Visual positioning control system and method of industrial camera

Abstract

The invention relates to the technical field of industrial cameras, and discloses a visual positioning control system and method for an industrial camera, and the system carries out the calculation of an environment pollution index and an image definition change value through monitoring the internal environment of a working region and monitoring the definition of an image shot by the industrial camera. And calculating a lens pollution index by combining the environmental pollution index and the image definition change value, judging whether the current lens needs to be cleaned or not according to the lens pollution index, sending a cleaning signal to a cleaner to clean the lens under the condition that cleaning is needed, and cleaning the lens according to the change of the environmental factors and the image definition according to the change of the environmental factors and the image definition. According to the method, the probability of pre-judging lens pollution can be visually found, so that the lens pollution can be cleaned conveniently, the complexity of manual inspection is avoided, misjudgment and missed judgment are reduced, the working sensitivity of the industrial camera is improved, the problem of industrial camera lens pollution is effectively solved, and the working efficiency and the image quality of the industrial camera are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial cameras, and in particular to a visual positioning control system and method for an industrial camera. Background Art

[0002] The development of industrial cameras began in the 1970s. With the rapid development of computer technology, especially the maturity of image processing technology, industrial cameras have begun to be widely used in industrial automation and machine vision systems. Initially, industrial cameras were mainly used for simple image acquisition tasks, such as barcode scanning and product detection. With the advancement of technology, the functions of industrial cameras have been continuously enhanced, and the application fields have been continuously expanded. With the continuous advancement of image sensor technology and image processing algorithms, the performance of industrial cameras will continue to improve. In the future, industrial cameras will have higher resolution, faster frame rate and stronger anti-interference ability. Intelligence will become an important development direction of industrial cameras. In the future, industrial cameras will have functions such as automatic recognition, automatic classification, and automatic alarm to achieve more intelligent detection and management. With the continuous improvement of the level of industrial automation, the application scope of industrial cameras will be further expanded. In addition to the traditional manufacturing field, industrial cameras will play an increasingly important role in agriculture, medical care, security and other fields. In response to the needs of different industries and application scenarios, industrial cameras will gradually realize customized design, which will enable industrial cameras to better meet the needs of specific industries and scenarios and provide more accurate detection solutions. Industrial cameras will be integrated with other sensors, control systems, etc. to form a more complete industrial automation solution, which will make the entire automation system more powerful and more intelligent.

[0003] The visual positioning control system of industrial cameras plays a vital role in automated production lines. Its accuracy and reliability are directly related to the judgment of product quality. However, the effect of machine vision inspection is often significantly affected by environmental conditions, causing lens contamination. For example, contaminants such as stains, dust or fingerprints on the lens will scatter light and cause image blur. This blur not only reduces the clarity of the image, but may also cover up the detailed defects on the surface of the product, making it impossible for the inspection system to accurately identify it, thereby affecting the accuracy of the inspection results. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a visual positioning control system and method for an industrial camera, which can intuitively discover and predict the probability of lens contamination through changes in environmental factors and image clarity, so as to clean the lens contamination in time, and can dynamically and intuitively analyze the degree of lens contamination, thereby avoiding the tediousness of manual inspection, reducing misjudgments and missed judgments, increasing the sensitivity of the industrial camera, providing a new perspective for the evaluation of the degree of lens contamination, effectively solving the problem of lens contamination of industrial cameras, improving the working efficiency and image quality of industrial cameras, and making the detection results more accurate, etc.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a visual positioning control system for an industrial camera, comprising a monitoring module, an analysis module, an evaluation module and a cleaning module;

[0008] The monitoring module includes an environment monitoring unit and an image monitoring unit. The environment monitoring unit is used to monitor the environment inside the working area and form an environment data set by combining multiple environments. The image monitoring unit is used to monitor images taken by an industrial camera and form an image data set by combining multiple image sharpnesses. The monitoring module is connected to the analysis module through a network.

[0009] The analysis module calculates the environmental pollution index according to the environmental data set formed by the environmental monitoring unit, the analysis module calculates the image clarity change value according to the image data set formed by the image monitoring unit, the analysis module calculates the lens pollution index according to the environmental pollution index and the image clarity change value, and the analysis module is connected to the evaluation module network;

[0010] The evaluation module determines whether the current lens needs to be cleaned according to the lens contamination index, and sends a signal to the cleaning module if cleaning is required;

[0011] When receiving the cleaning signal, the cleaning module sends a cleaning signal to the cleaner to perform lens cleaning.

[0012] Preferably, the environmental data set includes a variety of environmental factors that may cause lens contamination, specifically dust concentration, chemical substance concentration and humidity.

[0013] Preferably, the expression of the environmental data set is:

[0014] HJjh=FCnd、HXnd、SDsj

[0015] Among them, HJjh represents the environmental data set, FCnd represents the dust concentration, HXnd represents the chemical substance concentration, SDsj represents the humidity, and the time period for collecting the dust concentration, chemical substance concentration and humidity is consistent with the specific time point.

[0016] Preferably, the image dataset is expressed as follows:

[0017] TXjh=TXsj1, TXsj2, TXsj3,···,TXsj n

[0018] Among them, TXjh represents the image dataset, TXsj1 represents the first image clarity data in the image dataset, and TXsj n Represents the nth image definition data in the image data set, and n indicates that there are a total of n image definition data in the image data set.

[0019] Preferably, the image clarity is calculated as follows:

[0020] The Tenengrad gradient method is adopted and the Sobel operator is used to calculate the horizontal and vertical gradients of the image taken by the industrial camera. In the same scene, the higher the gradient value, the clearer the image. The average gray value of the image processed by the Sobel operator is marked as the image clarity data. The larger the value, the clearer the image.

[0021] Preferably, the calculation formula of the environmental pollution index is:

[0022]

[0023] Among them, HJwr is the environmental pollution index, FCnd v is the current dust concentration, FCnd min represents the minimum impact value of dust concentration, ω1 is the corresponding weight of dust concentration, α is the index of dust concentration, Represents the ratio between the current dust concentration and the minimum impact value of dust concentration;

[0024] HkDJ v is the current chemical concentration, HXnd min represents the minimum impact value of chemical concentration, ω2 is the corresponding weight of chemical concentration, β is the index of chemical concentration, Represents the ratio between the current chemical concentration and the minimum impact value of the chemical concentration;

[0025] SJ v is the current humidity data, SDsj minrepresents the minimum influence value of humidity data, ω3 represents the corresponding weight of humidity data, and γ is the index of humidity data. Represents the ratio between the current humidity data and the minimum impact value of the humidity data;

[0026] Moreover, ω1+ω2+ω3=1.

[0027] Preferably, the calculation formula of the image definition change value is:

[0028]

[0029] Among them, HJqx represents the image clarity change value, TXsj i Represents the i-th image clarity data in the image dataset, Represents the sum of all image clarity data from i=1 to i=n, Represents the average image clarity in the image dataset, TXsj v Represents the clarity of the current image, TXsj v-1 Represents the clarity of the previous image of the current image. Represents the ratio of the current image clarity to the average image clarity. Represents the ratio of the clarity of the previous image to the average image clarity. The difference between the ratio of the current image clarity to the average image clarity and the ratio of the clarity of the image before the current image to the average image clarity is the image clarity change value.

[0030] Preferably, the calculation formula of the lens contamination index is:

[0031] JTwr=(θ1*HJwr)·(θ2*HJqx)

[0032] Among them, θ1 is the weight of the environmental pollution index, and θ2 is the weight of the image clarity change value.

[0033] Preferably, when the lens contamination index is higher than a conventional lens contamination index, it means that the current lens is contaminated and a cleaning signal needs to be sent to a cleaning module.

[0034] A visual positioning control method for an industrial camera comprises the following steps:

[0035] Step 1: Monitor the environment inside the work area;

[0036] Step 2: Monitor the clarity of images taken by industrial cameras;

[0037] Step 3: Calculate the environmental pollution index based on multiple data from environmental monitoring;

[0038] Step 4: Calculate the image clarity change value according to the clarity of multiple images taken by the industrial camera; Step 5: Analyze the lens contamination index according to the calculation result;

[0039] Step 6: Determine the contamination status of the lens based on the lens contamination index, and determine whether cleaning is required based on the contamination status.

[0040] Compared with the prior art, the present invention provides a visual positioning control system and method for an industrial camera, which has the following beneficial effects:

[0041] 1. The present invention can intuitively discover and predict the probability of lens contamination through changes in environmental factors and image clarity, so as to clean the lens contamination in time.

[0042] 2. The present invention combines the environmental pollution index and the image clarity change value to calculate the lens pollution index, and then compares the lens pollution index with the corresponding threshold value to determine whether the lens needs to be cleaned. It can dynamically and intuitively analyze the degree of lens pollution, avoiding the tediousness of manual inspection, while reducing misjudgments and missed judgments, increasing the sensitivity of industrial cameras, and providing a new perspective for the assessment of lens pollution levels. Through quantitative analysis, the judgment is more objective and accurate, while avoiding damage to the lens caused by unnecessary cleaning operations, effectively solving the problem of industrial camera lens pollution, and improving the working efficiency and image quality of industrial cameras. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0044] Figure 2 It is a schematic diagram of the process flow of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1-2 , a visual positioning control system for an industrial camera, comprising a monitoring module, an analysis module, an evaluation module and a cleaning module;

[0048] The monitoring module includes an environment monitoring unit and an image monitoring unit. The environment monitoring unit is used to monitor the environment inside the working area and form an environment data set by combining multiple environments. The image monitoring unit is used to monitor the images taken by the industrial camera and form an image data set by combining multiple image sharpness. The monitoring module is connected to the analysis module through a network.

[0049] The environmental data set includes various environmental factors that may cause lens contamination, specifically dust concentration, chemical concentration, and humidity;

[0050] The expression of the environment dataset is:

[0051] HJjh=FCnd、HXnd、SDsj

[0052] Among them, HJjh represents the environmental data set, FCnd represents the dust concentration, HXnd represents the chemical substance concentration, and SDsj represents the humidity. The time period for collecting dust concentration, chemical substance concentration, and humidity is consistent with the specific time point, which is convenient for data analysis.

[0053] Dust particles in the air will adhere to the surface of the lens, forming a dust film, which will cause image blur and reduced contrast. If not cleaned in time, dust will accumulate on the lens, which may cause scratches or wear on the lens surface over time. Certain chemicals such as acidic or alkaline gases will corrode the coating on the lens surface, affecting the optical performance of the lens. High concentrations of chemicals may also react chemically with the lens material, causing the lens material to deteriorate or discolor. In general, dust concentration, chemical concentration and humidity are important environmental factors for industrial camera lens contamination. By monitoring these data, the contamination of industrial camera lenses can be evaluated;

[0054] The image dataset is expressed as:

[0055] TXjh=TXsj1, TXsj2, TXsj3,···,TXsj n

[0056] Among them, TXjh represents the image dataset, TXsj1 represents the first image clarity data in the image dataset, and TXsj n Represents the nth image definition data in the image data set, and n indicates that there are n image definition data in the image data set;

[0057] By forming an image data set through multiple image clarity data, it is convenient to analyze the pictures taken by industrial cameras and grasp the changes in clarity in a timely manner;

[0058] Image clarity is calculated as:

[0059] The Tenengrad gradient method is used to calculate the horizontal and vertical gradients of the image taken by the industrial camera using the Sobel operator. The higher the gradient value in the same scene, the clearer the image. The average gray value of the image processed by the Sobel operator is marked as the image clarity data. The larger the value, the clearer the image.

[0060] The analysis module calculates the environmental pollution index according to the environmental data set formed by the environmental monitoring unit, the analysis module calculates the image clarity change value according to the image data set formed by the image monitoring unit, the analysis module calculates the lens pollution index according to the environmental pollution index and the image clarity change value, and the analysis module is connected to the evaluation module through a network;

[0061] The calculation formula of environmental pollution index is:

[0062]

[0063] Among them, HJwr is the environmental pollution index, FCnd v is the current dust concentration, FCnd min represents the minimum impact value of dust concentration, ω1 is the corresponding weight of dust concentration, and α is the dust concentration index, which is used to adjust the contribution of dust concentration to the environmental pollution index. Represents the ratio between the current dust concentration and the minimum impact value of the dust concentration, and is used to evaluate the current dust concentration exceeding the standard value;

[0064] HkDJ v is the current chemical concentration, HXnd min represents the minimum impact value of chemical concentration, ω2 is the corresponding weight of chemical concentration, and β is the index of chemical concentration, which is used to adjust the contribution of chemical concentration to the environmental pollution index. Represents the ratio between the current chemical concentration and the minimum impact value of the chemical concentration, and is used to evaluate the excessive value of the chemical concentration;

[0065] SJ v is the current humidity data, SDsj min represents the minimum impact value of humidity data, ω3 represents the corresponding weight of humidity data, and γ is the index of humidity data, which is used to adjust the contribution of humidity data to the environmental pollution index. Represents the ratio between the current humidity data and the minimum impact value of the humidity data, and is used to evaluate the excess value of the humidity data;

[0066] And, ω1+ω2+ω3=1;

[0067] The environmental pollution index is calculated by combining multiple values ​​of dust concentration, chemical concentration and humidity. When the environmental pollution index is large, the degree and probability of lens pollution will increase simultaneously.

[0068] The calculation formula of the image clarity change value is:

[0069]

[0070] Among them, HJqx represents the image clarity change value, TXsj i Represents the i-th image clarity data in the image dataset, Represents the sum of all image clarity data from i=1 to i=n, Represents the average image clarity in the image dataset, TXsj v Represents the clarity of the current image, TXsj v-1 Represents the clarity of the previous image of the current image. Represents the ratio of the current image clarity to the average image clarity. Represents the ratio of the clarity of the previous image to the average image clarity. The difference between the ratio of the current image clarity to the average image clarity and the ratio of the previous image clarity to the average image clarity is the image clarity change value;

[0071] When the lens of an industrial camera is contaminated by environmental factors such as dust, chemicals or humidity, the image clarity will decrease. The contaminants will adhere to the surface of the lens, forming a layer of obstacles that hinder the normal propagation of light, making the image blurry, reducing contrast, and losing details. In addition to monitoring the environment, the clarity of the picture can also clearly determine the contamination of the lens.

[0072] The calculation formula of lens contamination index is:

[0073] JTwr=(θ1*HJwr)·(θ2*HJqx)

[0074] Among them, θ1 is the weight of the environmental pollution index, and θ2 is the weight of the image clarity change value;

[0075] The evaluation module determines whether the current lens needs to be cleaned based on the lens contamination index:

[0076] When the lens contamination index is higher than the normal lens contamination index, it means that the current lens is contaminated and a cleaning signal needs to be sent to the cleaning module;

[0077] The lens pollution index is calculated by combining the environmental pollution index and the image clarity change value, and then the lens pollution index is compared with the corresponding threshold to determine whether the lens needs to be cleaned. The degree of lens pollution can be analyzed dynamically and intuitively, avoiding the tediousness of manual inspection, reducing misjudgment and missed judgment, increasing the sensitivity of industrial camera work, and providing a new perspective for the evaluation of lens pollution degree. Through quantitative analysis, the judgment is more objective and accurate, and the damage to the lens caused by unnecessary cleaning operations is avoided, effectively solving the problem of industrial camera lens pollution and improving the working efficiency and image quality of industrial cameras.

[0078] When the cleaning module receives the cleaning signal, it sends a cleaning signal to the cleaner to clean the lens;

[0079] A visual positioning control method for an industrial camera comprises the following steps:

[0080] Step 1: Monitor the environment inside the work area;

[0081] Step 2: Monitor the clarity of images taken by industrial cameras;

[0082] Step 3: Calculate the environmental pollution index based on multiple data from environmental monitoring;

[0083] Step 4: Calculate the image clarity change value according to the clarity of multiple images taken by the industrial camera; Step 5: Analyze the lens contamination index according to the calculation result;

[0084] Step 6: Determine the contamination status of the lens based on the lens contamination index, and determine whether cleaning is required based on the contamination status.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A visual positioning control system for an industrial camera, characterized in that: It includes monitoring module, analysis module, evaluation module and cleaning module; The monitoring module includes an environment monitoring unit and an image monitoring unit. The environment monitoring unit is used to monitor the environment inside the working area and form an environment data set by combining multiple environments. The image monitoring unit is used to monitor images taken by an industrial camera and form an image data set by combining multiple image sharpnesses. The monitoring module is connected to the analysis module through a network. The analysis module calculates the environmental pollution index according to the environmental data set formed by the environmental monitoring unit, the analysis module calculates the image clarity change value according to the image data set formed by the image monitoring unit, the analysis module calculates the lens pollution index according to the environmental pollution index and the image clarity change value, and the analysis module is connected to the evaluation module network; The evaluation module determines whether the current lens needs to be cleaned according to the lens contamination index, and sends a signal to the cleaning module if cleaning is required; When receiving the cleaning signal, the cleaning module sends a cleaning signal to the cleaner to perform lens cleaning.

2. The visual positioning control system of an industrial camera according to claim 1, characterized in that: The environmental data set includes various environmental factors that may cause lens contamination, specifically dust concentration, chemical substance concentration and humidity.

3. The visual positioning control system of an industrial camera according to claim 2, characterized in that: The expression of the environmental data set is: HJjh=FCnd、HXnd、SDsj Among them, HJjh represents the environmental data set, FCnd represents the dust concentration, HXnd represents the chemical substance concentration, SDsj represents the humidity, and the time period for collecting the dust concentration, chemical substance concentration and humidity is consistent with the specific time point.

4. The visual positioning control system of an industrial camera according to claim 1, characterized in that: The image dataset is expressed as follows: TXjh = TXsj1, TXsj2, TXsj3,..., TXsj n Among them, TXjh represents the image dataset, TXsj1 represents the first image clarity data in the image dataset, and TXsj n Represents the nth image definition data in the image data set, and n indicates that there are a total of n image definition data in the image data set.

5. The visual positioning control system of an industrial camera according to claim 4, characterized in that: The image clarity is calculated as follows: The Tenengrad gradient method is adopted and the Sobel operator is used to calculate the horizontal and vertical gradients of the image taken by the industrial camera. In the same scene, the higher the gradient value, the clearer the image. The average gray value of the image processed by the Sobel operator is marked as the image clarity data. The larger the value, the clearer the image.

6. The visual positioning control system of an industrial camera according to claim 3, characterized in that: The calculation formula of the environmental pollution index is: Among them, HJwr is the environmental pollution index, FCnd v is the current dust concentration, FCnd min represents the minimum impact value of dust concentration, ω1 is the corresponding weight of dust concentration, α is the index of dust concentration, Represents the ratio between the current dust concentration and the minimum impact value of dust concentration; HkDJ v is the current chemical concentration, HXnd min represents the minimum impact value of chemical concentration, ω2 is the corresponding weight of chemical concentration, β is the index of chemical concentration, Represents the ratio between the current chemical concentration and the minimum impact value of the chemical concentration; SJ v is the current humidity data, SDsj min represents the minimum influence value of humidity data, ω3 represents the corresponding weight of humidity data, and γ is the index of humidity data. Represents the ratio between the current humidity data and the minimum impact value of the humidity data; Moreover, ω1+ω2+ω3=1.

7. The visual positioning control system of an industrial camera according to claim 4, characterized in that: The calculation formula of the image definition change value is: Among them, HJqx represents the image clarity change value, TXsj i Represents the i-th image clarity data in the image dataset, Represents the sum of all image clarity data from i=1 to i=n, Represents the average image clarity in the image dataset, TXsj v Represents the clarity of the current image, TXsj v-1 Represents the clarity of the previous image of the current image. Represents the ratio of the current image clarity to the average image clarity. Represents the ratio of the clarity of the previous image to the average image clarity. The difference between the ratio of the current image clarity to the average image clarity and the ratio of the clarity of the image before the current image to the average image clarity is the image clarity change value.

8. The visual positioning control system of an industrial camera according to claim 7, characterized in that: The calculation formula of the lens contamination index is: JTwr=(θ1*HJwr)·(θ2*HJqx) Among them, θ1 is the weight of the environmental pollution index, and θ2 is the weight of the image clarity change value.

9. The visual positioning control system of an industrial camera according to claim 8, characterized in that: When the lens contamination index is higher than the normal lens contamination index, it means that the current lens is contaminated and a cleaning signal needs to be sent to the cleaning module.

10. A visual positioning control method for an industrial camera, based on a visual positioning control system for an industrial camera according to claims 1-9, characterized in that: The following steps are involved: Step 1: Monitor the environment inside the work area; Step 2: Monitor the clarity of images taken by industrial cameras; Step 3: Calculate the environmental pollution index based on multiple data from environmental monitoring; Step 4: Calculate the image clarity change value according to the clarity of multiple images taken by the industrial camera; Step 5: Analyze the lens contamination index according to the calculation results; Step 6: Determine the contamination status of the lens based on the lens contamination index, and determine whether cleaning is required based on the contamination status.

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