CN113538345A - Industrial bottle and can device counting method based on image processing - Google Patents
Industrial bottle and can device counting method based on image processing Download PDFInfo
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Abstract
An industrial bottle and can device counting method based on image processing is characterized in that an industrial camera takes a snapshot of an industrial bottle and can device picture as a data source and transmits the snapshot to a computer image preprocessing part; carrying out image enhancement on the original industrial bottle device image by a histogram equalization enhancement technology; then, a wiener filtering algorithm is adopted to reduce noise of the image, and meanwhile, the edge profile of the bottle device is kept well; after image preprocessing is carried out, the bottle device and the background are segmented by adopting an OTSU maximum between-class variance algorithm to obtain an ideal bottle device binary image; removing false from the binary image of the bottle device by morphological expansion and corrosion operation; separating the adhered bottle and can devices by using a watershed segmentation algorithm; finally, the number of devices is counted based on the size area threshold. The device and the method are suitable for counting the number of the industrial bottle and can devices, replace the traditional manual counting mode, save the labor cost and improve the working efficiency of industrial production.
Description
Technical Field
The invention relates to the field of image processing, in particular to an industrial bottle and can device counting method based on image processing.
Background
The production of bottle-can type devices is huge in industry, and the bottle-can type devices are widely used, such as beverage bottles, dairy product packages, liquid medical medicine containers and the like. The traditional counting method for devices by manufacturers is usually a manual counting method, the counting method consumes a large amount of manpower time and is low in efficiency, and eye fatigue is easily caused due to long-time checking and counting by naked eyes, so that result errors are large.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides the image processing-based industrial bottle and can device counting method which is simple and easy to operate, has accurate and efficient calculation, can effectively count industrial bottle and can devices, replaces a manual counting mode, saves the labor cost and greatly improves the industrial work efficiency.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an industrial bottle and can device counting method based on image processing comprises the following steps:
step S1: overlooking and shooting images of the bottle and can devices, and transmitting the images to a computer system as a data source;
step S2: carrying out image enhancement on the original industrial bottle device image by a histogram equalization enhancement technology;
step S3: carrying out noise reduction processing on the original image by adopting a wiener filtering algorithm;
step S4: segmenting the bottle device and the background by adopting an OTSU maximum between-class variance algorithm to obtain an ideal bottle device binary image;
step S5: removing false operation of the binary image of the bottle device by adopting morphological expansion and corrosion operation;
step S6: separating the adhered bottle and can devices by using a watershed segmentation algorithm;
step S7: finally, the number of devices is counted based on the size area threshold.
Further, in step S2, the histogram is a visual statistic of the gray level distribution range of the image, and the gray level histogram function represents the gray level range of [0, L-1 ].
h(rp)＝np
Where np is the number of pixels whose image gray scale is rp, and rp is the p-th gray scale in the image.
Still further, in step S3, the wiener filter is an optimal linear filtering method proposed according to the minimum mean square error criterion, and the application range is wide. When the image is subjected to wiener filtering, the mean and variance of the local matrix of the target pixel a (n1, n2) are estimated, and the calculation formula is as follows:
wherein η is a neighborhood of M × N of a certain pixel;
then estimating the transformed pixel value b (n1, n2) by using a wiener filter;
wherein v is2Is the image variance.
In step S4, the image is binarized by using the maximum inter-class variance algorithm of the OSTU, and a threshold T is obtained.
In step S5, morphological operations such as erosion and dilation are performed on the extracted binary image, so that noise can be effectively eliminated, and a complete device region and clear edge information can be obtained.
In step S6, a distance-based watershed segmentation method is used, and after distance change is performed on the binary image, a distance set from each pixel in the binary image to a pixel closest to a zero value is obtained, and watershed transformation is performed according to the set;
complementing the target binary image to obtain a distance transformation function:
D(p)＝min{dist(p,q),q∈m}
the region of interest is m, D represents a set after distance transformation, p and q are pixel points, and dist is a distance function.
After the processing of steps S2, S3, S4, S5, and S6, the target regions are basically binary images of the bottle-can devices in a plan view state separated from each other, and a counting method based on a size area is adopted for the statistical number.
According to the method, firstly, the image of the device is enhanced through a histogram equalization enhancement technology, then the original image is subjected to primary noise reduction processing through wiener filtering, after a binary image of a more ideal original image is extracted through a maximum between-class variance algorithm, false removing processing is performed through morphological corrosion and expansion operation, the secondary noise reduction effect is achieved, bottle and can devices in the binary image are mutually independent through a better segmentation effect of a watershed algorithm, and therefore counting is achieved through a counting method based on the size area.
The invention has the following beneficial effects: the counting machine is simple and easy to operate, is accurate and efficient in calculation, can effectively count industrial bottle devices, replaces a manual counting mode, saves labor cost and greatly improves industrial work efficiency.
Drawings
FIG. 1 is a system flow diagram of an industrial can device counting method based on image processing.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, an image processing-based industrial bottle and can device counting method includes the following steps:
step S1: image acquisition
The high-definition images of the bottle and can devices can be obtained by a high-definition camera in a way of overhead shooting, the images are clear, have no shielding and are not obvious, the shot images are used as data sources of the method, the number of the bottle and can devices is counted, the arrangement modes of the same kind of bottle and can devices in the images are random, and then the images are transmitted to a computer for processing and counting.
Step 2: histogram equalization enhancement
Preprocessing a target image, performing image enhancement on the image of the bottle device by utilizing a gray level histogram, enhancing the contrast ratio of a device region and a background, and providing a more real digital image for noise reduction, false removal and counting of a subsequent image;
the histogram is a visual statistic of the gray level distribution range of the image, with the gray level histogram function representing the gray level range of [0, L-1 ].
h(rp)＝np
Np is the number of pixels with the image gray level rp, and rp is the p-th gray level in the image;
and step 3: wiener filtering noise reduction
Carrying out first noise reduction on the original picture subjected to histogram equalization enhancement through wiener filtering, wherein the noise reduction is carried out aiming at eliminating noise caused by factors such as shooting angle, light intensity and vibration and simultaneously keeping the edge outline of a bottle device in the image;
wiener filtering is an optimal linear filtering method provided according to the minimum mean square error criterion, and has a wide application range. When the image is subjected to wiener filtering, the mean and variance of the local matrix of the target pixel a (n1, n2) are estimated, and the calculation formula is as follows:
wherein η is a neighborhood of M × N of a certain pixel;
then estimating the transformed pixel value b (n1, n2) by using a wiener filter;
wherein v is2Is the image variance.
And 4, step 4: OTSU binary image extraction
After image processing, what is needed for counting is a binary image of the can device. The OTSU maximum between-class variance algorithm is based on a clustering idea, data is divided into two categories by using a threshold T, when the variance between the thresholds is maximum, the probability of wrong division is minimum, so that the gray value of a pixel point with the gray value larger than the threshold T is adjusted to be 255 again, and the gray value of a pixel point with the gray value smaller than the threshold T is adjusted to be 0. The device and the background are segmented by the algorithm, and an ideal device binary image is obtained.
And 5: morphological operations to remove artifacts
Erosion and dilation are basic operations of mathematical morphology, where erosion reduces the area of a white region, while dilation is the expansion of a white region in an image, and the pixel area is changed by erosion and erosion to obtain an ideal output image.
Step 6: watershed segmentation based on distance
The watershed algorithm based on distance can effectively separate the adhered objects. For the condition that a plurality of bottle devices are adhered and abutted in the image, the device is relatively independent by complementing the target image to obtain distance conversion and then segmenting, so that a subsequent counting method is conveniently utilized;
adopting a distance-based watershed segmentation method, obtaining a distance set from each pixel in the binary image to a pixel closest to a zero value after distance change is carried out on the binary image, and carrying out watershed transformation according to the set;
complementing the target binary image to obtain a distance transformation function:
D(p)＝min{dist(p,q),q∈m}
the region of interest is m, D represents a set after distance transformation, p and q are pixel points, and dist is a distance function.
And 7: counting method based on size area
After the processing of steps S2, S3, S4, S5 and S6, the target regions are basically binary images of the bottle-can devices in a mutually separated, overlooked state, and a counting method based on size and area is adopted for counting the number;
after the images are processed for a plurality of times in the previous period, the target areas are basically separated from each other, 2/3 sizes of corresponding actual bottle size areas are selected as judgment threshold values S in the algorithm process, when the areas are larger than the threshold values S, the devices are determined, the number is increased by 1, otherwise, no operation is performed, and finally the number of the devices in the input bottle device images can be obtained.
The embodiments described in this specification are merely illustrative of implementations of the inventive concepts, which are intended for purposes of illustration only. The scope of the present invention should not be construed as being limited to the particular forms set forth in the examples, but rather as being defined by the claims and the equivalents thereof which can occur to those skilled in the art upon consideration of the present inventive concept.
Claims (7)
1. An industrial bottle and can device counting method based on image processing is characterized by comprising the following steps:
step S1: overlooking and shooting images of the bottle and can devices, and transmitting the images to a computer system as a data source;
step S2: carrying out image enhancement on the original industrial bottle device image by a histogram equalization enhancement technology;
step S3: carrying out noise reduction processing on the original image by adopting a wiener filtering algorithm;
step S4: segmenting the bottle device and the background by adopting an OTSU maximum between-class variance algorithm to obtain an ideal bottle device binary image;
step S5: removing false operation of the binary image of the bottle device by adopting morphological expansion and corrosion operation;
step S6: separating the adhered bottle and can devices by using a watershed segmentation algorithm;
step S7: finally, the number of devices is counted based on the size area threshold.
2. The image processing based industrial canister device counting method according to claim 1, characterized in that: in step S2, the histogram is a visual statistic of the gray-level distribution range of the image, and the gray-level histogram function represents the gray-level range of [0, L-1 ].
h(rp)＝np
Where np is the number of pixels whose image gray scale is rp, and rp is the p-th gray scale in the image.
3. Image processing based industrial bottle and can device counting method according to claim 1 or 2, characterized in that: in step S3, the wiener filter is an optimal linear filtering method proposed according to the minimum mean square error criterion, and the application range is wide. When the image is subjected to wiener filtering, the mean and variance of the local matrix of the target pixel a (n1, n2) are estimated, and the calculation formula is as follows:
μ＝1/MN∑n1,n2∈ηa(n1,n2)
σ2＝1/MN∑n1,n2∈ηa2(n1,n2)-μ2
wherein η is a neighborhood of M × N of a certain pixel;
then estimating the transformed pixel value b (n1, n2) by using a wiener filter;
wherein v is2Is the image variance.
4. Image processing based industrial bottle and can device counting method according to claim 1 or 2, characterized in that: in step S4, the image is binarized by using the maximum inter-class variance algorithm of the OSTU, and a threshold T is obtained.
5. Image processing based industrial bottle and can device counting method according to claim 1 or 2, characterized in that: in step S5, morphological operations such as erosion and dilation are performed on the extracted binary image, so that noise can be effectively eliminated, and a complete device region and clear edge information can be obtained.
6. Image processing based industrial bottle and can device counting method according to claim 1 or 2, characterized in that: in step S6, a distance-based watershed segmentation method is used to obtain a distance set from each pixel in the binary image to a pixel closest to a zero value after distance change is performed on the binary image, and watershed transformation is performed according to the set;
complementing the target binary image to obtain a distance transformation function:
D(p)＝min{dist(p,q),q∈m}
the region of interest is m, D represents a set after distance transformation, p and q are pixel points, and dist is a distance function.
7. The image processing based industrial canister device counting method according to claim 1, characterized in that: after the processing of steps S2, S3, S4, S5, and S6, the target regions are basically binary images of the bottle-can devices in a plan view state separated from each other, and a counting method based on a size area is adopted for the statistical number.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103914843A (en) * | 2014-04-04 | 2014-07-09 | 上海交通大学 | Image segmentation method based on watershed algorithm and morphological marker |
| WO2016091016A1 (en) * | 2014-12-12 | 2016-06-16 | 山东大学 | Nucleus marker watershed transformation-based method for splitting adhered white blood cells |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914843A (en) * | 2014-04-04 | 2014-07-09 | 上海交通大学 | Image segmentation method based on watershed algorithm and morphological marker |
| WO2016091016A1 (en) * | 2014-12-12 | 2016-06-16 | 山东大学 | Nucleus marker watershed transformation-based method for splitting adhered white blood cells |
Non-Patent Citations (2)
| Title |
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| 王念富: "基于机器视觉的羊群图像分割及计数算法研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》, no. 2 * |
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