀₃ -η₁₂ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ];

η_ij表示图像的(i+j)阶规格化中心矩。图像函数的f(x+y)的(i+j)的中心矩定义为其中Ω为x，y的取值区间。对于N*M的数字图像，利用求和代替积分，则(i+j)阶中心可表示为则(i+j)阶格式化中心矩可以表示为${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{{\mathrm{\&mu;}}_{\mathrm{ij}}}{{{\mathrm{\&mu;}}^{\mathrm{\&gamma;}}}_{00}},$其中$\mathrm{\&gamma;}=\frac{i+j}{2}+1(i+j=\mathrm{2,3},...);$η_ij represents the (i+j) order normalized central moment of the image. The central moment of (i+j) of f(x+y) of the image function is defined as Where Ω is the value interval of x and y. For N*M digital images, using summation instead of integral, the (i+j) order center can be expressed as Then the (i+j) order formatted central moment can be expressed as${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{{\mathrm{\&mu;}}_{\mathrm{ij}}}{{{\mathrm{\&mu;}}^{\mathrm{\&gamma;}}}_{00}},$ in$\mathrm{\&gamma;}=\frac{i+j}{2}+1(i+j=\mathrm{2,3},...);$

其中，七个Hu不变矩的归一化矩对平移、缩放、伸展和挤压变化不变；前六个Hu不变矩的归一化中心矩对旋转不变；第七个Hu不变矩的归一化中心矩对旋转不变并且对扭曲也不变。Among them, the normalized moments of the seven Hu invariant moments are invariant to translation, scaling, stretching and extrusion; the normalized central moments of the first six Hu invariant moments are invariant to rotation; the seventh Hu invariant The normalized central moments of moments are invariant to rotation and invariant to twist.

所述步骤(3)中引入新的参数包括：面积、周长、致密度、孔洞数目、孔洞数目和面积之比；其中，面积：用来计算孔洞所包含的像素数；周长：孔洞的轮廓线上像素间距离之和来度量；致密度：其中S为面积，L为周长；孔洞数目：一个包装上的孔洞数目；孔洞数目和面积之比：用来区分大孔洞和小孔洞。The new parameters introduced in the step (3) include: area, perimeter, compactness, number of holes, ratio of number of holes and area; wherein, area: used to calculate the number of pixels contained in holes; perimeter: the number of holes Measured by the sum of the distances between pixels on the contour line; compactness: Among them, S is the area, L is the perimeter; the number of holes: the number of holes on a package; the ratio of the number of holes to the area: used to distinguish large holes from small holes.

所述步骤(4)中使用最小二乘支持向量机选取1000个孔洞和2000个非孔洞进行训练，使用训练出来的分类器对分割好的超像素进行分类。In the step (4), use the least squares support vector machine to select 1000 holes and 2000 non-holes for training, and use the trained classifier to classify the segmented superpixels.

本发明解决其技术问题所采用的技术方案是：还提供一种基于形状特征提取的工业透明薄膜包装检测装置，包括：传送带模块，用于传送包装产品；图片拍摄模块，位于传送带模块的正上方，用于获取传送包装产品的图像；算法模块，与所述图片拍摄模块相连，用于根据上述的检测方法进行图片处理；结果分析模块，用于对图片处理的结果进行分析有益效果The technical solution adopted by the present invention to solve its technical problems is: it also provides an industrial transparent film packaging detection device based on shape feature extraction, including: a conveyor belt module, used to transport packaged products; a picture shooting module, located directly above the conveyor belt module , used to acquire the image of the conveying packaged product; the algorithm module is connected with the picture shooting module, and is used to process the picture according to the above-mentioned detection method; the result analysis module is used to analyze the result of the picture processing.

由于采用了上述的技术方案，本发明与现有技术相比，具有以下的优点和积极效果：本发明采用的形状特征提取不仅利用了七个Hu不变矩定义形状特征，并且引入了新的参数，还利用了主成分分析法进行了降维处理，并且利用LSSVM进行训练，然后将训练好的分类器对超像素块进行分类。本发明的方法不仅减少了时间，效果也比较好，在工业上具有可行性。Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the shape feature extraction adopted by the present invention not only utilizes seven Hu invariant moments to define shape features, but also introduces new Parameters, dimensionality reduction is also performed by principal component analysis, and LSSVM is used for training, and then the trained classifier is used to classify superpixel blocks. The method of the invention not only reduces the time, but also has better effect and is industrially feasible.

附图说明Description of drawings

图1是本发明的流程图；Fig. 1 is a flow chart of the present invention;

图2是使用本发明的装置结构示意图。Fig. 2 is a schematic diagram of the structure of the device using the present invention.

具体实施方式Detailed ways

下面结合具体实施例，进一步阐述本发明。应理解，这些实施例仅用于说明本发明而不用于限制本发明的范围。此外应理解，在阅读了本发明讲授的内容之后，本领域技术人员可以对本发明作各种改动或修改，这些等价形式同样落于本申请所附权利要求书所限定的范围。Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

本发明的实施方式涉及一种基于形状特征提取的工业透明薄膜包装检测方法，如图1所示，包括以下步骤：Embodiments of the present invention relate to an industrial transparent film packaging detection method based on shape feature extraction, as shown in Figure 1, comprising the following steps:

(1)对获取的包装产品图像进行预处理；(1) Preprocessing the obtained packaging product image;

(2)对预处理后的图像进行超像素的分割；(2) Carry out superpixel segmentation to the preprocessed image;

(3)利用七个Hu不变矩定义形状特征实现形状特征提取，并引入新的参数；(3) Use seven Hu invariant moments to define shape features to realize shape feature extraction, and introduce new parameters;

(4)对多变量参数矩阵进行处理，得到主成分；(4) Process the multivariate parameter matrix to obtain the principal components;

(5)使用最小二乘支持向量机根据形状特征进行训练，再对超像素块进行分类。(5) Use the least squares support vector machine to train according to the shape features, and then classify the superpixel blocks.

下面对每个步骤进行详细介绍。Each step is described in detail below.

预处理：Preprocessing:

首先利用Canny边缘检测，再利用膨胀操作。First use Canny edge detection, and then use expansion operation.

超像素分割：Superpixel segmentation:

以往对图像的理解是像素组成的二维矩阵，所以分割也是基于像素的，但是以像素为基础的分割会导致处理效率过低，因此本发明采用的是超像素分割。超像素是指图像中局部区域内连通的、亮度或者是颜色相近的像素的集合。本发明采用的是SLIC(Simple LinearIterative Clustering)超像素分割。In the past, the understanding of images is a two-dimensional matrix composed of pixels, so the segmentation is also based on pixels, but the pixel-based segmentation will lead to low processing efficiency, so the present invention uses superpixel segmentation. A superpixel is a collection of connected, similar in brightness or color pixels in a local area of an image. What the present invention adopts is SLIC (Simple Linear Iterative Clustering) superpixel segmentation.

特征提取feature extraction

本发明采用方法的是七个Hu不变矩定义形状特征，并且还引入的新的参数。七个Hu不变矩的表达是如下所示：The present invention uses seven Hu invariant moments to define shape features, and also introduces new parameters. The expressions of the seven Hu invariant moments are as follows:

φ₁＝η₂₀+η₀₂φ₁ =η₂₀ +η₀₂

${\mathrm{<span><span>\&phi;</span>\&phi;</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>=</span>=</span>{<span><span>(</span>(</span>{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>20</span>20</span>}}<span><span>-</span>-</span>{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>02</span>02</span>}}<span><span>)</span>)</span>}^{\mathrm{<span><span>2</span>2</span>}}<span><span>+</span>+</span>\mathrm{<span><span>4</span>4</span>}{\mathrm{<span><span>\&eta;</span>\&eta;</span>}}_{\mathrm{<span><span>11</span>11</span>}}^{\mathrm{<span><span>2</span>2</span>}}$

φ₃＝(η₃₀-3η₁₂)²+(3η₂₁-η₀₃)²φ₃ ＝(η₃₀ -3η₁₂ )² +(3η₂₁ -η₀₃ )²

φ₄＝(η₃₀+η₁₂)²+(η₂₁+η₀₃)²φ₄ ＝(η₃₀ +η₁₂ )² +(η₂₁ +η₀₃ )²

φ₅＝(η₃₀-3η₁₂)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]+φ₅ ＝(η₃₀ -3η₁₂ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]+

(3η₂₁-η₀₃)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²](3η₂₁ -η₀₃ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ]

φ₆＝(η₂₀-η₀₂)[(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]+4η₁₁(η₃₀+η₁₂)(η₂₁+η₀₃)φ₆ =(η₂₀ -η₀₂ )[(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ]+4η₁₁ (η₃₀ +η₁₂ )(η₂₁ +η₀₃ )

φ₇＝(3η₂₁-η₀₃)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]+φ₇ ＝(3η₂₁ -η₀₃ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]+

(η₀₃-η₁₂)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²](η₀₃ -η₁₂ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ]

其中，η_ij表示图像的(i+j)阶规格化中心矩。图像函数的f(x+y)的(i+j)的中心矩定义为其中Ω为x，y的取值区间。对于N*M的数字图像，利用求和代替积分，则(i+j)阶中心可表示为则(i+j)阶格式化中心矩可以表示为${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{{\mathrm{\&mu;}}_{\mathrm{ij}}}{{{\mathrm{\&mu;}}^{\mathrm{\&gamma;}}}_{00}},$其中$\mathrm{\&gamma;}=\frac{i+j}{2}+1(i+j=\mathrm{2,3},...).$Among them, η_ij represents the (i+j) order normalized central moment of the image. The central moment of (i+j) of f(x+y) of the image function is defined as Where Ω is the value interval of x and y. For N*M digital images, using summation instead of integral, the (i+j) order center can be expressed as Then the (i+j) order formatted central moment can be expressed as${\mathrm{\&eta;}}_{\mathrm{ij}}=\frac{{\mathrm{\&mu;}}_{\mathrm{ij}}}{{{\mathrm{\&mu;}}^{\mathrm{\&gamma;}}}_{00}},$ in$\mathrm{\&gamma;}=\frac{i+j}{2}+1(i+j=\mathrm{2,3},...).$

经过计算的的不变矩特征为F_m＝φ₁,φ₂.......φ₇，其中高阶矩的值很小，故在匹配的时候需要进行标准化处理，归一化矩对平移、缩放、伸展和挤压变化不变。另外，前6个归一化中心矩对旋转不变，而第7个对扭曲也不变。The calculated invariant moment features are F_m = φ₁ , φ₂ ... φ₇ , where the value of the high-order moments is very small, so it needs to be standardized when matching, and the normalized moments Invariant to translation, scaling, stretching and pinching changes. In addition, the first 6 normalized central moments are invariant to rotation, while the seventh is also invariant to twist.

虽然这七个矩那能很好地描述形状特征，但是当图像数据库较大时，仅仅这七个标量是不够的，本发明引进了新的参数：面积、周长、致密度、孔洞数目、孔洞数目和面积之比。面积、周长、致密度、孔洞数目、孔洞数目和面积之比的表述如下所示：Although these seven moments can describe the shape features well, when the image database is large, only these seven scalars are not enough. The present invention introduces new parameters: area, perimeter, density, number of holes, The ratio of the number of holes to the area. The expressions of area, perimeter, density, number of holes, and ratio of number of holes to area are as follows:

1)面积：用来计算孔洞所包含的像素数；1) Area: used to calculate the number of pixels contained in the hole;

2)周长：孔洞的轮廓线上像素间距离之和来度量，并列的像素点之间的距离是1个像素，倾斜方向间像素的距离在进行周长测量时，需要根据像素之间连接方式进行分别计算距离；2) Circumference: Measured by the sum of the distances between pixels on the contour line of the hole, the distance between parallel pixels is 1 pixel, and the distance between pixels in the oblique direction needs to be measured according to the connection between pixels. method to calculate the distance separately;

3)致密度：其中S为面积，L为周长；3) Density: Where S is the area and L is the perimeter;

4)孔洞数目：计算一个包装上的孔洞数目；4) Number of holes: calculate the number of holes on a package;

5)孔洞数目和面积之比：主要用来区分大孔洞和小孔洞。5) The ratio of the number of holes to the area: it is mainly used to distinguish large holes from small holes.

主成分分析principal component analysis

主成分分析的主要思想是对多变量的参数矩阵进行矩阵处理，得到的是原始变量的线性组合，并两两不相关，能最大限度地反应原始变量所包含的信息。The main idea of principal component analysis is to perform matrix processing on the multivariate parameter matrix, and obtain a linear combination of the original variables, which are not correlated with each other, and can reflect the information contained in the original variables to the maximum extent.

LSSVM分类LSSVM classification

使用最小二乘支持向量机(Least Squares Support Vector Machine，LSSVM)将分割的超像素分类为孔洞和非孔洞。对于最小二乘支持向量机，优化问题可以表示为：The segmented superpixels are classified into holes and non-holes using Least Squares Support Vector Machine (LSSVM). For the least squares support vector machine, the optimization problem can be expressed as:

$\mathrm{<span><span>min</span>min</span>}\mathrm{<span><span>J</span>J</span>}<span><span>(</span>(</span>\mathrm{<span><span>w</span>w</span>}<span><span>,</span>,</span>\mathrm{<span><span>\&xi;</span>\&xi;</span>}<span><span>)</span>)</span><span><span>=</span>=</span>\frac{\mathrm{<span><span>1</span>1</span>}}{\mathrm{<span><span>2</span>2</span>}}\mathrm{<span><span>w</span>w</span>}<span><span>\&CenterDot;</span>\&Center\; Dot;</span>\mathrm{<span><span>w</span>w</span>}<span><span>+</span>+</span>\mathrm{<span><span>\&gamma;</span>\&gamma;</span>}\underset{\mathrm{<span><span>i</span>i</span>}<span><span>=</span>=</span>\mathrm{<span><span>1</span>1</span>}}{\overset{\mathrm{<span><span>t</span>t</span>}}{\mathrm{<span><span>\&Sigma;</span>\&Sigma;</span>}}}{\mathrm{<span><span>\&xi;</span>\&xi;</span>}}_{\mathrm{<span><span>i</span>i</span>}}^{\mathrm{<span><span>2</span>2</span>}}<span><span>-</span>-</span><span><span>-</span>-</span><span><span>-</span>-</span><span><span>(</span>(</span>\mathrm{<span><span>4.9</span>4.9</span>}<span><span>)</span>)</span>$

使用拉格朗日求解上述优化问题，转换为求解一个线性方程问题。Using Lagrangian to solve the above optimization problem is transformed into solving a linear equation problem.

在LSSVM的训练中，选取1000个孔洞，2000个非孔洞进行训练，即训练3000组数据。使用训练出来的分类器对分割好的超像素进行分类，每个超像素被分为孔洞和非孔洞。In the training of LSSVM, 1000 holes and 2000 non-holes are selected for training, that is, 3000 sets of data are trained. The segmented superpixels are classified using the trained classifier, and each superpixel is divided into holes and non-holes.

本发明的第二实施方式涉及基于形状特征提取的工业透明薄膜包装检测装置，如图2所示，包括：传送带模块1，图片拍摄模块2，算法模块3，结果分析模块4。传送带模块1由传送带组成，其作用是匀速传送工业包装产品，图片拍摄模块2位于传送带模块正上方，作用是运用碗状光源和广角镜头进行拍摄从而获取包装产品的图像，算法模块3的作用是根据上述检测方法进行图片处理，结果分析模块4的作用是对图片处理的结果进行分析。The second embodiment of the present invention relates to an industrial transparent film packaging detection device based on shape feature extraction, as shown in FIG. The conveyor belt module 1 is composed of a conveyor belt, and its function is to convey industrial packaging products at a constant speed. The picture shooting module 2 is located directly above the conveyor belt module, and its function is to use a bowl-shaped light source and a wide-angle lens to capture images of the packaging products. The function of the algorithm module 3 is based on The above detection method performs image processing, and the function of the result analysis module 4 is to analyze the image processing result.

不难发现，本发明采用的形状特征提取不仅利用了七个Hu不变矩定义形状特征，并且引入了新的参数，还利用了主成分分析法进行了降维处理，并且利用LSSVM进行训练，然后将训练好的分类器对超像素块进行分类。本发明的方法不仅减少了时间，效果也比较好，在工业上具有可行性。It is not difficult to find that the shape feature extraction adopted in the present invention not only uses seven Hu invariant moments to define shape features, but also introduces new parameters, and also uses principal component analysis to perform dimension reduction processing, and uses LSSVM for training, The trained classifier is then used to classify superpixel patches. The method of the invention not only reduces the time, but also has better effect and is industrially feasible.

Claims (8)

Translated fromChinese

1.一种基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，包括以下步骤：1. An industrial transparent film packaging detection method based on shape feature extraction, is characterized in that, comprises the following steps:(1)对获取的包装产品图像进行预处理；(1) Preprocessing the obtained packaging product image;(2)对预处理后的图像进行超像素的分割；(2) Carry out superpixel segmentation to the preprocessed image;(3)利用七个Hu不变矩定义形状特征实现形状特征提取，并引入新的参数；(3) Use seven Hu invariant moments to define shape features to realize shape feature extraction, and introduce new parameters;(4)对多变量参数矩阵进行处理，得到主成分；(4) Process the multivariate parameter matrix to obtain the principal components;(5)使用最小二乘支持向量机根据形状特征进行训练，再对超像素块进行分类。(5) Use the least squares support vector machine to train according to the shape features, and then classify the superpixel blocks.2.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述包装产品图像运用碗状光源和广角镜头进行获取。2. The industrial transparent film packaging detection method based on shape feature extraction according to claim 1, wherein the packaged product image is acquired using a bowl-shaped light source and a wide-angle lens.3.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述步骤(1)中采用先利用Canny边缘检测，再利用膨胀操作对获取的包装产品图像进行预处理。3. the industrial transparent film packaging detection method based on shape feature extraction according to claim 1, is characterized in that, adopts to utilize Canny edge detection earlier in described step (1), then utilizes expansion operation to carry out to the packaged product image that obtains preprocessing.4.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述步骤(2)采用简单的线性迭代聚类的方式进行超像素分割。4. The industrial transparent film packaging detection method based on shape feature extraction according to claim 1, wherein said step (2) adopts simple linear iterative clustering to perform superpixel segmentation.5.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述步骤(3)中七个Hu不变矩分别为：5. the industrial transparent film packaging detection method based on shape feature extraction according to claim 1, is characterized in that, in the described step (3), seven Hu invariant moments are respectively:φ₁＝η₂₀+η₀₂；φ₁ =η₂₀ +η₀₂ ;φ₂＝(η₂₀-η₀₂)²+4η₁²₁；φ₂ =(η₂₀ -η₀₂ )² +4η₁²₁ ;φ₃＝(η₃₀-3η₁₂)²+(3η₂₁-η₀₃)²；φ₃ =(η₃₀ -3η₁₂ )² +(3η₂₁ -η₀₃ )² ;φ₄＝(η₃₀+η₁₂)²+(η₂₁+η₀₃)²；φ₄ =(η₃₀ +η₁₂ )² +(η₂₁ +η₀₃ )² ;φ₅＝(η₃₀-3η₁₂)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]φ₅ =(η₃₀ -3η₁₂ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]+(3η₂₁-η₀₃)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]；+(3η₂₁ -η₀₃ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ];φ₆＝(η₂₀-η₀₂)[(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]+4η₁₁(η₃₀+η₁₂)(η₂₁+η₀₃)；φ₆ =(η₂₀ -η₀₂ )[(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ]+4η₁₁ (η₃₀ +η₁₂ )(η₂₁ +η₀₃ );φ₇＝(3η₂₁-η₀₃)(η₃₀+η₁₂)[(η₃₀+η₁₂)²-3(η₂₁+η₀₃)²]+φ₇ ＝(3η₂₁ -η₀₃ )(η₃₀ +η₁₂ )[(η₃₀ +η₁₂ )² -3(η₂₁ +η₀₃ )² ]+(η₀₃-η₁₂)(η₂₁+η₀₃)[3(η₃₀+η₁₂)²-(η₂₁+η₀₃)²]；(η₀₃ -η₁₂ )(η₂₁ +η₀₃ )[3(η₃₀ +η₁₂ )² -(η₂₁ +η₀₃ )² ];η_ij表示图像的(i+j)阶规格化中心矩；η_ij represents the (i+j) order normalized central moment of the image;其中，七个Hu不变矩的归一化矩对平移、缩放、伸展和挤压变化不变；前六个Hu不变矩的归一化中心矩对旋转不变；第七个Hu不变矩的归一化中心矩对旋转不变并且对扭曲也不变。Among them, the normalized moments of the seven Hu invariant moments are invariant to translation, scaling, stretching and extrusion; the normalized central moments of the first six Hu invariant moments are invariant to rotation; the seventh Hu invariant The normalized central moments of moments are invariant to rotation and invariant to twist.6.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述步骤(3)中引入新的参数包括：面积、周长、致密度、孔洞数目、孔洞数目和面积之比；其中，面积：用来计算孔洞所包含的像素数；周长：孔洞的轮廓线上像素间距离之和来度量；致密度：其中S为面积，L为周长；孔洞数目：一个包装上的孔洞数目；孔洞数目和面积之比：用来区分大孔洞和小孔洞。6. The industrial transparent film packaging detection method based on shape feature extraction according to claim 1, wherein the new parameters introduced in the step (3) include: area, perimeter, density, number of holes, hole The ratio of number to area; among them, area: used to calculate the number of pixels contained in the hole; perimeter: measured by the sum of distances between pixels on the contour line of the hole; density: Among them, S is the area, L is the perimeter; the number of holes: the number of holes on a package; the ratio of the number of holes to the area: used to distinguish large holes from small holes.7.根据权利要求1所述的基于形状特征提取的工业透明薄膜包装检测方法，其特征在于，所述步骤(4)中使用最小二乘支持向量机选取1000个孔洞和2000个非孔洞进行训练，使用训练出来的分类器对分割好的超像素进行分类。7. the industrial transparent film packaging detection method based on shape feature extraction according to claim 1, is characterized in that, in described step (4), use least squares support vector machine to select 1000 holes and 2000 non-holes to train , use the trained classifier to classify the segmented superpixels.8.一种基于形状特征提取的工业透明薄膜包装检测装置，其特征在于，包括：传送带模块，用于传送包装产品；图片拍摄模块，位于传送带模块的正上方，用于获取传送包装产品的图像；算法模块，与所述图片拍摄模块相连，用于根据如权利要求1-7中任一权利要求所述的检测方法进行图片处理；结果分析模块，用于对图片处理的结果进行分析。8. An industrial transparent film packaging detection device based on shape feature extraction, characterized in that it includes: a conveyor belt module, used to transport packaged products; a picture shooting module, located directly above the conveyor belt module, used to acquire images of transported packaged products The algorithm module is connected with the picture shooting module, and is used to process the picture according to the detection method according to any one of claims 1-7; the result analysis module is used to analyze the result of the picture processing.