Electronic equipment shell intelligent detection method based on machine vision

Abstract

The invention relates to the technical field of data analysis, and particularly discloses an electronic equipment shell intelligent detection method based on machine vision, which comprises the following steps: S1, obtaining detection records of electronic equipment shells in a sub-time period, and determining the total number of the electronic equipment shells of the same defect type; s2, calculating a quantity difference value, generating coordinate points, and fitting the coordinate points to obtain a fitting curve; determining a maximum number difference value, setting a number difference value interval, and setting a number difference value grade in the number difference value interval; and S3, determining a target proportion, screening an abnormal curve based on the target proportion, and judging that the defect type improvement effect corresponding to the abnormal curve is poor. According to the method, the defect types with poor improvement effects are determined by comparing the improvement effects of different defect types, and a basis is provided for improvement of production quality.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent detection method for electronic device casings based on machine vision. Background Art

[0002] The shells of electronic devices such as mobile phones and tablets are not only simple barriers to protect the devices from falling, scratches and external impacts, but also important elements to enhance user experience and highlight personality, and are a key part to protect the internal precision components.

[0003] Defect detection of finished mobile phone shells usually relies on manual methods. Quality inspectors use the naked eye to determine whether the shell has defects. However, this method not only consumes a lot of manpower and time, but also highly relies on the personal experience and judgment of quality inspectors. It lacks scientificity and consistency and is prone to human errors. With the rapid development of artificial intelligence and machine learning technologies, these technologies have shown great potential and advantages in many fields. Due to their high precision, automation, scalability and stable detection results, they are widely used in device shell defect detection.

[0004] However, although machine vision detection methods can quickly screen out defective shell products and remove them, subsequent processing often only stops at reworking or directly eliminating these defective products, lacking further in-depth analysis, making it difficult to provide a scientific basis for improving production quality. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent detection method for electronic device housing based on machine vision to solve the following technical problems:

[0006] Although machine vision detection methods can quickly screen out defective shell products and remove them, subsequent processing often only stops at reworking or directly eliminating these defective products, lacking further in-depth analysis, making it difficult to provide a scientific basis for quality improvement.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The intelligent detection method of electronic device housing based on machine vision comprises the following steps:

[0009] S1: Divide a preset monitoring period into n sub-periods of equal length, where n is a first preset number, obtain detection records of electronic device casings in the sub-periods, and determine the total number of electronic device casings with the same defect type;

[0010] S2: Calculate the quantity difference ΔCa=Ca-Cys, where Ca represents the total number of electronic equipment housings with the same defect type in the i-th sub-period, Cb' represents the total number of electronic equipment housings of the same defect type in the b-th sub-period in the previous monitoring period adjacent to the current monitoring period, generates coordinate points (a, ΔCa), and fits the coordinate points to obtain a fitting curve f(t);

[0011] Determine the maximum quantity difference ΔCmax=max(ΔCJH), ΔCJH=(max(f1(t)), max(f2(t)), ..., max(fm(t))), fm(t) represents the fitting curve corresponding to the m-th type of defect, set the quantity difference interval [0, ΔCmax], and set N quantity difference levels from low to high at a preset quantity difference interval within the quantity difference interval, where N is a second preset number;

[0012] S3: Taking the portion of the fitting curve within the single quantity difference level as the target curve, determining the ratio of the definition domain of the target curve to the definition domain of the fitting curve, taking it as the target ratio, screening abnormal curves based on the target ratio, and determining that the defect type corresponding to the abnormal curve has a poor improvement effect.

[0013] As a further solution of the present invention: in the step S3, the process of screening abnormal curves based on the target ratio more specifically includes:

[0014] Determine the influence coefficient YXi=i*γ of the i-th quantity difference level, where γ is a preset correction coefficient;

[0015] The target ratios are sorted according to their size, and the larger the target ratio is, the later its position in the sorting is, and the position of the target ratio in the sorting is determined;

[0016] Calculating judgment scores Among them, Fi represents the ranking position of the target proportion in the ranking corresponding to the i-th quantity difference level, when the judgment score P≥Pys, the corresponding fitting curve is used as the abnormal curve, and Pys represents the preset judgment score threshold.

[0017] As a further solution of the present invention: in the step S1, the process of determining the defect of the electronic device housing specifically includes:

[0018] Establishing a database, wherein the database stores images of electronic device housings with marked defect types;

[0019] A defect recognition model is established based on the deep learning model, the defect recognition model is trained and verified through the database, and the electronic device housing image is input into the trained defect recognition model to obtain the defect type of the electronic device housing.

[0020] As a further solution of the present invention, the process of determining the defect type of the electronic device housing further includes the following steps:

[0021] A preset number of electronic device casing images are randomly selected, and the defect types corresponding to the electronic device casing images are manually determined.

[0022] When the defect type determined manually is different from the defect type determined by the defect recognition model, the defect type determined manually and the corresponding electronic device casing image are input into the defect recognition model together, and deep learning training is performed again.

[0023] As a further solution of the present invention: the image of the electronic device housing is acquired at a preset position based on a camera device.

[0024] As a further solution of the present invention: the step S1 further includes the following steps:

[0025] When the quantity difference corresponding to a single sub-cycle is greater than or equal to the preset quantity difference threshold ΔCys, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0026] When the total quantity difference , the subsequent steps are not executed, and the corresponding defect type is judged to have poor improvement effect.

[0027] As a further solution of the present invention: the step S2 further includes the following steps:

[0028] Determining the monotonicity of the fitted curve;

[0029] When the fitting curve increases monotonically, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0030] When the fitting curve increases monotonically, the subsequent steps are not executed, and it is determined that the corresponding defect type has a good improvement effect.

[0031] As a further solution of the present invention: when the fitting curve does not have monotonicity, the following steps are performed:

[0032] When the proportion of the monotonically increasing part on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0033] When the proportion of the monotonically decreasing portion on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the corresponding defect type has a good improvement effect.

[0034] Beneficial effects of the present invention: In this solution, by dividing the monitoring period into shorter time periods, the defect situation can be tracked in a refined manner, and the time distribution of defect occurrence can be more clearly reflected. This fine-grained monitoring makes it easy to identify the defect change trend that occurs within a specific time, and the defect data is statistically analyzed in sub-periods to provide detailed basic data for subsequent analysis; with data records in different time periods, the laws and trends of defect generation can be better understood; difference calculation can reveal the relative change trend of the number of defects, and help identify the increase or decrease of defects in the current sub-period, and the quantity difference reflects the change in the total number of electronic equipment casings of the same defect type in the sub-period relative to the average situation of the previous monitoring period; through curve fitting, the fluctuation of data is smoothed, which can eliminate the interference of some accidental fluctuations, making the data more coherent and referenceable, and the fitting curve can effectively The overall trend of the number of defects can be identified, which helps to grasp the changing rules of defects as a whole; the difference level is divided by preset intervals, and the defect difference data is standardized by grading, which helps to conduct comparative analysis under a unified framework, ensuring that defects of different categories are placed in the same difference range for comparison, reducing the complexity when comparing different defect types, and improving the efficiency and accuracy of analysis; the performance of different defect types in different level difference intervals is determined by calculating the target curve and the definition domain ratio, and the setting of the influence coefficient can help quantify the influence weight of different quantity difference levels, so that the defect types with high fluctuations (i.e., larger quantity difference) can be expressed more accurately, and the influence coefficient is combined with the sorting position to determine the improvement effect of different types of defects (using the judgment score as a measure), and prompts are given after determining the abnormal curve, so as to facilitate the allocation of resources for targeted improvement. The present invention compares the improvement effects of different defect types with each other, determines the defect types with poor improvement effects, and provides a basis for improving production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 It is a flow chart of the intelligent detection method of electronic device housing based on machine vision of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] See also Figure 1 As shown, the present invention is an intelligent detection method for electronic device housing based on machine vision, comprising the following steps:

[0039] S1: Divide a preset monitoring period into n sub-periods of equal length, where n is a first preset number, obtain detection records of electronic device casings in the sub-periods, and determine the total number of electronic device casings with the same defect type;

[0040] S2: Calculate the quantity difference ΔCa=Ca-Cys, where Ca represents the total number of electronic equipment housings with the same defect type in the i-th sub-period, Cb' represents the total number of electronic equipment housings of the same defect type in the b-th sub-period in the previous monitoring period adjacent to the current monitoring period, generates coordinate points (a, ΔCa), and fits the coordinate points to obtain a fitting curve f(t);

[0041] Determine the maximum quantity difference ΔCmax=max(ΔCJH), ΔCJH=(max(f1(t)), max(f2(t)), ..., max(fm(t))), fm(t) represents the fitting curve corresponding to the m-th type of defect, set the quantity difference interval [0, ΔCmax], and set N quantity difference levels from low to high at a preset quantity difference interval within the quantity difference interval, where N is a second preset number;

[0042] S3: Taking the portion of the fitting curve within the single quantity difference level as the target curve, determining the ratio of the definition domain of the target curve to the definition domain of the fitting curve, taking it as the target ratio, screening abnormal curves based on the target ratio, and determining that the defect type corresponding to the abnormal curve has a poor improvement effect.

[0043] It should be noted that by dividing the monitoring period into shorter time periods, the defect situation can be tracked in a refined manner and the time distribution of defect occurrence can be more clearly reflected. This fine-grained monitoring makes it easy to identify the defect change trend that occurs within a specific time, and the defect data is statistically analyzed in sub-periods to provide detailed basic data for subsequent analysis; with data records in different time periods, the laws and trends of defect generation can be better understood; difference calculation can reveal the relative change trend of the number of defects and help identify the increase or decrease of defects in the current sub-period. The quantity difference reflects the change in the total number of electronic equipment casings of the same defect type in the sub-period relative to the average situation of the previous monitoring period. The larger the quantity difference, the worse the improvement effect; through curve fitting, the fluctuation of the data is smoothed, which can eliminate the interference of some accidental fluctuations, making the data more coherent and referenceable, and the fitting curve It can effectively identify the overall trend of the number of defects, which helps to grasp the changing rules of defects as a whole; it uses preset intervals to divide the difference levels, and standardizes the defect difference data through grading, which helps to conduct subsequent comparative analysis under a unified framework, ensuring that defects of different categories are placed in the same difference range for comparison, reducing the complexity when comparing different defect types, and improving the efficiency and accuracy of the analysis; through the calculation of the target curve and the definition domain ratio, the performance of different defect types in different level difference intervals is determined, and the setting of the influence coefficient can help quantify the influence weights of different quantity difference levels, so that the defect types with high fluctuations (i.e., larger quantity differences) can be expressed more accurately. The influence coefficient is combined with the sorting position to determine the improvement effect of different types of defects (using the judgment score as the measurement standard), and prompts are given after the abnormal curve is determined to facilitate the allocation of resources for targeted improvements.

[0044] In another preferred embodiment of the present invention, in step S3, the process of screening abnormal curves based on the target ratio more specifically includes:

[0045] Determine the influence coefficient YXi=i*γ of the i-th quantity difference level, where γ is a preset correction coefficient;

[0046] The target ratios are sorted according to their size, and the larger the target ratio is, the later its position in the sorting is, and the position of the target ratio in the sorting is determined;

[0047] Calculating judgment scores Among them, Fi represents the ranking position of the target proportion in the ranking corresponding to the i-th quantity difference level, when the judgment score P≥Pys, the corresponding fitting curve is used as the abnormal curve, and Pys represents the preset judgment score threshold.

[0048] It is worth noting that the performance of different defect types in different level difference intervals can be determined by calculating the target curve and the definition domain ratio. The setting of the influence coefficient can help quantify the influence weights of different quantity difference levels, so that the defect types with high fluctuations (i.e., larger quantity differences) can be expressed more accurately. The influence coefficient is combined with the sorting position to determine the improvement effect of different types of defects (using the judgment score as the measurement standard). After determining the abnormal curve, a prompt will be given to facilitate the allocation of resources for targeted improvements.

[0049] In another preferred embodiment of the present invention, in step S1, the process of determining the defect of the electronic device housing specifically includes:

[0050] Establishing a database, wherein the database stores images of electronic device housings with marked defect types;

[0051] A defect recognition model is established based on the deep learning model, the defect recognition model is trained and verified through the database, and the electronic device housing image is input into the trained defect recognition model to obtain the defect type of the electronic device housing.

[0052] It is understandable that the use of artificial intelligence technology can realize automatic detection and classification of defects, improve detection efficiency, reduce the time and cost of manual detection, and at the same time improve the accuracy and consistency of identification, avoid missed detections and misjudgments caused by human factors. Automated defect identification can quickly and accurately obtain a large amount of defect data, provide a reliable data foundation for subsequent refined tracking and analysis, and help to better understand the laws and trends of defect generation.

[0053] In another preferred embodiment of the present invention, the process of determining the defect type of the electronic device housing further includes the following steps:

[0054] A preset number of electronic device casing images are randomly selected, and the defect types corresponding to the electronic device casing images are manually determined.

[0055] When the defect type determined manually is different from the defect type determined by the defect recognition model, the defect type determined manually and the corresponding electronic device casing image are input into the defect recognition model together, and deep learning training is performed again.

[0056] It should be noted that by adding random sampling and manual judgment steps in the process of determining the defect type of the electronic equipment casing, the accuracy of the defect recognition model can be effectively verified and optimized; the high accuracy of manual judgment can be used to correct the model's misjudgment and gradually improve the model's recognition performance. Through this dynamic update, the model can better adapt to the diverse defects in actual production and improve the accuracy and reliability of defect recognition; manual correction can be used to make up for the shortcomings of the model, ensuring that the data output by the model is more accurate, providing reliable data support for subsequent defect analysis.

[0057] In another preferred embodiment of the present invention, the image of the electronic device housing is acquired at a preset position based on a camera device.

[0058] In another preferred embodiment of the present invention, the step S1 further includes the following steps:

[0059] When the quantity difference corresponding to a single sub-cycle is greater than or equal to the preset quantity difference threshold ΔCys, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0060] When the total quantity difference , the subsequent steps are not executed, and the corresponding defect type is judged to have poor improvement effect.

[0061] It can be understood that by making threshold judgments on the quantity difference of each sub-cycle and the total quantity difference, it is possible to quickly identify the types of defects with poor improvement effects, avoid wasting resources on unnecessary analysis steps, and intervene in time when obvious abnormalities occur without waiting for the end of the entire monitoring cycle, thus avoiding unnecessary data processing.

[0062] In another preferred embodiment of the present invention, the step S2 further includes the following steps:

[0063] Determining the monotonicity of the fitted curve;

[0064] When the fitting curve increases monotonically, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0065] When the fitting curve increases monotonically, the subsequent steps are not executed, and it is determined that the corresponding defect type has a good improvement effect.

[0066] It is worth noting that the monotonicity of the fitting curve can reflect the overall increase or decrease of the defect type during the monitoring period, thereby revealing the effectiveness of its improvement. If the fitting curve shows a monotonically increasing trend (i.e., the number of defects continues to increase), it means that the defect type has not been effectively controlled during the improvement process, but has continued to grow. At this time, the system will judge that the improvement effect of this defect type is poor and stop subsequent analysis steps. It can directly determine the defect type with poor improvement effect, avoid unnecessary data processing and analysis, and save computing resources. On the contrary, if the fitting curve shows a monotonically decreasing trend (i.e., the number of defects continues to decrease), it means that the improvement measures for this defect type are effective, the number of defects is gradually decreasing, and a good improvement effect is shown.

[0067] In another preferred embodiment of the present invention, when the fitting curve does not have monotonicity, the following steps are performed:

[0068] When the proportion of the monotonically increasing part on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor;

[0069] When the proportion of the monotonically decreasing portion on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the corresponding defect type has a good improvement effect.

[0070] It is worth noting that if the proportion of the monotonically increasing part in the fitting curve is greater than or equal to 0.8 (80%), this means that the number of defects is on an upward trend most of the time during the monitoring period, indicating that the improvement effect of this defect type is poor; if the proportion of the monotonically decreasing part in the fitting curve is greater than or equal to 0.8 (80%), it means that the number of defects of this defect type is decreasing most of the time during the monitoring period, indicating that the improvement measures are effective and the improvement effect is good; if the proportion of the monotonically increasing part of the fitting curve is less than 0.8 or the proportion of the monotonically decreasing part of the fitting curve is less than 0.8, then the subsequent steps can be performed normally.

[0071] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent detection method for electronic device housing based on machine vision, characterized in that: The following steps are involved: S1: Divide a preset monitoring period into n sub-periods of equal length, where n is a first preset number, obtain detection records of electronic device casings in the sub-periods, and determine the total number of electronic device casings with the same defect type; S2: Calculate the quantity difference ΔCa=Ca-Cys, where Ca represents the total number of electronic equipment housings with the same defect type in the i-th sub-period, Cb' represents the total number of electronic equipment housings of the same defect type in the b-th sub-period in the previous monitoring period adjacent to the current monitoring period, generates coordinate points (a, ΔCa), and fits the coordinate points to obtain a fitting curve f(t); Determine the maximum quantity difference ΔCmax=max(ΔCJH), ΔCJH=(max(f1(t)), max(f2(t)), ..., max(fm(t))), fm(t) represents the fitting curve corresponding to the m-th type of defect, set the quantity difference interval [0, ΔCmax], and set N quantity difference levels from low to high at a preset quantity difference interval within the quantity difference interval, where N is a second preset number; S3: Taking the portion of the fitting curve within the single quantity difference level as the target curve, determining the ratio of the definition domain of the target curve to the definition domain of the fitting curve, taking it as the target ratio, screening abnormal curves based on the target ratio, and determining that the defect type corresponding to the abnormal curve has a poor improvement effect.

2. The method for intelligent detection of electronic device housing based on machine vision according to claim 1, characterized in that: In step S3, the process of screening abnormal curves based on the target ratio more specifically includes: Determine the influence coefficient YXi=i*γ of the i-th quantity difference level, where γ is a preset correction coefficient; The target ratios are sorted according to their size, and the larger the target ratio is, the later its position in the sorting is, and the position of the target ratio in the sorting is determined; Calculating judgment scores Among them, Fi represents the ranking position of the target proportion in the ranking corresponding to the i-th quantity difference level, when the judgment score P≥Pys, the corresponding fitting curve is used as the abnormal curve, and Pys represents the preset judgment score threshold.

3. The method for intelligent detection of electronic device housing based on machine vision according to claim 1, characterized in that: In step S1, the process of determining the defects of the electronic device housing specifically includes: Establishing a database, wherein the database stores images of electronic device housings with marked defect types; A defect recognition model is established based on the deep learning model, the defect recognition model is trained and verified through the database, and the electronic device housing image is input into the trained defect recognition model to obtain the defect type of the electronic device housing.

4. The method for intelligent detection of electronic device housing based on machine vision according to claim 3 is characterized in that: The process of determining the defect type of the electronic equipment housing also includes the following steps: A preset number of electronic device casing images are randomly selected, and the defect types corresponding to the electronic device casing images are manually determined. When the defect type determined manually is different from the defect type determined by the defect recognition model, the defect type determined manually and the corresponding electronic device casing image are input into the defect recognition model together, and deep learning training is performed again.

5. The method for intelligent detection of electronic device housing based on machine vision according to claim 3 is characterized in that: The image of the electronic device housing is acquired at a preset position based on the camera device.

6. The method for intelligent detection of electronic device housing based on machine vision according to claim 1, characterized in that: The step S1 further includes the following steps: When the quantity difference corresponding to a single sub-cycle is greater than or equal to the preset quantity difference threshold ΔCys, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor; When the total quantity difference , the subsequent steps are not executed, and the corresponding defect type is judged to have poor improvement effect.

7. The method for intelligent detection of electronic device housing based on machine vision according to claim 1, characterized in that: The step S2 further includes the following steps: Determining the monotonicity of the fitted curve; When the fitting curve increases monotonically, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor; When the fitting curve increases monotonically, the subsequent steps are not executed, and it is determined that the corresponding defect type has a good improvement effect.

8. The method for intelligent detection of electronic device housing based on machine vision according to claim 7, characterized in that: When the fitting curve does not have monotonicity, perform the following steps: When the proportion of the monotonically increasing part on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the improvement effect of the corresponding defect type is poor; When the proportion of the monotonically decreasing portion on the fitting curve to the fitting curve is greater than or equal to 0.8, the subsequent steps are not performed, and it is determined that the corresponding defect type has a good improvement effect.

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