CN115442535B - Method for adaptively adjusting exposure of industrial camera - Google Patents
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Abstract
The invention discloses a method for adaptively adjusting exposure of an industrial camera, which aims to solve the problems that the existing method is complex in operation, slow in adjusting speed and dependent on an empirical function of exposure value, and can not completely solve the problems of efficiency and adaptability. The method comprises the following steps: calculating an area average weighted gray value, calculating a calculated exposure value based on feedback adjustment, evaluating feedback adjustment parameters, and adaptively adjusting the feedback adjustment parameters. According to the invention, the area average weighted gray level of the designed area is calculated to serve as a standard for evaluating the environment brightness to evaluate the environment where the current industrial camera is located, so that the operation efficiency is improved on the premise of reducing undersampling errors; the feedback adjustment method is introduced into the dynamic adjustment process, so that the exposure is effectively regulated and controlled in the process of dynamic environment change, and the imaging quality is ensured; the feedback adjustment parameters are controlled by utilizing organic combinations of different functions, so that errors and instability caused by fixed parameters and empirical adjustment are reduced.
Description
Technical Field
The invention relates to the technical field of industrial camera application, in particular to a method for adaptively adjusting exposure of an industrial camera.
Background
Industrial cameras are an important component of the intelligent industry due to their superior performance, but imaging of industrial cameras is often affected by the environment in which they are located to have different effects. For example, high precision laser machining often requires dynamic observation of the machined workpiece in order to better adjust the machining parameters and evaluate the quality of the workpiece machining. However, due to the high brightness and instability of the industrial environments such as laser processing, industrial cameras often cannot image normally in such environments. How to effectively image in an environment with high brightness and mutation is still a problem to be solved in the application field of the current industrial camera technology.
For ease of deployment, automatic exposure adjustment methods inherent in industrial cameras are often used. The self automatic exposure method of the industrial camera comprises the following steps: the signals acquired by the photosensitive elements are processed through hardware design, so that a good imaging effect is achieved, and the adaptability of the imaging device to the external environment is enhanced. The method can solve the imaging problem to a certain extent, but is limited by the limitation of the related hardware, and once the ambient brightness exceeds the adjusting range, the adjusting capability is lost, so that the method cannot be fully suitable for the high-brightness environment.
In addition to the above-mentioned automatic exposure method for industrial camera hardware, there are also methods of adaptive exposure based on image histogram and adaptive exposure based on image information entropy. The adaptive exposure method based on the image histogram takes the brightness histogram of the image as the standard of the quantized ambient brightness, and then adjusts the exposure in a stepping manner. The self-adaptive exposure method based on the image information entropy is to judge the environment by calculating the information entropy of the image, quantifying the information contained in the image, and adjusting the exposure. Compared with the inherent hardware automatic exposure method of the industrial camera, the two methods have larger adjustment range and adaptability, but the problems of efficiency and adaptability cannot be completely solved because the operation is complex, the adjustment speed is slow, the exposure adjustment is dependent on the exposure value empirical function.
The self-adaptive exposure method based on the convolutional neural network is widely applied as a processing mode of a relatively front edge. However, training by the convolutional neural network requires a large amount of sample data, and requires wider coverage of the sample data; meanwhile, the deployment condition requirement is high, and the problems cannot be perfectly solved.
For this reason, a new method is urgently needed to solve the above-mentioned problems.
Disclosure of Invention
The invention aims at: in order to solve the problems, an industrial camera exposure self-adaptive adjusting method is provided.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for adaptive adjustment of exposure of an industrial camera, comprising the steps of:
(1) Calculating area average weighted gray value
According to the exposure range of the industrial camera, a central area a, a peripheral area b, a peripheral area c, a peripheral area d and a peripheral area e are defined, wherein the central area a, the peripheral area b, the peripheral area c, the peripheral area d and the peripheral area e are square; according to the imaging principle, a central area a is set as a main-stage area, a peripheral area b, c, d, e is set as a secondary area, the side length ratio of the main-stage area to the secondary area is 4:1, the area ratio of the main-stage area to the secondary area is 16:1, and the central area a is set as: surrounding area b: surrounding area c: surrounding area d: the weight of the surrounding area e=6:1:1:1:1, and the area average weighted gray value of the exposure range is obtained through calculation;
(2) Performing exposure value feedback adjustment operation by using the area average weighted gray value obtained in the step (1) to obtain a current calculated exposure value;
(3) Taking the difference value between the current exposure value and the set ideal exposure value as a standard reference variable for evaluating the current feedback adjustment effect, and carrying out normalization processing to obtain normalization parameters;
(4) Taking the normalized parameter obtained in the step (3) as a standard reference variable for regional threshold parameter adjustment, optimizing the internal parameter in the feedback adjustment operation in the step (2), and taking the internal parameter as a parameter used in the next feedback adjustment operation;
(5) The steps (2) - (4) are performed in a reciprocating sequence until the normalized parameters in the step (3) are located in the dead zone region, namely, the parameter adaptation is considered to be completed, and the parameter change is stopped.
The areas of the surrounding areas b, c and d are the same as the area of the surrounding area e.
The area average weighted gray value p of the exposure range z The calculation formula of (2) is as follows:
in the formula (1), p z For the area average gray weighting value of the exposure range E a For the total gray value of the central region a, E b For the total gray value of the surrounding area b, E c For the total gray value of the surrounding area c, E d For the total gray value of the surrounding area d, E e For the total gray value of the surrounding area e, S a A pixel area of the central region a, S b Mu, the sum of the pixel areas of the surrounding area b, c, d, e 1 Is the weight value of the primary region, mu 2 Is the weight value of the secondary region.
In the present application, the weight value μ of the primary region 1 A weight value μ of 0.6 for the secondary region 2 A primary region pixel area and a secondary region pixel area S of 0.1 a :S b ＝16:1。
The calculation formula of the exposure value feedback adjustment operation in the step (2) is as follows:
e(k)＝p z -V(2)；
in the formula (2), e (k) is the error value in the current iteration, p z The average weighted gray value of the area obtained in the step (1) is V, which is an ideal exposure value set for human, and the range is 100-800; in the formula (3), k is the current iteration number, u (k) is the calculated exposure value,
to accumulate the integrated error sum, e (k-1) is the error value of the previous iteration, where the error value is 0 at the first iteration, p is the proportional phase parameter, I is the integral phase parameter, and D is the differential phase parameter.
In the step (3), the calculation formula of the normalization process is as follows:
in the formula (3), θ is a normalized coefficient, and e (k) is an error value of the current iteration calculated in the formula (2).
In the step (4), the optimization of the internal parameters in the feedback adjustment operation is performed by the following setting strategies:
(1) if the parameter P is the adjustment area, the parameter control technology
The following formula is shown: />
Wherein θ is the normalized coefficient described in step (3), P is the proportional phase parameter in formula (2), and P' is the proportional parameter update value; at this time, the parameters D and I in the feedback adjustment are both 0;
(2) if the parameter D is the adjustment area, the parameter control technology
The following formula is shown:
wherein θ is the normalized coefficient described in step (3), D is the differential stage parameter in equation (2), and D' is the proportional parameter update value. At this time, the parameter P in the feedback adjustment is the aforementioned value, and the parameter I is 0;
(3) if the parameter I is the adjustment area, the parameter I is e -15 Other parameters are unchanged；
(4) If the dead zone is the dead zone area, stopping feedback adjustment and parameter updating; outside the dead zone area, the calculated parameters are used as the parameters for the next feedback adjustment to participate in adjustment.
In the method, aiming at the problem that the environment brightness exceeds the automatic exposure limit adjusting range, the exposure value is controlled by using a feedback adjusting closed-loop method, so that the adaptability to the brightness environment and the adjusting application range are effectively expanded; aiming at the problem that the standard operation of the quantized ambient brightness is too complex and affects the operation speed, a method of combining regional sampling and weighted calculation is adopted, and the regional weighted gray value is used as a quantization standard, so that the calculation amount is reduced, the operation efficiency is improved, and meanwhile, the calculation loss and the calculation error caused by downsampling are reduced; aiming at the problem that the feedback adjustment parameters are fixed and cannot adapt to the environment, the feedback adjustment parameters are controlled by using a quantization parameter setting and adjustment parameter system, so that the effect of dynamic control is achieved, and the adaptability to the environment dynamic is improved.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1) The method of the invention ensures that the industrial camera can get rid of the limitation of the exposure range of the inherent automatic exposure of the camera in the high-brightness environment, and solves the problems of imaging blurring, bright spots and the like of the camera in the high-brightness environment;
2) In the method, the parameter adjustment process is performed by utilizing the function characteristics, so that the complexity of manually adjusting the parameters and the error of empirical adjustment are reduced, and the adjustment efficiency is improved; meanwhile, the parameter setting improves the adaptability of the method to the environment;
3) The method and the device have the advantages that the weighted sampling of the specific area is used as the reference value, so that the calculated amount is reduced to a certain extent, the operation speed is improved, and the follow-up adjustment work is completed more efficiently.
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The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a graph comparing test results.
In fig. 1, a is an imaging image under normal light, b is a transitional exposure image, and c is an imaging image processed by the algorithm.
FIG. 2 is a schematic diagram of a calculated region selected by calculating a region average weighted gray value;
in fig. 2, the central region a is a high-weight primary region, and the peripheral regions b, c, d, and e are low-weight secondary regions.
FIG. 3 is a schematic block diagram of a calculated exposure value based on feedback adjustment.
In fig. 3, 1 corresponds to an input value, 2 corresponds to a proportional calculation, 3 corresponds to an integral calculation, 4 corresponds to a differential calculation, 5 corresponds to an error calculation, and 6 corresponds to an output value.
Fig. 4 is a schematic diagram of a zoned threshold parameter control.
Detailed Description
All of the features disclosed in this specification, or all of the steps in a method or process disclosed, may be combined in any combination, except for mutually exclusive features and/or steps.
Any feature disclosed in this specification may be replaced by alternative features serving the same or equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Example 1
As shown in fig. 1, the example uses an incandescent lamp as a target for testing, and the target fixed focus is the filament of the incandescent lamp and images the filament, and the imaging effects are shown in (a), (b) and (c). FIG. 1 (a) is a graph showing the effect of imaging under normal ambient light without turning on an incandescent lamp, and it can be observed that imaging is clear when the incandescent lamp is not turned on; the effect of imaging after the incandescent lamp is turned on, which is shown in fig. 1 (b), is affected by the bright light, and only the appearance of the incandescent lamp can be observed, but the filament of the incandescent lamp cannot be observed; after the algorithm is used, as shown in fig. 1 (c), the filament of the incandescent lamp can be effectively observed, and overexposure caused by the highlighting of the incandescent lamp is effectively restrained.
As shown in fig. 2, the central region a and the peripheral region b, the peripheral region c, the peripheral region d, and the peripheral region e are defined as sampling regions. The central area a, the peripheral area b, the peripheral area c, the peripheral area d and the peripheral area e are square in shape.
According to an imaging principle, setting a central area a as a main-stage area, and setting a peripheral area b, a peripheral area c, a peripheral area d and a peripheral area e as secondary areas respectively, wherein the side length ratio of the main-stage area to the secondary areas is 4:1, and the area ratio of the main-stage area to the secondary areas is 16:1; meanwhile, the weight a, b and d are set, e=6:1:1:1, and the area average weighted gray value of the exposure range is obtained through calculation. The area average weighted gray value of the exposure range is adopted
And (5) calculating to obtain the product.
As shown in the feedback adjustment schematic block diagram of fig. 3, 1 in fig. 3 is a set ideal exposure value; the position feedback adjustment is divided into three phases of proportion, integral and differential, namely 2, 3 and 4 in figure 3, and the calculation results of the three phases are accumulated, namely the formula (2)
Wherein 2 in the graph is proportional operation, namely Pe (k), and the response speed is increased through operation; in the figure 3 is the integral operation, i.e. +.>
Eliminating steady state errors by continuous integration; in the figure 4 is a differential operation, i.e. D [ e (k) -e (k-1)]The trend of the error is predicted through differential operation, the oscillation caused by overshoot is reduced, then error calculation is carried out through 5, the error value of the operation is obtained, namely e (k), and the current calculated exposure value of the camera is output through 6, namely u (k). The image of the current frame is calculated to obtain p through regional average weighted gray scale z And (3) obtaining the error of the current frame by making a difference with the set ideal exposure value, obtaining the current calculated exposure value through the process of 1-6 in fig. 3, and controlling the camera to adjust the exposure.
As shown in fig. 4, the zone settings shown in the figure are based on the characteristics of the feedback adjustment and the adjustment experience. The curve shown in the figure is the image of equation (3). FIG. 4 shows error value E and normalizationAnd (3) transforming the relation between the parameters theta, wherein E represents the error value E (k) calculated in the formula (2). The region P shown in the figure is a proportional parameter adjusting region, the region D is a parameter adjusting region, the region D is an integral parameter adjusting region, and the region I is a parameter I in the formula (3). The dead zone shown in the figure is a dead zone, i.e., the "perfect exposure" at which the parameters in the dead zone are assumed to be present. |E shown in the figure 1 |、|E 2 |、|E 3 |、|E 4 I is the threshold value of the above adjustment region, in which i E is set in order to improve the speed and accuracy of adjustment 3 |＝2|E 2 |，|E 4 |＝2|E 3 |，|E 1 |∈[0.05,0.5]. The error value e (k) is used for obtaining a normalized parameter theta through a formula (4). And after the normalized parameter theta falls into the area, the different parameter adjustment strategies of the formulas (5) - (8) are completed to update the optimized feedback control parameters P, I, D.
At this time, the adjustment of one frame of image is completed, then the operation of continuous frames is repeatedly completed, and the camera is continuously controlled to reach the normal exposure level.
The implementation of the invention is different from the current automatic exposure method, the environment where the current industrial camera is positioned is evaluated by calculating the area average weighted gray scale of the design area as the standard for evaluating the environment brightness, and the operation efficiency is improved on the premise of ensuring the reduction of undersampling errors; the feedback adjustment method is introduced into the dynamic adjustment process, so that the exposure is effectively regulated and controlled in the process of dynamic environment change, and the imaging quality is ensured; the feedback adjustment parameters are controlled by utilizing the organic combination of different functions, so that errors and instability caused by fixed parameters and empirical adjustment are reduced, and the adaptability of the method to the environment is ensured.
The invention is not limited to the specific embodiments described above. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification, as well as to any novel one, or any novel combination, of the steps of the method or process disclosed.
Claims (5)
1. A method for adaptive adjustment of exposure of an industrial camera, comprising the steps of:
(1) Calculating area average weighted gray value
According to the exposure range of the industrial camera, a central area a, a peripheral area b, a peripheral area c, a peripheral area d and a peripheral area e are defined, wherein the central area a, the peripheral area b, the peripheral area c, the peripheral area d and the peripheral area e are square; according to the imaging principle, a central area a is set as a main-stage area, a peripheral area b, c, d, e is set as a secondary area, the side length ratio of the main-stage area to the secondary area is 4:1, the area ratio of the main-stage area to the secondary area is 16:1, and the central area a is set as: surrounding area b: surrounding area c: surrounding area d: the weight of the surrounding area e=6:1:1:1:1, and the area average weighted gray value of the exposure range is obtained through calculation;
(2) Performing exposure value feedback adjustment operation by using the area average weighted gray value obtained in the step (1) to obtain a current calculated exposure value;
(3) Taking the difference value between the current exposure value and the set ideal exposure value as a standard reference variable for evaluating the current feedback adjustment effect, and carrying out normalization processing to obtain normalization parameters;
(4) Taking the normalized parameter obtained in the step (3) as a standard reference variable for regional threshold parameter adjustment, optimizing the internal parameter in the feedback adjustment operation in the step (2), and taking the internal parameter as a parameter used in the next feedback adjustment operation;
(5) The steps (1) - (4) are performed in a reciprocating sequence until the normalized parameters in the step (3) are in the dead zone range, namely, the parameter self-adaption is considered to be completed, and the parameter change is stopped;
the calculation formula of the exposure value feedback adjustment operation in the step (2) is as follows:
in the formula (2), the amino acid sequence of the compound,
for the error value in the current iteration, +.>
For the area average weighted gray value obtained in step (1),Vthe ideal exposure value is artificially set in the range of 100-800; in the formula (3), the amino acid sequence of the compound,kfor the current iteration number>
For calculating the exposure value +.>
To accumulate the sum of the integral errors +.>
For the error value of the previous iteration, the error value is 0, ++in the first iteration>
For proportional phase parameter, ++>
For integrating phase parameters +.>
Is a differential stage parameter;
in the step (4), the optimization of the internal parameters in the feedback adjustment operation is performed by the following setting strategies:
(1) if it is a parameterPAdjusting the area, then parameter control technique
The following formula is shown:
wherein ,
for the normalization coefficient described in step (3), +.>
Is the proportional phase parameter in formula (2), +.>
Updating values for the scale parameters; at this time, the parameter in feedback regulation +.>
Parameter->
Are all 0;
(2) if it is a parameter
Adjusting area, parameter control technique->
The following formula is shown: />
wherein ,
for the normalization coefficient described in step (3), +.>
Is the differential phase parameter in equation (2), +.>
Updating values for the scale parameters; at this time, the parameters in the feedback adjustmentPUpdating value of the proportional parameter obtained for the last feedback adjustment +.>
Parameter->
Is 0;
(3) if it is a parameter
Regulation area, then parameter->
Is->
Other parameters are unchanged;
(4) if the dead zone is the dead zone area, stopping feedback adjustment and parameter updating; outside the dead zone area, the calculated parameters are used as the parameters for the next feedback adjustment to participate in adjustment.
2. The method for adaptive adjustment of exposure to an industrial camera according to claim 1, wherein the surrounding areas b, c, d are respectively the same as the surrounding area e.
3. The method for adaptive adjustment of exposure of an industrial camera according to claim 1 or 2, characterized in that the area average weighted gray value of the exposure range
The calculation formula of (2) is as follows:
in the formula (1), the components are as follows,
for the area average gray scale weighting value of the exposure range, is>
For the total gray value of the central region a +.>
For the total gray value of the surrounding area b +.>
For the total gray value of the surrounding area c +.>
For the total gray value of the surrounding area d +.>
For the total gray value of the surrounding area e +.>
Is the pixel area of the central area a +.>
Is the sum of the pixel areas of the surrounding area b, c, d, e, < >>
Weight value of main level region, +.>
Is the weight value of the secondary region.
4. The industrial phase according to claim 3The method for self-adaptive adjustment of the exposure of the machine is characterized in that the weight value of the main level area
Weight value of sub-area 0.6 +.>
Is 0.1, the pixel area of the primary region and the pixel area of the secondary region are +.>
=16:1。
5. The method for adaptive adjustment of exposure of industrial camera according to claim 1, wherein in the step (3), the calculation formula of the normalization process is as follows:
in the formula (3), the amino acid sequence of the compound,
for normalizing the coefficient, +.>
The error value for the current iteration calculated for equation (2). />
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