CN119936026A

CN119936026A - An AOI visual online high-speed detection method and system

Info

Publication number: CN119936026A
Application number: CN202510183697.9A
Authority: CN
Inventors: 袁隆宾; 朱旺; 叶啸龙
Original assignee: Shenzhen Yingshang Semiconductor Technology Co ltd
Current assignee: Shenzhen Yingshang Semiconductor Technology Co ltd
Priority date: 2025-02-19
Filing date: 2025-02-19
Publication date: 2025-05-06

Abstract

The present invention proposes an AOI visual online high-speed detection method and system, the method comprising: obtaining the type information of products on the production line, confirming the features of the products that need to be detected, and setting the features that need to be detected as target features to obtain multiple target features; setting the frame rate and resolution of the camera according to the actual size of the target features and the required clarity; obtaining the surface material information in the pre-acquired product type information, selecting the light source according to the surface material information, and setting the irradiation angle and intensity of the light source so that the light source will not be reflected on the image surface and thus cause image quality problems; obtaining photos of multiple target features of the product, comparing the multiple target features according to standard parameters, and dividing the products into qualified products and unqualified products according to the comparison results. The technical problems of the influence of ambient light on image quality and light source adaptation caused by the reflection specificity of multiple materials in the prior art are solved.

Description

AOI vision online high-speed detection method and system

Technical Field

The invention relates to the technical field of AOI detection, in particular to an AOI visual on-line high-speed detection method and system.

Background

AOI technology is widely used in quality control in electronics, automobiles, printing and other industries. It relies on high speed, high precision image capturing and processing capabilities to automatically detect the appearance, shape, size and color differences of the product. Modern AOI systems typically combine computer vision and machine learning techniques for improving the accuracy and efficiency of detection. However, the speed and the precision are difficult to balance in a high-speed production environment, the image quality caused by ambient light is unstable, various materials cannot be effectively processed by a light source due to the difference of reflection characteristics, the visual quality of a product is affected by limited color difference detection capability, and the defects in the aspects of complex geometric structure identification and system integration are overcome. These problems affect the overall efficiency and degree of automation of the AOI system.

Disclosure of Invention

Based on the technical problems, the invention provides an AOI visual on-line high-speed detection method and system, and the adopted technical scheme is as follows:

An AOI vision on-line high-speed detection method, the method comprising:

S1, obtaining type information of products on a production line, confirming characteristics of the products to be detected, setting the characteristics to be detected as target characteristics, and obtaining a plurality of target characteristics;

S2, setting the frame rate and the resolution of the camera according to the actual size of the target feature and the required definition;

s3, acquiring surface material information in the pre-acquired product type information, selecting a light source according to the surface material information, and setting the irradiation angle and the intensity of the light source so that the light source irradiates on the surface of an image and is not reflected, thereby causing the quality problem of the image;

And S4, obtaining photos of a plurality of target features of the product, comparing the target features according to standard parameters, and dividing the product into qualified products and unqualified products according to comparison results.

Preferably, the S1 includes:

S11, scanning identification information of products on a production line through scanning equipment, and matching the acquired identification information with a central database to acquire category information of the products;

And S12, setting the features to be detected as target features according to the requirements of feature detection of the products in the central database, wherein the target features comprise, but are not limited to, shapes and sizes, surface defects, colors and structural features, obtaining a plurality of target features, and setting an image acquisition sequence for each target feature.

Preferably, the S2 includes:

S21 of acquiring an actual size of each target feature, the actual size representing a size of the target feature in length, and setting a frame rate of the camera according to a minimum actual size of the plurality of target features, and setting the frame rate of the camera according to the following formula, Wherein D represents the moving distance between frames, V represents the speed of the production line, namely the moving distance of the product in the production line in unit time, F represents the frame rate of the camera, namely the number of camera photos in unit time, and when the frame rate of the camera meets the condition that D is less than or equal to the actual size, the frame rate setting of the camera is completed at the moment;

S22, setting the corresponding resolution of the camera under each definition according to the definition required by each target feature, and switching the camera to the resolution required by the next target feature according to the preset image acquisition sequence after the current target feature image is acquired.

Preferably, the S3 includes:

s31, obtaining surface material information of a product according to the type information of the product, wherein the surface material information comprises, but is not limited to, plastics, metals, ceramics, glass, corresponding reflection characteristics and surface smoothness;

S32, selecting a light source according to the surface material information, wherein the light source comprises but is not limited to an LED, a laser and infrared light, setting the irradiation angle and the intensity of the light source according to the reflection angle of the surface material, and arranging the light source in a ring shape.

Preferably, the S4 includes:

S41, acquiring a shape and size characteristic photo of a product, acquiring contour information and a size value of the product according to the shape and size characteristic photo, comparing the contour information and the size value with tolerance ranges in a contour template and standard parameters, judging that the shape and the size of the product are qualified if the contour information is consistent with the contour template and the size value is within the tolerance ranges, detecting the next target characteristic, judging that the shape and the size of the product are not qualified if the contour information and the size value are not consistent or are not consistent, and classifying the product into a shape and size unqualified area;

s42, obtaining a surface feature photo of a product, determining the type, the size, the number and the depth of defects on the surface of the product according to the surface feature photo, determining whether the product is qualified or not according to the maximum allowable size, the maximum allowable size and the maximum allowable depth of the defects, judging that the product is qualified if the size, the number and the depth corresponding to one defect do not reach the maximum allowable size, the number and the depth corresponding to the defect, detecting the next target feature, judging that the product is unqualified if any one of the size, the number and the depth corresponding to the defect exceeds the maximum allowable size of the defect, and classifying the product into a surface defect area;

S43, obtaining a color feature photo of the product, obtaining an actual color value of the product according to the color feature photo, comparing the actual color value with a standard color value required by the product, quantifying the difference through a color difference formula, wherein the color difference formula is as follows: Wherein, L represents the difference between the actual brightness and standard brightness of the product, a represents the difference between the actual redness and greenness and standard redness of the product, b represents the difference between the actual redness Lan Du and standard redness and blueness of the product, when L is more than or equal to 0 and less than or equal to 1, the difference between the actual color value and standard color value of the product is less than or equal to 1, the difference between the color of the product and the standard color value is less than or equal to 2, the detection of the next target characteristic is carried out, when L is more than or equal to 1, the difference between the actual color value and standard color value of the product is more than or equal to 2, the difference between the color of the product and the standard color value is more than or equal to 1, and the product is classified into the color difference unqualified region;

S44, obtaining a structural feature photo of the product, aligning the structural feature photo with the standard template image, and confirming a deviation value of a structure of the product and the structure on the standard template image, wherein the deviation value comprises a deviation value of the structure in an angle and a deviation value of the structure in a position horizontal direction, if the deviation value is within a tolerance range, the product is qualified, the product is classified as a qualified product, and if the deviation value is outside the tolerance range, the product is classified as a structure deviation area.

An AOI vision on-line high-speed detection system, the system comprising:

The product information identification system acquires the type information of products on a production line, confirms the characteristics of the products to be detected, and sets the characteristics to be detected as target characteristics to obtain a plurality of target characteristics;

the camera configuration system is used for setting the frame rate and the resolution of the camera according to the actual size of the target feature and the definition required to be achieved;

The light source setting system is used for acquiring surface material information in the pre-acquired product type information, selecting a light source according to the surface material information, and setting the irradiation angle and the intensity of the light source so that the light source irradiates on the surface of an image and is not reflected to cause the quality problem of the image;

and the image detection and product classification system is used for acquiring photos of a plurality of target features of the product, comparing the target features according to standard parameters and dividing the product into qualified products and unqualified products according to comparison results.

Preferably, the product information identification system includes:

The product identification information matching system is used for scanning the identification information of the product on the production line through scanning equipment, and matching the acquired identification information with a central database to acquire the type information of the product;

and the acquisition sequence setting system is used for setting the characteristics to be detected as target characteristics according to the requirements of the central database on the characteristic detection of the product, wherein the target characteristics comprise, but are not limited to, shape and size, surface defects, colors and structural characteristics, a plurality of target characteristics are obtained, and an image acquisition sequence is set for each target characteristic.

Preferably, the camera configuration system comprises:

A camera frame rate setting system that acquires an actual size of each target feature, the actual size representing a size of the target feature in length, and sets a frame rate of the camera according to a minimum actual size of the plurality of target features, and sets the frame rate of the camera according to the following formula, Wherein D represents the moving distance between frames, V represents the speed of the production line, namely the moving distance of the product in the production line in unit time, F represents the frame rate of the camera, namely the number of camera photos in unit time, and when the frame rate of the camera meets the condition that D is less than or equal to the actual size, the frame rate setting of the camera is completed at the moment;

and the camera resolution setting system sets the corresponding resolution of the camera under each definition according to the definition required to be achieved by each target feature, and when the current target feature image is acquired, the camera is switched to the resolution required by the next target feature according to the preset image acquisition sequence.

Preferably, the light source setting system includes:

The surface texture analysis system is used for acquiring surface texture information of the product according to the type information of the product, wherein the surface texture information comprises, but is not limited to, plastics, metals, ceramics, glass, corresponding reflection characteristics and surface smoothness;

The intelligent light source configuration system is used for selecting light sources according to surface material information, wherein the light sources comprise, but are not limited to, LEDs, lasers and infrared light, setting the irradiation angle and the intensity of the light sources according to the reflection angle of the surface material, and arranging the light sources in a ring shape.

Preferably, the image detection and product classification system comprises:

The shape and size detection system is used for acquiring a shape and size characteristic photo of a product, acquiring contour information and a size numerical value of the product according to the shape and size characteristic photo, comparing the contour information and the size numerical value with tolerance ranges in a contour template and standard parameters, judging that the shape and the size of the product are qualified if the contour information is consistent with the contour template and the size numerical value is within the tolerance ranges, detecting the next target characteristic, judging that the shape and the size of the product are unqualified if one or both of the contour information and the size numerical value are not consistent, and classifying the product into a shape and size unqualified area;

The surface defect detection system comprises a surface feature photo of a product, a product judgment system, a surface defect detection system and a surface defect detection system, wherein the surface feature photo of the product is obtained, the type, the size, the number and the depth of defects on the surface of the product are determined according to the surface feature photo, the maximum allowable size and the maximum allowable depth of the defects are determined, whether the product is qualified or not is determined, if the size, the number and the depth corresponding to one defect do not reach the maximum allowable size, the number and the depth corresponding to the defect are not reached, the product is judged to be unqualified, and the product is classified into a surface defect area if any one of the size, the number and the depth corresponding to the defect exceeds the maximum allowable size, the number and the maximum allowable depth of the defect;

the color difference analysis system is used for acquiring a color feature photo of a product, acquiring an actual color value of the product according to the color feature photo, comparing the actual color value with a standard color value required by the product, quantifying the difference through a color difference formula, and the color difference formula is as follows: Wherein, L represents the difference between the actual brightness and standard brightness of the product, a represents the difference between the actual redness and greenness and standard redness of the product, b represents the difference between the actual redness Lan Du and standard redness and blueness of the product, when L is more than or equal to 0 and less than or equal to 1, the difference between the actual color value and standard color value of the product is less than or equal to 1, the difference between the color of the product and the standard color value is less than or equal to 2, the detection of the next target characteristic is carried out, when L is more than or equal to 1, the difference between the actual color value and standard color value of the product is more than or equal to 2, the difference between the color of the product and the standard color value is more than or equal to 1, and the product is classified into the color difference unqualified region;

And the structural feature alignment system is used for acquiring a structural feature photo of the product, aligning the structural feature photo with the standard template image, and confirming deviation values of the structure of the product and the structure on the standard template image, wherein the deviation values comprise deviation values of the structure in an angle and deviation values of the structure in a position horizontal direction, if the deviation values are within a tolerance range, the product is qualified, the product is classified as a qualified product, and if the deviation values are outside the tolerance range, the product is classified as a structural deviation area.

The AOI visual online high-speed test method and system have the beneficial effects that the detection precision and speed are remarkably improved through optimizing the methods of camera setting, intelligent light source configuration, multi-target feature detection and the like. By utilizing an advanced color difference algorithm and a structural feature alignment technology, the system can accurately judge complex geometric structures and chromatic aberration, and simultaneously, the optical adaptability to different materials is enhanced. In addition, by systematic detection module integration, manual intervention is reduced, the overall automation level and the reliability of product quality control are improved, and finally the efficiency of a production line is improved, and the defect rate and the rework cost are reduced.

Drawings

FIG. 1 is a diagram of an AOI visual on-line high-speed detection method according to the invention.

Detailed Description

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.

In one embodiment of the invention, an AOI visual on-line high-speed detection method comprises the following steps:

The technical scheme has the working principle and the effect that the system firstly acquires the type information of the products on the production line and identifies the characteristics of the products to be detected. These features often include size, shape, color, surface features, etc., according to which the system sets the detection target. The system adjusts the frame rate and resolution of the camera based on the actual size of the target feature and the required sharpness. High frame rates effectively capture fast moving products, while high resolution ensures that the details of the image are sufficient for accurate analysis. And selecting a proper light source type comprising an LED or laser by using the obtained product material information, and adjusting the irradiation angle and intensity of the light source. By these adjustments, the system avoids the negative effects of light reflection on image quality, ensuring that the image is clearly usable. The camera takes pictures for each target feature and processes the pictures. The system judges whether the product meets the quality requirement or not through comparison with standard parameters (such as tolerance range and color difference standard) and templates. By means of automatic detection, the quality of the product can be monitored and evaluated in real time in the production process. The system reduces the manual detection error and the manual intervention requirement, and greatly improves the production efficiency and consistency. The method can be rapidly adapted to the detection requirements of various products, and realizes comprehensive nondestructive inspection of the product characteristics by flexibly adjusting parameters of a camera and a light source and an efficient image processing and analyzing method. Finally, such automated systems not only help manufacturers ensure high standard product quality, but also provide them with more immediate and accurate quality management information, improving the efficiency and reliability of the overall production line.

In one embodiment of the present invention, the S1 includes:

The technical scheme has the working principle and the effect that the scanning equipment is used for automatically scanning the identification information of each product on the production line. The identification information obtained by scanning is transmitted to the system and matched with records in the central database. The database contains detailed information of each product including its type, specification, inspection criteria, etc. Once the matching is successful, the system retrieves the category information of the specific product to form a basic information set. Based on the product type information extracted from the central database, the system identifies the features of the product that need to be detected. This includes shape, size, surface imperfections, color, and structural features. The identified detection feature is set as a system target feature. To complete the detection process in order, the system sets the order of image acquisition for each target feature. The sequence is set based on the priority of detection and the requirements of flow optimization, ensuring that the detection of each feature can be performed under optimal conditions, so that the detection sequence of the target features comprises shape and size, surface defects, color and structural features. The main advantage of AOI systems in the target feature recognition and setup phase is their ability to be highly intelligent and automated. By scanning the product identification information on the production line and matching with the central database, the system can rapidly and accurately identify the type information of each product and extract the corresponding detection requirement. Accurate data matching and transmission avoid possible errors caused by manual identification, and greatly improve production efficiency. In addition, the system automatically sets the detection target characteristics and the proper image acquisition sequence according to the database instructions, and ensures the accuracy and the priority of various characteristics in the detection process. The full-automatic flow design ensures that the production line can perform high-efficiency quality control reliably in real time, and finally improves the qualification rate and the overall production efficiency of the product.

In one embodiment of the present invention, the S2 includes:

The working principle and effect of the technical scheme are that the system firstly identifies and obtains the actual size of each target feature, in particular the size in length. Among the plurality of target features, the feature of the smallest actual size is found as the adjustment criterion. The camera frame rate (F) is set according to the minimum physical size, ensuring that the camera can capture a complete image of the target feature. The frame rate is calculated from the pixel movement and the product movement speed (V) on the production line, and the condition that the inter-frame movement distance (D) is less than or equal to the actual minimum feature size must be satisfied. The detection accuracy that each target feature needs to achieve will affect the camera resolution that needs to be set. Each target feature may have a different sharpness criterion. According to the definition requirement of the current target feature, the system sets the resolution of the camera to acquire the best quality image. After the detection of one target feature is completed, the system dynamically switches the resolution of the camera to the setting required by the next target feature according to the preset image acquisition sequence and scheme. The advantage of AOI systems in terms of dynamic settings of camera frame rate and resolution is their flexibility and accuracy, optimized for different target features by adjusting camera parameters in real time. The system first recognizes the size and sharpness requirements of each feature, automatically sets the appropriate frame rate and resolution, and ensures that the camera is able to capture high quality images in its entirety. Not only ensures the integrity of details and the accuracy of detection, but also can adapt to the diversified detection requirements of different products. The camera setting is dynamically switched to ensure that the system keeps stable detection capability under a rapidly-changed production environment and unnecessary resource consumption is avoided, so that the overall production efficiency and the product quality are improved, and the quality control and the optimization flow in the modern manufacturing process are effectively supported.

In one embodiment of the present invention, the S3 includes:

The technical scheme has the working principle and the effect that the product type information obtained from the production line is used for inquiring the material data of the product. The system retrieves the surface texture characteristics of the product from the database, covering the reflective characteristics and surface smoothness of common materials including, but not limited to, plastics, metals, ceramics, and glass. And selecting a proper light source type according to the acquired surface material characteristics. The LED, the laser and the infrared light have different optical characteristics and are suitable for different surface materials. For example, LEDs are suitable for general use, lasers are suitable for applications where higher resolution is required, and infrared light is suitable for viewing or detecting subsurface features. The angle and intensity of illumination by the light source are adjusted based on the light reflection characteristics and the surface smoothness. By setting the incidence angle of the light, glare and interference caused by direct light are avoided. The light sources are arranged in an annular layout, so that uniform illumination is ensured, and shadow influence is reduced. This arrangement helps to reduce the specular reflection and increase the uniformity of the light distribution in the detection area. The AOI system has the advantages of customizing and flexibly adjusting the light source configuration according to different materials, and the system ensures that the light source type and the irradiation parameters can optimize the reflection characteristics of the matched materials by firstly acquiring the surface material information of the product. This process prevents degradation of image quality due to excessive or improper light reflection, enhancing light uniformity and feature definition in the detection area. By selecting a suitable light source, such as an LED, laser or infrared light, and adjusting the angle of incidence and intensity, the annular light source is able to provide uniform illumination from multiple directions, avoiding shadows and uneven illumination that may be caused by a single direction light source. Through annular arrangement, light can better disperse and incident on the detection surface, reduces the direct light reflection problem of high reflection face, reduces the glare that probably leads to improve the quality of image and testing result.

In one embodiment of the present invention, the S4 includes:

S42, obtaining a surface feature photo of a product, determining the type, size, number and depth of defects on the surface of the product according to the surface feature photo, determining whether the product is qualified according to the maximum allowable size, maximum allowable size and maximum allowable depth of the defects, judging that the product is qualified if the size, number and depth corresponding to one defect do not reach the maximum allowable size, number and depth corresponding to the defect, detecting the next target feature, judging that the product is unqualified if any one of the size, number and depth corresponding to the defect exceeds the maximum allowable size, number and depth corresponding to the defect, and classifying the product into a surface defect area;

The working principle and the effect of the technical scheme are that the image acquisition technology is adopted to acquire the photos of the shape and the size characteristics of the product. Contour information and dimensional values of the product are extracted from the photographs. The resulting profile information and dimensions are compared to tolerance ranges in predetermined templates and standard parameters. If the outline and the size are within the allowable range, the outline and the size are judged to be qualified in the dimension, if the outline and the size are not qualified, the outline and the size are marked to be unqualified, the outline and the size are marked to enter a shape and size unqualified area, a photo of the surface of the product is obtained to be analyzed in detail, the type, the size, the number and the depth of defects existing on the surface are determined, the detection results are compared with the allowable maximum size, the allowable maximum size and the allowable maximum size of the defects, the detection results are compared with the allowable maximum size and the allowable maximum size of the defects, if any dimension is within the allowable range, the outline and the size are marked to be unqualified and classified into the surface defect area, a photo of the color characteristic of the product is obtained to be detected, an actual color value of the product is obtained through the photo, a color difference formula is used, the difference between the actual color value and the standard color value is measured by L and the A and B, if the difference is within a specified range, if the difference is beyond the upper limit, the product is classified into the unqualified area due to the color disagreement, the image is aligned with the obtained image of the maximum size and the horizontal position deviation of the structure, if the deviation is within the allowable range, the deviation is indicated that the structure is qualified. By analyzing key characteristics of products one by one, the system can comprehensively identify potential defects and deviations and rapidly classify products which do not meet the standard, so that only qualified products can enter the next production link. The refined detection mechanism reduces human detection errors by high reliability, and improves the overall production efficiency. Meanwhile, the system ensures that the chromatic aberration, surface defects and structural deviation of the complex product are in a controllable range by automatically comparing standard parameters, and improves the accuracy and consistency of the quality assurance process.

In one embodiment of the invention, an AOI vision on-line high-speed detection system comprises:

In one embodiment of the present invention, the product information identification system includes:

In one embodiment of the present invention, the camera configuration system includes:

In one embodiment of the present invention, the light source setting system includes:

The camera acquires reflected light intensity irradiated on the surface of a product through a self-contained photometer, converts the reflected light intensity into an electric signal, and transmits the electric signal to the intelligent light source configuration system, the intelligent light source configuration system compares the reflected light intensity of the surface of the product with the maximum allowed reflected light intensity of the surface of the product according to the electric signal after receiving the electric signal, and then dynamically adjusts the intensity of incident light, and the adjustment mode is acquired through the following formula:

Wherein, Indicating the illumination intensity (watts per square meter) of the reflected light,The illumination intensity of the incident light is represented by R, the surface reflectivity of the product is represented by θ, the angle of the incident light is represented by s, the diffuse reflection coefficient is represented by s, and the value range of s is (0, 1)

When (when)When the minimum reflected light intensity allowed by the surface of the product is less than or equal to the minimum reflected light intensity, the reflected light is too weak, so that some details in the image are not clear enough due to insufficient light, the risk of erroneous judgment in the detection process is increased, and the intelligent light source configuration system enhances the intensity of the incident light;

When the minimum reflected light intensity allowed by the surface of the product is < At less than or equal to the maximum reflected light intensity allowed by the surface of the product, indicating that the reflected light intensity is right at this time;

When (when) When the maximum reflected light intensity allowed by the surface of the product is higher, the reflected light is too strong, the image is overexposed, and the intelligent light source configuration system weakens the intensity of the incident light.

In the formula for calculating the intensity of the reflected light, the formula can comprehensively represent the reflection characteristics of light on different types of surfaces by combining the specular reflection R and the diffuse reflection s. Specular reflection describes the situation where light is reflected at a particular angle, whereas diffuse reflection takes into account the characteristic of uniform scattering of light at the surface. The equation incorporates θ to describe the effect of the angle of incidence on the intensity of reflected light. Smaller angles of incidence (i.e., near parallel to the surface) increase the mirror effect, while larger angles of incidence (i.e., normal incidence) reduce this effect. The reflectivity R represents the reflective capability of the surface material, the reflectivity of different materials is different, but is between 0 and 1, the reflectivity of the metal material is highest and is 60% -90%, the reflectivity of the natural material is lowest and is 15% -30%, and the diffuse reflection factor s is introduced to quantify the scattering proportion of light in diffuse reflection, so that the formula effectively reflects the light scattering condition of various surfaces (such as rough or textured surfaces). By changing the intensity of incident light, the intensity of reflected light is adjusted according to the real-time detection requirement

In one embodiment of the present invention, the image detection and product classification system comprises:

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. An AOI vision on-line high-speed detection method, the method comprising:

2. The AOI vision on-line high-speed detection method according to claim 1, wherein S1 includes:

3. The AOI vision on-line high-speed detection method according to claim 1, wherein S2 includes:

S21 of acquiring an actual size of each target feature, the actual size representing a size of the target feature in length, and setting a frame rate of the camera according to a minimum actual size of the plurality of target features, and setting the frame rate of the camera according to the following formula, Wherein D represents the moving distance between frames, V represents the speed of the production line, namely the moving distance of a product in the production line in unit time, F represents the frame rate of a camera, the unit is frames per second, theta represents the included angle formed by the moving direction of the product and the optical axis direction of the camera, and alpha represents the visual angle of the camera, namely the angle range of a camera lens capable of capturing pictures;

4. The AOI vision on-line high-speed detection method according to claim 1, wherein S3 includes:

5. The AOI vision on-line high-speed detection method according to claim 1, wherein S4 includes:

6. An AOI vision on-line high-speed detection system, the system comprising:

7. The AOI visual on-line high-speed inspection system of claim 6, wherein the product information identification system comprises:

8. The AOI vision on-line high-speed detection system of claim 6, wherein the camera configuration system comprises:

9. The AOI vision on-line high-speed detection system of claim 6, wherein the light source setup system comprises:

10. The AOI vision on-line high-speed inspection system of claim 6, wherein the image inspection and product classification system comprises: