CN116320387B - Camera module detection system and detection method - Google Patents
Camera module detection system and detection method Download PDFInfo
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Abstract
The application discloses a camera module detection system and a detection method, which relate to the technical field of camera module detection, and are characterized in that preliminary detection is carried out when a camera module is detected, a module detection data set is established, an image quality coefficient Txs and an imaging performance coefficient Cxs are respectively generated, when at least one of the module detection data sets is higher than a corresponding threshold value, abnormal data are screened out, and when the quantity of the abnormal data exceeds the threshold value, detection schemes corresponding to the abnormal data are collected, and a detection scheme library is summarized and established; and matching a corresponding detection scheme from the detection scheme library, simulating the detection scheme by combining a module digital twin model, judging whether the detection scheme is feasible, outputting the feasible detection scheme as a secondary detection scheme if the detection scheme is feasible, and sending out early warning if the detection scheme is not feasible. Under the condition that the environmental factors of the detection area are considered, the effect of rechecking the preliminary detection result is achieved, and the reliability of the detection result is guaranteed.
Description
Technical Field
The application relates to the technical field of camera module detection, in particular to a camera module detection system and a detection method.
Background
A camera module is a modular device comprising components such as a camera, an image sensor, a lens, a control circuit, an interface, etc., and is commonly used in various consumer electronic devices such as smart phones, tablet computers, notebook computers, digital cameras, etc., and applications such as machine vision, monitoring, etc. in the industrial field.
It is capable of converting an optical signal into a digital signal and transmitting it to a processor through a control circuit, while a lens is used to focus the light, typically with an adjustable focal length and aperture size. The interface typically employs a standard interface, such as MIPI or USB, to facilitate connection to other devices.
After the preliminary production is accomplished to the camera module, in order to ensure product quality, need detect the camera module to screening out the defective products wherein, but the camera module is receiving continuous illumination, and when the temperature rise's in the detection zone condition of place, the performance and the image quality of camera module can suffer certain influence, for example, when light is stronger, the image quality of camera module can be relatively poor, this detection result reliability that just leads to current detecting system output is not enough.
Therefore, the application provides a camera module detection system and a detection method.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a camera module detection system and a detection method, which respectively generate an image quality coefficient Txs and an imaging performance coefficient Cxs by establishing a module detection data set, screen abnormal data when at least one of the image quality coefficients is higher than a corresponding threshold value, collect detection schemes corresponding to the abnormal data when the quantity of the abnormal data exceeds the threshold value, and summarize and establish a detection scheme library; and matching a corresponding detection scheme from the detection scheme library, simulating the detection scheme by combining a module digital twin model, judging whether the detection scheme is feasible, outputting the feasible detection scheme as a secondary detection scheme if the detection scheme is feasible, and sending out early warning if the detection scheme is not feasible. Under the condition that the environmental factors of the detection area are considered, the effect of rechecking the primary detection result is realized, the reliability of the detection result is ensured, and the problems in the background technology are solved.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
the camera module detection system comprises a quality detection unit, a first processing unit, a control unit, an adjusting unit, a collecting unit, a second processing unit and a third processing unit, wherein,
when the camera module is detected, the quality detection unit detects the environmental conditions in the detection area, and when the temperature in the detection area is increased due to the irradiation of light, the imaging quality and the imaging performance of the camera module are respectively detected preliminarily, a plurality of sub-data obtained by detection are summarized, and a module detection data set is established; the module detection data set is sent to a first processing unit, an image quality coefficient Txs and an imaging performance coefficient Cxs are respectively generated, and when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value, abnormal data in sub-data of the module detection data set are obtained through analysis; when the quantity of the abnormal data exceeds a threshold value, a control command is formed by the control unit, so that the adjusting unit adjusts the illumination and temperature conditions in the detection area, the collecting unit collects detection schemes corresponding to the abnormal data, and a detection scheme library is summarized and established;
the abnormal data and the degree of abnormality are used as abnormal characteristics, the abnormal characteristics and a detection scheme library are sent to a second processing unit, and a detection scheme corresponding to the camera module is matched from the detection scheme library according to the abnormal characteristics by the trained matching model; according to the specification parameters of the camera module and the detection process of the camera module, after training and testing, a module digital twin model is built, a secondary detection scheme is sent to a third processing unit, the module digital twin model is combined, the detection scheme is simulated under the current illumination and temperature conditions of a detection area, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as the secondary detection scheme, and if not, early warning is sent.
Further, the quality detection unit comprises an environment detection module, an imaging detection module and a performance detection module, wherein when the camera module is unfolded and detected, the environment detection module detects environment data in a detection area;
after the illumination intensity Gq and the indoor temperature St in the detection area are obtained, dimensionless processing is performed to generate a detection condition coefficient Jts in the detection area: the generation mode is according to the following formula:
wherein ,0≤F1 ≤1，0≤F 2 Not less than 1, and not less than 0.80% of F 1 +F 2 Less than or equal to 1.79, the specific value of which is adjusted and set by a user, C 1 Is a constant correction coefficient.
Further, when the camera module images, the performance detection module detects the imaging performance of the camera module, at least the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw in the imaging process are obtained, and the detection results are summarized; after the camera module images, the imaging detection module detects the quality of the acquired image, at least acquires the picture definition Hq, the color saturation Sc and the noise number Zd, and gathers the detection result; and summarizing the detection result obtained by the preliminary detection as sub-data, and establishing a module detection data set.
Further, the first processing unit includes an evaluation module, a judgment module, a prediction module and an analysis module, wherein a module detection data set obtained by preliminary detection is sent to the evaluation module to generate an image quality coefficient Txs and an imaging performance coefficient Cxs respectively; the imaging performance coefficient Cxs is generated as follows: the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw are sent to an evaluation module, and after dimensionless processing, the imaging performance coefficient Cxs is generated according to the following formula:
wherein alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha+beta is more than or equal to 1, alpha and beta are weights, the specific values of the weights are adjusted and set by a user, and C 2 Is a constant correction coefficient;
the image quality coefficient Txs is generated as follows: the picture definition Hq, the color saturation Sc and the noise number Zd are sent to an evaluation module, and after dimensionless processing, an image quality coefficient Txs is generated according to the following formula:
wherein, gamma and theta are parameters of changeable constants, gamma is more than or equal to 0.43 and less than or equal to 1.26,0.82 and theta is more than or equal to 1.98, and the specific values are adjusted and set by a user.
Further, the obtained imaging performance coefficient Cxs and the image quality coefficient Txs are sent to a judging module, and when the judging module judges that at least one sub-data higher than the corresponding threshold exists, the analyzing module selects sub-data exceeding the corresponding threshold from the module detection data set; after the abnormal data is used as the abnormal data, the quantity of the abnormal data is acquired, the proportion that the abnormal data exceeds the corresponding threshold value is used as the abnormal degree, and the abnormal data and the abnormal degree are respectively used as the abnormal characteristics.
Further, when the imaging performance coefficient Cxs and the image quality coefficient Txs are not higher than the corresponding threshold values, repeatedly detecting the camera module for several times along the time axis at fixed intervals, and correspondingly acquiring a plurality of imaging performance coefficients Cxs and image quality coefficients Txs, wherein n is a positive integer greater than the positive integer;
the imaging performance coefficient Cxs and the image quality coefficient Txs are associated to generate a module detection coefficient Mzs, wherein the module detection coefficient Mzs is obtained by the following method: the module detection coefficient Mzs is obtained according to the following formula:
wherein ,k2 、k 1 Is weight, 0 is less than or equal to k 1 ≤1，0≤k 2 Not more than 1, and k 1 2 +k 2 2 Specific values are adjustable by the user, where n is a positive integer greater than 1; the module detection coefficient Mzs is characterized by the following:
wherein ,Cxsi Txs as a moving median of imaging coefficient of performance i The intermediate value is shifted for the image quality coefficient.
Further, after a plurality of module detection coefficients Mzs are obtained along a time axis, a prediction module predicts the module detection coefficients Mzs by using a linear regression model and generates a predicted value, and when at least one of the number of abnormal data or the predicted value of the module detection coefficients Mzs is higher than a corresponding threshold value, a control unit forms a corresponding control instruction to enable a collection unit to obtain a detection scheme corresponding to the abnormal data and summarize the detection scheme library; and the analysis module calculates a correlation coefficient R between the module detection coefficient Mzs and the detection condition coefficient Jts, and when the correlation coefficient R is larger than a corresponding threshold value, a control unit forms a corresponding control instruction to enable the adjusting unit to adjust the illumination and temperature conditions in the detection area until the detection condition coefficient Jts accords with the corresponding threshold value.
Further, the second processing unit includes a feature recognition module, a model training module and a matching module, wherein, according to the correspondence between the abnormal features and the detection schemes, a similarity algorithm is used, after training and testing, the model training module generates a matching model, and a module detection dataset output by preliminary detection is obtained, so that the feature recognition module recognizes the abnormal features and outputs the abnormal features, and according to the abnormal features, the matching module matches the corresponding detection schemes from the detection scheme library and outputs the detection schemes.
Further, the third processing unit comprises a verification module, a scheme output module and an early warning module, wherein after training and testing, a module digital twin model is established according to the specification parameters of the camera module and the detection process of the camera module, a matched detection scheme is sent to the verification module, the detection scheme is simulated by using the module digital twin model under the current illumination and temperature conditions of a detection area, if the detection scheme is feasible, the detection scheme is output as a secondary detection scheme by the scheme output module, and if the detection scheme is not feasible, the early warning module sends early warning.
The camera module detection method comprises the following steps: when the camera module is detected, detecting the environmental conditions in a detection area, and when the temperature in the detection area is increased due to the irradiation of light, respectively carrying out preliminary detection on the imaging quality and imaging performance of the camera module, summarizing a plurality of sub-data obtained by detection, and establishing a module detection data set; generating an image quality coefficient Txs and an imaging performance coefficient Cxs respectively from the module detection data, and analyzing and acquiring abnormal data in sub-data of the module detection data set when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value; when the number of the abnormal data exceeds a threshold value, adjusting the illumination and temperature conditions in the detection area, collecting detection schemes corresponding to the abnormal data, summarizing and establishing a detection scheme library;
using the abnormal data and the degree of abnormality as abnormal characteristics, and matching a detection scheme corresponding to the camera module from a detection scheme library according to the abnormal characteristics by the trained matching model; according to the specification parameters of the camera module and the detection process thereof, after training and testing, a module digital twin model is established, the detection scheme is simulated by combining the module digital twin model, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as a secondary detection scheme, and if not, early warning is sent out.
(III) beneficial effects
The application provides a camera module detection system and a detection method, which have the following beneficial effects:
1. when the camera module is detected, the detection process is divided into a primary detection part and a secondary detection part, when the imaging performance coefficient Cxs and the image quality coefficient Txs are obtained, the camera module can be subjected to primary screening, the unqualified camera module is selected, and compared with single factor screening, the camera module has better comprehensiveness and a certain fault tolerance rate, and is used as a judgment standard, the rationality is better, and the detection reliability is improved;
2. by selecting abnormal sub-data in the module detection data set, during secondary detection, a targeted detection scheme can be selected, and under the condition of considering the environmental factors of the detection area, the effect of rechecking the primary detection result is realized, so that the reliability of the detection result is ensured;
3. the detection condition coefficient Jts and the module detection coefficient Mzs are established, the correlation coefficient R between the detection condition coefficient Jts and the module detection coefficient Mzs is further obtained, the influence of the environmental factors of the detection area on the detection result is judged, when the influence degree is large, the detection condition in the detection area is adjusted, the interference of the external environment on the detection result is reduced, and the reliability of the detection result is ensured;
4. the simulation test is carried out on the detection scheme during secondary detection by using the module digital twin model, the reliability of the detection scheme can be detected after the current detection condition is determined, and the reliability of the detection result is further guaranteed under the condition of reducing environmental interference.
Drawings
FIG. 1 is a schematic diagram of a first process of a camera module inspection system according to the present application;
FIG. 2 is a schematic diagram of a second process of the camera module inspection system according to the present application;
in the figure:
10. a quality detection unit; 11. an environment detection module; 12. an imaging detection module; 13. a performance detection module;
20. a first processing unit; 21. an evaluation module; 22. a judging module; 23. a prediction module; 24. an analysis module;
30. a control unit; 40. an adjusting unit; 50. a collection unit; 60. a second processing unit; 61. a feature recognition module; 62. a model training module; 63. a matching module;
70. a third processing unit; 71. a verification module; 72. a scheme output module; 73. and an early warning module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1-2, the present application provides a camera module detection system, which includes a quality detection unit 10, a first processing unit 20, a control unit 30, an adjusting unit 40, a collecting unit 50, a second processing unit 60, and a third processing unit 70, wherein,
when the camera module is detected, the quality detection unit 10 detects the environmental conditions in the detection area, when the temperature in the detection area is increased due to the irradiation of light, the imaging quality and the imaging performance of the camera module are respectively detected preliminarily, a plurality of sub-data obtained by detection are summarized, and a module detection data set is established;
the module detection data set is sent to the first processing unit 20, an image quality coefficient Txs and an imaging performance coefficient Cxs are respectively generated, and when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value, abnormal data in sub-data of the module detection data set are obtained through analysis; when the number of the abnormal data exceeds the threshold value, a control instruction is formed by the control unit 30, so that the adjusting unit 40 adjusts the illumination and temperature conditions in the detection area, the collecting unit 50 collects the detection schemes corresponding to the abnormal data, and a detection scheme library is summarized and built;
the abnormal data and the degree of abnormality are used as abnormal characteristics, the abnormal characteristics and a detection scheme library are sent to the second processing unit 60, and a detection scheme corresponding to the camera module is matched from the detection scheme library according to the abnormal characteristics by the trained matching model;
according to the specification parameters of the camera module and the detection process thereof, after training and testing, a module digital twin model is established, a secondary detection scheme is sent to the third processing unit 70, the module digital twin model is combined, the detection scheme is simulated under the current illumination and temperature conditions of the detection area, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as the secondary detection scheme, and if not, early warning is sent.
Referring to fig. 1 and 2, the quality detection unit 10 includes an environment detection module 11, an imaging detection module 12, and a performance detection module 13, wherein when the camera module is unfolded and detected, the environment detection module 11 detects environmental data in a detection area, and after respectively obtaining the illumination intensity Gq and the indoor temperature St in the detection area, dimensionless processing is performed to generate a detection condition coefficient Jts in the detection area: the generation mode is according to the following formula:
wherein ,0≤F1 ≤1，0≤F 2 Not less than 1, and not less than 0.80% of F 1 +F 2 Less than or equal to 1.79, the specific value of which is adjusted and set by a user, C 1 Is a constant correction coefficient.
When the camera module is used, the detection condition coefficient Jts is formed when the camera module is unfolded and detected, the detection conditions in the detection area can be comprehensively evaluated and used for judging the influence degree of the detection conditions in the detection area on the detection result, and therefore the detection conditions can be adjusted according to the judgment result, for example, the indoor illumination condition, the indoor temperature condition and the like are changed.
Referring to fig. 1, when the camera module images, the performance detection module 13 detects the imaging performance of the camera module, at least obtains the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw in the imaging process, and gathers the detection results; after the camera module images, the imaging detection module 12 detects the quality of the acquired image, at least acquires the picture definition Hq, the color saturation Sc and the noise number Zd, and gathers the detection result; and summarizing the detection result obtained by the preliminary detection as sub-data, and establishing a module detection data set.
During the use, through the module detection dataset of establishing, accomplish the preliminary detection to the camera module, at this moment, can carry out preliminary screening to the camera module, select out the disqualified article in a plurality of camera module.
Referring to fig. 1 and 2, the first processing unit 20 includes an evaluation module 21, a judging module 22, a predicting module 23 and an analyzing module 24, wherein a module detection data set obtained by preliminary detection is sent to the evaluation module 21 to generate an image quality coefficient Txs and an imaging performance coefficient Cxs respectively;
the imaging performance coefficient Cxs is generated as follows: the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw are sent to the evaluation module 21, and after dimensionless processing, the imaging performance coefficient Cxs is generated according to the following formula:
wherein alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha+beta is more than or equal to 1, alpha and beta are weights, the specific values of the weights are adjusted and set by a user, and C 2 Is a constant correction coefficient.
The image quality coefficient Txs is generated as follows: the image sharpness Hq, the color saturation Sc, and the noise number Zd are sent to the evaluation module 21, and after dimensionless processing, the image quality coefficient Txs is generated according to the following formula:
wherein, gamma and theta are parameters of changeable constants, gamma is more than or equal to 0.43 and less than or equal to 1.26,0.82 and theta is more than or equal to 1.98, and the specific values are adjusted and set by a user.
When the camera module is used, the imaging performance and the image performance of the camera module are respectively comprehensively evaluated by forming an imaging performance coefficient Cxs and an image quality coefficient Txs, and the comprehensive performance is relatively good when the camera module is used as a quality evaluation standard; compared with the method which uses single sub-data as a judgment standard and uses the imaging performance coefficient Cxs and the image quality coefficient Txs as the judgment standard, the method has better fault tolerance and is more relevant to the actual use scene.
Referring to fig. 1 and 2, the acquired imaging performance coefficients Cxs and image quality coefficients Txs are sent to the judging module 22, and when the judging module 22 judges that at least one sub-data above the corresponding threshold exists, the analyzing module 24 selects sub-data above the corresponding threshold from the module detection data set;
after the abnormal data is used as the abnormal data, the quantity of the abnormal data is acquired, the proportion that the abnormal data exceeds the corresponding threshold value is used as the abnormal degree, and the abnormal data and the abnormal degree are respectively used as the abnormal characteristics.
When the method is used, abnormal characteristics are screened out from the sub-data on the basis of the imaging performance coefficient Cxs and the image quality coefficient Txs, and the abnormal characteristics can be caused by error detection modes or can be interfered by detection conditions in a detection area, so that the matched detection scheme is reselected on the basis of acquiring the abnormal characteristics by combining the detection conditions, secondary detection can be expanded, and the reliability of the detection data is ensured.
Referring to fig. 1 and 2, when the imaging performance coefficient Cxs and the image quality coefficient Txs are not higher than the corresponding threshold values, repeatedly detecting the camera module several times along the time axis at fixed intervals, and correspondingly obtaining several imaging performance coefficients Cxs and image quality coefficients Txs; for example, imaging coefficient of performance Cxs 1 、Cxs 2 、Cxs 3 Up to Cxs n Image quality coefficient Txs 1 、Txs 2 、Txs 3 Up to Txs n Wherein n is a positive integer greater than 1;
the imaging performance coefficient Cxs and the image quality coefficient Txs are associated to generate a module detection coefficient Mzs, wherein the module detection coefficient Mzs is obtained by the following method: the module detection coefficient Mzs is obtained according to the following formula:
wherein ,k2 、k 1 Is weight, 0 is less than or equal to k 1 ≤1，0≤k 2 Not more than 1, and k 1 2 +k 2 2 The specific value is adjustable by the user, where n is a positive integer greater than 1; the module detection coefficient Mzs is characterized by the following:
wherein ,Cxsi Txs as a moving median of imaging coefficient of performance i Shifting the intermediate value for the image quality coefficient;
when the imaging performance coefficient Cxs and the image quality coefficient Txs are temporarily normal in use, in order to reduce detection risk, a module detection coefficient Mzs is further generated, the change of the module detection coefficient Mzs is predicted, the stability of the current detection mode is judged according to the predicted value of the module detection coefficient Mzs, and the influence degree of the detection condition coefficient Jts on the detection result can be judged according to the formed module detection coefficient Mzs.
Referring to fig. 1 and 2, after a plurality of module detection coefficients Mzs are acquired along a time axis, a prediction module 23 predicts the module detection coefficients Mzs by using a linear regression model and generates a predicted value, and when at least one of the number of abnormal data or the predicted value of the module detection coefficient Mzs is higher than a corresponding threshold value, a control unit 30 forms a corresponding control instruction to enable a collection unit 50 to acquire a detection scheme corresponding to the abnormal data, and a detection scheme library is formed in a summarizing manner;
the analysis module 24 calculates a correlation coefficient R between the module detection coefficient Mzs and the detection condition coefficient Jts, and when the correlation coefficient R is greater than a corresponding threshold value, the control unit 30 forms a corresponding control instruction to enable the adjustment unit 40 to adjust the illumination and temperature conditions in the detection area until the detection condition coefficient Jts meets the corresponding threshold value.
When the method is used, when abnormal data in primary detection occur more frequently, the current detection scheme is indicated to be possibly wrong or greatly influenced by the current detection conditions; at the moment, according to the abnormal data and the abnormal characteristics thereof, the detection scheme is reselected and matched, and according to the matched detection scheme, the camera module is subjected to secondary detection, so that the detection result is further ensured, and the reliability of the detection result is improved;
at another angle, when the correlation coefficient R is larger, the detection conditions in the detection area are adjusted, the illumination conditions and the temperature conditions in the detection area are properly adjusted, the influence of the environment on the detection process is reduced, the external interference is reduced, the detection result is guaranteed, and the reliability of the detection result is improved.
Referring to fig. 1 and 2, the second processing unit 60 includes a feature recognition module 61, a model training module 62 and a matching module 63, wherein, according to the correspondence between the abnormal features and the detection schemes, a similarity algorithm is used, after training and testing, the model training module 62 generates a matching model, and obtains a module detection data set output by the preliminary detection, so that the feature recognition module 61 recognizes the abnormal features therein and outputs, and according to the abnormal features, the matching module 63 matches the corresponding detection schemes from the detection scheme library and outputs.
When the camera module is used, according to the similarity of the abnormal characteristics and the detection scheme, a scheme with better pertinence can be selected during secondary detection, so that the reliability of the detection result of the output aiming at the camera module is higher.
Referring to fig. 2, the third processing unit 70 includes a verification module 71, a scheme output module 72, and an early warning module 73, wherein, according to the specification parameters of the camera module and the detection process thereof, after training and testing, a module digital twin model is established, a matched detection scheme is sent to the verification module 71, the detection scheme is simulated by using the module digital twin model under the current illumination and temperature conditions of the detection area, if the detection scheme is feasible, the scheme output module 72 outputs the detection scheme as a secondary detection scheme, and if the detection scheme is not feasible, the early warning module 73 sends out early warning.
When the method is used, simulation test is carried out on the selected detection scheme on the basis of the module digital twin model, and whether the detection can have usability is judged under the detection condition limited in the detection area, so that the applicability of the selected detection scheme in secondary detection is ensured, and the risk of error generation of the detection scheme is reduced.
The combination of the above contents:
when the camera module is detected, the detection process is divided into a primary detection part and a secondary detection part, when the imaging performance coefficient Cxs and the image quality coefficient Txs are obtained, the camera module can be subjected to primary screening, the unqualified camera module is selected, and compared with single factor screening, the camera module has better comprehensiveness and a certain fault tolerance rate, and is used as a judgment standard, the rationality is better, and the detection reliability is improved;
selecting abnormal sub-data in the module detection data set, selecting a targeted detection scheme during secondary detection, and realizing the effect of rechecking the primary detection result under the condition of considering the environmental factors of the detection area, thereby ensuring the reliability of the detection result;
the detection condition coefficient Jts and the module detection coefficient Mzs are established, the correlation coefficient R between the detection condition coefficient Jts and the module detection coefficient Mzs is further obtained, the influence of the environmental factors of the detection area on the detection result is judged, when the influence degree is large, the detection condition in the detection area is adjusted, the interference of the external environment on the detection result is reduced, and the reliability of the detection result is ensured;
the simulation test is carried out on the detection scheme during secondary detection by using the module digital twin model, the reliability of the detection scheme can be detected after the current detection condition is determined, and the reliability of the detection result is further guaranteed under the condition of reducing environmental interference.
Referring to fig. 1-2, the present application provides a method for detecting a camera module, comprising the following steps:
when the camera module is detected, detecting the environmental conditions in a detection area, and when the temperature in the detection area is increased due to the irradiation of light, respectively carrying out preliminary detection on the imaging quality and imaging performance of the camera module, summarizing a plurality of sub-data obtained by detection, and establishing a module detection data set;
generating an image quality coefficient Txs and an imaging performance coefficient Cxs respectively from the module detection data, and analyzing and acquiring abnormal data in sub-data of the module detection data set when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value; when the number of the abnormal data exceeds a threshold value, adjusting the illumination and temperature conditions in the detection area, collecting detection schemes corresponding to the abnormal data, summarizing and establishing a detection scheme library;
using the abnormal data and the degree of abnormality as abnormal characteristics, and matching a detection scheme corresponding to the camera module from a detection scheme library according to the abnormal characteristics by the trained matching model;
according to the specification parameters of the camera module and the detection process thereof, after training and testing, a module digital twin model is established, the detection scheme is simulated by combining the module digital twin model, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as a secondary detection scheme, and if not, early warning is sent out.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (10)
1. Camera module detecting system, its characterized in that: comprises a quality detection unit (10), a first processing unit (20), a control unit (30), an adjusting unit (40), a collecting unit (50), a second processing unit (60) and a third processing unit (70), wherein,
when the camera module is detected, the quality detection unit (10) detects the environmental conditions in the detection area, and when the temperature in the detection area is increased due to the irradiation of light, the quality detection unit (10) respectively performs preliminary detection on the image quality and imaging performance of the camera module, gathers a plurality of sub-data obtained by the preliminary detection, and establishes a module detection data set;
the module detection data set is sent to a first processing unit (20), an image quality coefficient Txs and an imaging performance coefficient Cxs are respectively generated, and when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value, abnormal data of the module detection data set are analyzed and obtained; when the number of the abnormal data exceeds a threshold value, a control command is formed by the control unit (30), the adjusting unit (40) is used for adjusting the illumination and temperature conditions in the detection area, the collecting unit (50) is used for collecting detection schemes corresponding to the abnormal data, and a detection scheme library is summarized and built;
the abnormal data and the degree of abnormality are used as abnormal characteristics, the abnormal characteristics and a detection scheme library are sent to a second processing unit (60), and a detection scheme corresponding to the camera module is matched from the detection scheme library according to the abnormal characteristics by the trained matching model;
according to the specification parameters of the camera module and the detection process of the camera module, after training and testing, a module digital twin model is established, the matched detection scheme is sent to a third processing unit (70), the matched detection scheme is simulated under the current illumination and temperature conditions of a detection area by combining the module digital twin model, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as a secondary detection scheme, and if not, early warning is sent.
2. The camera module inspection system of claim 1, wherein: the quality detection unit (10) comprises an environment detection module (11), an imaging detection module (12) and a performance detection module (13), wherein when the camera module is unfolded and detected, the environment detection module (11) detects environment data in a detection area;
after the illumination intensity Gq and the indoor temperature St in the detection area are respectively obtained, dimensionless processing and generation are carried outDetection condition coefficients Jts in the detection area: the generation mode is according to the following formula: wherein ,is->Is weight(s)>，/>And->The specific value of which is set by the user adjustment, +.>Is a constant correction coefficient.
3. The camera module detection system of claim 2, wherein: when the camera module images, detecting the imaging performance of the camera module by a performance detection module (13), and at least acquiring the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw in the imaging process; after the camera module images, the imaging detection module (12) detects the quality of the acquired image, and at least the picture definition Hq, the color saturation Sc and the noise number Zd are acquired.
4. The camera module inspection system of claim 1, wherein: the first processing unit (20) comprises an evaluation module (21), a judging module (22), a prediction module (23) and an analysis module (24), wherein a module detection data set obtained by preliminary detection is sent to the evaluation module (21) to respectively generate an image quality coefficient Txs and an imaging performance coefficient Cxs;
the imaging performance coefficient Cxs is generated as follows: the focusing speed Dj, the signal-to-noise ratio Xz and the dynamic range Dw are sent to an evaluation module (21), and after dimensionless processing, the imaging performance coefficient Cxs is generated according to the following formula: wherein ,/>，/>And (2) and，/>、/>for the weight, its specific value is set by the user adjustment, +.>Is a constant correction coefficient;
the image quality coefficient Txs is generated as follows: the picture definition Hq, the color saturation Sc and the noise number Zd are sent to an evaluation module (21), and after dimensionless processing, an image quality coefficient Txs is generated according to the following formula: wherein ,/>Is->For changeable constant parameters->，/>The specific value of which is set by the user adjustment.
5. The camera module inspection system of claim 4, wherein: transmitting the acquired imaging performance coefficients Cxs and image quality coefficients Txs to a judging module (22), and selecting sub-data exceeding a corresponding threshold value from the module detection data set by an analyzing module (24) when the judging module (22) judges that at least one sub-data exceeding the corresponding threshold value exists; after the abnormal data is used as the abnormal data, the quantity of the abnormal data is acquired, the proportion that the abnormal data exceeds the corresponding threshold value is used as the abnormal degree, and the abnormal data and the abnormal degree are respectively used as the abnormal characteristics.
6. The camera module inspection system of claim 5, wherein: when neither the imaging performance coefficient Cxs nor the image quality coefficient Txs is higher than the corresponding threshold, repeatedly detecting the camera module n times along the time axis at fixed intervals, and correspondingly acquiring n imaging performance coefficients、/>、/> Up to->And n image quality coefficients->、/>、/> Up to->；
The imaging performance coefficient and the image quality coefficient are related to generate a module detection coefficient Mzs, wherein the module detection coefficient Mzs is obtained by the following steps: wherein ,/>、/>Is weight(s)>，/>And->The specific value is adjusted and set by a user;wherein n is a positive integer greater than 1.
7. The camera module inspection system of claim 6, wherein: after a plurality of module detection coefficients Mzs are obtained along a time axis, a prediction module (23) predicts the module detection coefficients Mzs by using a linear regression model and generates a predicted value, and when at least one of the number of abnormal data or the predicted value of the module detection coefficients Mzs is higher than a corresponding threshold value, a control unit (30) forms a corresponding control instruction to enable a collection unit (50) to obtain detection schemes corresponding to the abnormal data, and a detection scheme library is formed in a summarizing mode;
and the analysis module (24) calculates a correlation coefficient R between the module detection coefficient Mzs and the detection condition coefficient Jts, and when the correlation coefficient R is larger than a corresponding threshold value, a control unit (30) forms a corresponding control instruction to enable the adjusting unit (40) to adjust the illumination and temperature conditions in the detection area until the detection condition coefficient Jts accords with the corresponding threshold value.
8. The camera module inspection system of claim 7, wherein: the second processing unit (60) comprises a feature recognition module (61), a model training module (62) and a matching module (63), wherein,
according to the correspondence between the abnormal characteristics and the detection schemes, a similarity algorithm is used, a model training module (62) generates a matching model after training and testing, a module detection data set output by preliminary detection is obtained, the characteristic recognition module (61) recognizes the abnormal characteristics in the model detection data set and outputs the abnormal characteristics, and according to the abnormal characteristics, the matching module (63) matches the corresponding detection schemes from the detection scheme library and outputs the detection schemes.
9. The camera module inspection system of claim 8, wherein: the third processing unit (70) comprises a verification module (71), a scheme output module (72) and an early warning module (73), wherein,
according to the specification parameters of the camera module and the detection process of the camera module, after training and testing, a module digital twin model is established, the matched detection scheme is sent to a verification module (71), the matched detection scheme is simulated by using the module digital twin model under the current illumination and temperature conditions of a detection area, if the detection scheme is feasible, the detection scheme is output as a secondary detection scheme by a scheme output module (72), and if the detection scheme is not feasible, an early warning is sent by an early warning module (73).
10. The camera module detection method is characterized in that: comprising the following steps:
when the camera module is detected, detecting the environmental conditions in a detection area, and when the temperature in the detection area is increased due to the irradiation of light, respectively carrying out preliminary detection on the image quality and imaging performance of the camera module, summarizing a plurality of sub-data obtained by the preliminary detection, and establishing a module detection data set;
generating an image quality coefficient Txs and an imaging performance coefficient Cxs respectively by the module detection data set, and analyzing and acquiring abnormal data in sub-data of the module detection data set when at least one of the image quality coefficient Txs and the imaging performance coefficient Cxs is higher than a corresponding threshold value; when the number of the abnormal data exceeds a threshold value, adjusting the illumination and temperature conditions in the detection area, collecting detection schemes corresponding to the abnormal data, summarizing and establishing a detection scheme library;
using the abnormal data and the degree of abnormality as abnormal characteristics, and matching a detection scheme corresponding to the camera module from a detection scheme library according to the abnormal characteristics by the trained matching model;
according to the specification parameters of the camera module and the detection process of the camera module, after training and testing, a module digital twin model is established, the matched detection scheme is simulated by combining the module digital twin model, whether the detection scheme is feasible or not is judged, if so, the feasible detection scheme is output as a secondary detection scheme, and if not, early warning is sent out.
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