CN112598642A - High-speed high-precision visual detection method - Google Patents
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Abstract
The invention relates to a high-speed high-precision visual detection method, and belongs to the technical field of image recognition. The method specifically comprises the following steps: counting the measuring method of a measurer after the product is changed when the product process is changed; sorting the measurement methods and establishing a table document; selecting the difference key points of the measuring method under different conditions from the generalized measuring method table document; establishing a deep learning network, and classifying and dividing products by using the network; classifying all products in the bearable difference change into the products respectively, and setting a set of optimal parameters for each product; when a new product needs to be detected, defining the product into a certain classified class through a deep learning network, and calling the classified detection parameters; and after the optimal parameters are selected, measuring by using an image recognition algorithm. The invention can improve the data accuracy of visual identification in product detection, the adaptability of software to products and the efficiency of software detection.
Description
Technical Field
The invention belongs to the technical field of image recognition, and relates to a high-speed high-precision visual detection method.
Background
The image processing technology is a technology for processing image information by a computer. The method mainly comprises the steps of image digitization, image enhancement and restoration, image data coding, image segmentation, image identification and the like. The image processing technology comprises four methods of point processing, group processing, geometric processing and frame processing.
The image processing technology has wide application fields, is mainly used for precise positioning, precise measurement and the like in factory automation application, and has the precision reaching the micron level, but the technology has the defect that the influence of the characteristic change of a product to be side can be caused, and when the characteristic of the product to be side changes to a certain extent, the image processing technology is difficult to be adaptively adjusted along with the change. Deep learning is an important breakthrough in the field of artificial intelligence in the last decade. It has been used in speech recognition, natural language processing, computer vision, image and video analysis, multimedia and other fields with great success. The existing deep learning model belongs to a neural network. The origin of neural networks dates back to the 40's of the 20 th century, once seen in the eighties and ninety. The neural network tries to solve various machine learning problems by simulating a brain cognitive mechanism, the technology is mainly used for detecting appearance defects and simple logic judgment functions needing human participation in factory automation application, the technology has strong adaptability, but the technology cannot perform accurate measurement, and the detection speed is slow.
Currently, when the above-mentioned technology is used in some product inspection processes, the inspected object may need to go through a stamping process/injection molding process/electroplating process before being inspected. The production processes are influenced by temperature/humidity/production parameters in the production process, the appearance performance of the production processes has certain differences, and the detection by using a single technology has the following problems: 1) the uncertain change of the production process can cause great differences in color, brightness, warping degree and the like of products, and the traditional image recognition technology realizes detection and measurement by setting a brightness fixed threshold in a fixed area. When the product itself is too different, the real validity of the data is questionable when the detection/measurement is performed in a fixed area by a fixed brightness threshold, which results in the need to frequently adjust the detection area and the detection parameters in the actual production process. 2) The detection of defects on the surface of a product by using a single deep learning algorithm can be very complicated due to different detection requirements of different areas. The injection molding area/metal plating area/metal non-plating area will have different requirements. The more complex the neural network system is, the more network layers are, and the more network layers are, the slower the detection efficiency is.
In addition, the conventional image recognition technology cannot accurately process and detect images with large feature changes. When the product has the problems of warping, deformation and the like, the image recognition technology cannot well track and position. In validating the measurement data, the measured reference/measured position has a large impact on the final data result. For example, in a product drawing, the outer contour of the product is a standard rectangle, the length and the width of the product need to be measured, but a plane projection image of an actual product cannot be the standard rectangle, and even the sides of the rectangle are not straight. In order to ensure the dimension of the regular graph when the irregular graph is measured in the actual drawing, in the original measuring mode, the measuring mode can be adjusted in real time by a skilled measuring person according to the knowledge of the skilled measuring person on the product. But this adjustment of random strain is not possible with conventional image recognition techniques. The image recognition technology can replace the measurement process, but cannot replace the adjustment of the qualified measurement personnel according to the change of the product. The deep learning algorithm needs longer operation time, the traditional image recognition technology is utilized to extract important features, the deep learning algorithm is enabled to operate only aiming at the important features, and the visual inspection efficiency is improved. The method is characterized in that a deep learning algorithm is simply used, the defect features can only be established in a complex neural network system, and then a large number of data information pictures are utilized for learning training (in order to avoid the mutual interference of the defect features needing to be detected in different regions). Therefore, the whole network is very large, and the detection efficiency of the deep learning system which is originally low in detection efficiency is low.
Disclosure of Invention
In view of the above, the present invention provides a high-speed and high-precision visual inspection method, which performs deep fusion between an image processing technique and a deep learning technique, so as to improve the data accuracy of visual recognition in product inspection, the adaptability of software to products, and the efficiency of software inspection.
In order to achieve the purpose, the invention provides the following technical scheme:
a high-speed high-precision visual inspection method comprises the following steps: s1: counting and analyzing the measurement method information of a measurer after the product is changed when the product process is changed, and taking the measurement method information as basic data; s2: classifying and recording the basic data, namely sorting the measurement method information and establishing a measurement method table document; s3: selecting the difference key points of the measuring method under different conditions from the generalized measuring method table document; s4: classifying the measuring method and inputting the corresponding pictures into a database, establishing a deep learning network, and classifying and dividing the product by using the network; s5: classifying all products in the bearable difference change into the products respectively, and setting a set of optimal parameters for each product; s6: when a new product needs to be detected, defining the product into a certain classified class through a deep learning network, and calling the classified detection parameters; s7: and after the optimal parameters are selected, measuring by using an image recognition algorithm to obtain a measured value and generate a data report.
Further, in step S3, the differences of the measurement methods under different conditions mainly include: 1) adjusting the illumination of the light source: when the surfaces of different batches of products have differences, different light source illuminances are adjusted to carry out measurement; 2) adjusting the measuring position: and when the shapes of different batches of products are different, adjusting different measuring positions for measuring.
Further, in the visual inspection method, the neural network system is optimized, three neural network systems are established aiming at different types of defect characteristics of three different areas of a product to be inspected, and in each single neural network system, other areas are divided and filled; each neural network system completes an independent loop of detection environment.
Further, the detection by using three independent neural network systems specifically includes:
s1: dividing the defects into three types according to the detection requirement of the appearance defects, namely an injection molding area, a metal plating area and a metal non-plating area;
s2: according to the difference of the characteristics of the three defects, the color/brightness judgment in an image recognition algorithm is utilized to carry out segmentation and extraction: 1) injection molding area: is not reflective and is black; 2) metal non-plating area: reflecting light, silvery metallic color; 3) metal plating area: reflecting light, golden metallic color;
s3: positioning the position of the product by using an image recognition algorithm to obtain the position coordinate of the product;
s4: after obtaining the position coordinates of the product, extracting a color/brightness area at the position corresponding to the product: 1) injection molding area: reserving a black area on the product, and filling other color areas with white; 2) metal non-plating area: reserving a silver area on the product, and filling other areas with black; 3) metal plating area: reserving a golden area on the product, and filling other areas with black;
s5: the pictures which are respectively extracted by using different effect segmentation are stored in different folders;
s6: different deep learning networks are respectively established for pictures in folders in different areas, and each network also has different branches including but not limited to: scratching, pressing damage, glue overflow, glue shortage, poor electroplating, dirt and foreign matters;
s7: the three neural network systems synchronously operate in different GPUs, and the operation result of each neural network is stored in the cache of the industrial personal computer;
s8: and when all the networks are operated, integrating all the results and outputting a comprehensive result.
The invention has the beneficial effects that: the method provided by the invention utilizes the combination of the traditional image recognition algorithm and the deep learning algorithm to mutually make up the defects and solve the problems of short boards which cannot be solved by using a single algorithm originally. The method can better adapt to the production condition of an automatic factory, reduces the times of on-site maintenance personnel on-line debugging parameters to a certain extent, improves the detection efficiency to a certain extent, enables the efficiency of automatic detection equipment to be higher, correspondingly increases the return on investment rate, and has wide application prospect.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a block diagram of a detection system.
Detailed Description
The invention carries out deep fusion on an image processing technology and a deep learning technology, uses an AI technology to establish a deep learning neural network system for replacing judgment of a qualification depth measurer, firstly selects an optimal measuring mode through the AI technology of deep learning, and then uses an image recognition technology to carry out accurate measurement. Meanwhile, before the neural network is constructed, the image processing technology is utilized to segment/fill the picture, and a complex neural network is decomposed into a plurality of relatively simple neural network systems, so that the operation efficiency of the neural network systems is improved.
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of the method of the present invention, and fig. 2 is a block diagram of a detection system, as shown in the figure, the method provided by the present invention is used for product visual detection to improve detection accuracy, and the specific embodiment is as follows:
s1: counting and analyzing the measurement method information of a measurer after the product is changed when the product process is changed;
s2: the information is induced into 4 categories, and product image information of different categories is induced into a certain category by using a deep learning classification tool;
s3: a large number of pictures of 4 categories are used for constructing a neural network system, so that new products can be accurately summarized into the 4 categories when being identified;
class 1: plating darker, unbent products
Class 2: plated brighter, unbent products
Class 3: plating darker, slightly curved products
Class 4: plated brighter, slightly curved products
S4: according to the difference of the categories, different detection parameters are set:
1. for darker plated products, the edge detection threshold was set to 80
2. For products that are more brightly plated, the edge detection threshold is set to 120
3. The spacing of each measurement position was 0.12mm for the unbent product
4. The spacing of each measurement position was 0.115mm for a slightly curved product.
Before the mode is used, if no person follows to change parameters, the measured data of the automatic detection equipment is compared with the measured data of the measuring chamber, and after the data are compared, the difference of the measured data of the products in the same batch is less than 0.005 mm. However, the difference of the measured data of different batches of products can reach 0.02mm at most, and the difference of the measured data of 0.02mm is not acceptable. After the method is used, the difference of the measurement data of different batches of products is less than 0.01mm, and the difference of the measurement data of 0.01mm can well meet the actual requirement.
The technical scheme provided by the invention can achieve a good effect in the aspect of improving the processing speed, and the specific embodiment is as follows:
s1: according to the difference of the characteristics of the three defects, the color/brightness judgment in an image recognition algorithm is utilized to carry out segmentation and extraction:
1) injection molding area: is not reflective and is black; 2) metal non-plating area: reflecting light, silvery metallic color;
3) metal plating area: reflecting light, golden metallic color;
s2: and respectively establishing a neural network system for each area.
S3: during detection, the neural networks of the electroplating area and the non-electroplating area are distributed to the display card A for operation, and the neural network of the injection molding area is distributed to the display card B for operation
S4: and integrating the operation results and outputting the final detection result after all operations are finished.
Before the method is used, all areas are trained into a neural network system, and about 3000 pictures are needed to construct the neural network system with a good detection effect. After the method is used, only 1000 pictures are needed for constructing a neural network system with a good detection effect. When the neural network is trained and constructed, the improved efficiency is about 60 percent.
Before the method is used, only a single display card can be used for operation due to only one neural network system during actual detection. The time required to test a product was 0.8S. By using the method, the detected area is reduced, and the two display cards can be used for synchronous operation. The speed is increased to 0.25S. The speed is greatly increased, the production period of the equipment in the original 1S production period is increased to 0.45S, and the benefit can be obviously improved.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (4)
1. A high-speed high-precision visual detection method is characterized in that: the method comprises the following steps:
s1: counting and analyzing the measurement method information of a measurer after the product is changed when the product process is changed, and taking the measurement method information as basic data;
s2: classifying and recording the basic data, namely sorting the measurement method information and establishing a measurement method table document;
s3: selecting the difference key points of the measuring method under different conditions from the generalized measuring method table document;
s4: classifying the measuring method and inputting the corresponding pictures into a database, establishing a deep learning network, and classifying and dividing the product by using the network;
s5: classifying all products in the bearable difference change into the products respectively, and setting a set of optimal parameters for each product;
s6: when a new product needs to be detected, defining the product into a certain classified class through a deep learning network, and calling the classified detection parameters;
s7: and after the optimal parameters are selected, measuring by using an image recognition algorithm to obtain a measured value and generate a data report.
2. The high-speed high-precision visual inspection method according to claim 1, characterized in that: in step S3, the important differences of the measurement methods under different conditions mainly include:
1) adjusting the illumination of the light source: when the surfaces of different batches of products have differences, different light source illuminances are adjusted to carry out measurement;
2) adjusting the measuring position: and when the shapes of different batches of products are different, adjusting different measuring positions for measuring.
3. The high-speed high-precision visual inspection method according to claim 2, characterized in that: in the visual detection method, a neural network system is optimized, three neural network systems are established aiming at different types of defect characteristics of three different areas of a product to be detected, and in each single neural network system, other areas are divided and filled; each neural network system completes an independent loop of detection environment.
4. A high-speed high-precision visual inspection method according to claim 3, characterized in that: the detection by using three independent neural network systems specifically comprises the following steps:
s1: dividing the defects into three types according to the detection requirement of the appearance defects, namely an injection molding area, a metal plating area and a metal non-plating area;
s2: according to the difference of the characteristics of the three defects, the color/brightness judgment in an image recognition algorithm is utilized to carry out segmentation and extraction: 1) injection molding area: is not reflective and is black; 2) metal non-plating area: reflecting light, silvery metallic color; 3) metal plating area: reflecting light, golden metallic color;
s3: positioning the position of the product by using an image recognition algorithm to obtain the position coordinate of the product;
s4: after obtaining the position coordinates of the product, extracting a color/brightness area at the position corresponding to the product: 1) injection molding area: reserving a black area on the product, and filling other color areas with white; 2) metal non-plating area: reserving a silver area on the product, and filling other areas with black; 3) metal plating area: reserving a golden area on the product, and filling other areas with black;
s5: the pictures which are respectively extracted by using different effect segmentation are stored in different folders;
s6: different deep learning networks are respectively established for pictures in folders in different areas, and each network also has different branches including but not limited to: scratching, pressing damage, glue overflow, glue shortage, poor electroplating, dirt and foreign matters;
s7: the three neural network systems synchronously operate in different GPUs, and the operation result of each neural network is stored in the cache of the industrial personal computer;
s8: and when all the networks are operated, integrating all the results and outputting a comprehensive result.
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