A nail clipper defect detection method and device

Abstract

The application discloses a nail clippers defect detection method and device, the method comprises the following steps: collecting a blade image of the nail clippers, the blade image comprises a first image and / or a second image, the first image comprises a first direction view facing a gap between two blades of the nail clippers, and the second image comprises a second direction view facing side edges of the two blades; determining a defect detection parameter of the nail clippers according to the blade image, and the defect detection parameter is used for representing a defect detection result of the nail clippers. It can be seen that the application can analyze the defect detection parameter of the nail clippers according to the first direction view facing the gap between the two blades of the nail clippers and the second direction view facing the side edges of the two blades of the nail clippers, and the defect detection result can be represented by the defect detection parameter, so that the quality inspector does not need to manually detect the defects of the nail clippers, the accuracy and efficiency of the defect detection of the nail clippers are improved, the intuitiveness of the defect detection result is improved, and the accuracy of the correction of the production parameters of the nail clippers is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nail clippers, in particular to a nail clipper defect detection method and device. BACKGROUND

[0002] In the production process of nail clippers, if the production quality and installation quality of the blade exist problems, it may cause the installed nail clipper to have defects such as two blades not parallel and two blades misaligned. Therefore, after the installation of the nail clipper is completed, the nail clipper must be detected for defects. At present, the appearance of the blade of the nail clipper is mainly detected manually by the quality inspector, that is, whether the nail clipper has defects such as two blades not parallel and two blades misaligned is judged by the naked eye. However, since the manual detection method completely depends on the subjective judgment of the quality inspector, the accuracy and efficiency of the nail clipper defect detection are low. Therefore, how to improve the accuracy and efficiency of the nail clipper defect detection is particularly important. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a nail clipper defect detection method and device, which can improve the accuracy and efficiency of nail clipper defect detection.

[0004] In order to solve the above technical problem, the present application discloses a nail clipper defect detection method in the first aspect, which comprises:

[0005] Collecting a blade image of a nail clipper, the blade image comprising a first image and / or a second image, the first image comprising a first direction view facing a blade gap between two blades of the nail clipper, and the second image comprising a second direction view facing two side edges of the blades;

[0006] Determining a defect detection parameter of the nail clipper according to the blade image, the defect detection parameter being used to represent a defect detection result of the nail clipper.

[0007] As an optional implementation, in the first aspect of the present application, the collecting of the blade image of the nail clipper comprises:

[0008] Collecting a gap light transmission image of the blade gap based on a first image collecting device facing the blade gap between the two blades of the nail clipper as the first direction view, wherein the angle between the lens optical axis of the first image collecting device and the central axis of the nail clipper is less than a first deviation angle threshold; and / or,

[0009] Collecting a blade gap side edge reflection image of the two blades based on a second image collecting device facing the two blade side edges as the second direction view.

[0010] As an optional implementation, before the first image acquisition device based on the gap of the cutting edge between the two blades of the nail clipper acquires the gap light transmission image of the cutting edge as the first direction view, the method further comprises:

[0011] controlling at least one first light source on the side edge of the two blades of the nail clipper to light the gap of the cutting edge between the two blades, so that the light spot of the first light source covers the gap of the cutting edge, and the light of the first light source propagates outward from the inside of the nail clipper through the gap of the cutting edge when the two blades are in an open state;

[0012] and before the second image acquisition device based on the side edge of the two blades of the nail clipper acquires the reflection image of the side edge of the cutting edge of the two blades as the second direction view, the method further comprises:

[0013] controlling a second light source on the side edge of the two blades of the nail clipper to light the side edge of the cutting edge of the two blades, so that the light spot of the second light source covers the corner point of the cutting edge of the two blades, wherein the second image acquisition device for image acquisition and the second light source are located on the same side edge of the two blades, and the corner point of the cutting edge includes the tip of the side edge of the cutting edge of the blade.

[0014] As an optional implementation, in the first aspect of the application, the defect detection parameters include first defect parameters determined based on the first direction view and / or second defect parameters determined based on the second direction view, the first defect parameters include the parallel degree of the cutting edge of the two blades and / or the smooth degree of the cutting edge of each blade, and the second defect parameters include the alignment degree of the corner point of the cutting edge on the same side of the two blades.

[0015] As an optional implementation, in the first aspect of the application, the second defect parameters are determined by:

[0016] determining the vertical distance between the corner point of the cutting edge on the same side of each blade in the second direction view and a reference line as the vertical position corresponding to the corner point of the cutting edge of the blade, the reference line including any straight line perpendicular to the division line between the two blades in the second direction view;

[0017] determining the alignment degree of the corner point of the cutting edge on the same side of the two blades according to the vertical positions corresponding to the corner points of the cutting edge on the same side of the two blades as the second defect parameters.

[0018] As an optional implementation, in the first aspect of the present application, before determining the perpendicular distance between the corner point of the cutting edge of each blade in the second direction view and the contrast line as the corresponding perpendicular position of the corner point of the cutting edge, the method further comprises:

[0019] image calibrating the second direction view to obtain an image calibration result;

[0020] correcting the second direction view according to the image calibration result, so that the direction of the split line between the two blades in the second direction view is changed to a preset direction;

[0021] wherein, the image calibration of the second direction view to obtain an image calibration result comprises:

[0022] determining the coordinate values of one or more key structure points of the nail cutter in the second direction view;

[0023] determining the inclination angle between the direction of the split line between the two blades in the second direction view and the preset direction as the image calibration result according to the coordinate values of all the key structure points.

[0024] As an optional implementation, in the first aspect of the present application, the first defect parameter is determined by:

[0025] determining the parallel degree of the cutting edge of the two blades according to the actual cutting edge curve of the two blades extracted from the first direction view, or determining the parallel degree of the cutting edge of the two blades according to the light transmission degree of the cutting edge gap in the first direction view; and / or,

[0026] for each blade, comparing the actual cutting edge curve of the blade with the standard cutting edge curve corresponding to the blade to obtain the smoothness of the cutting edge of the blade, wherein the actual cutting edge curve of each blade comprises the cutting edge curve of the blade extracted from the first direction view, or the smoothness of the cutting edge of the two blades is determined according to the light transmission degree of the cutting edge gap in the first direction view.

[0027] As an optional implementation, in the first aspect of the present application, for each blade, the comparison of the actual cutting edge curve of the blade with the standard cutting edge curve corresponding to the blade to obtain the smoothness of the cutting edge of the blade comprises:

[0028] placing the actual cutting edge curve of the blade and the standard cutting edge curve corresponding to the blade in the same plane;

[0029] determine a first distance between each of a plurality of first pixel pairs in the actual blade edge curve and the standard blade edge curve, wherein each of the first pixel pairs comprises one actual pixel in the actual blade edge curve and one standard pixel in the standard blade edge curve that matches the actual pixel;

[0030] determine a degree of dispersion of a first distance set composed of all the first distances as a degree of smoothness of the blade edge of the blade.

[0031] The second aspect of the present application discloses a nail cutter defect detection device, which comprises:

[0032] a collection module configured to collect a blade image of the nail cutter, wherein the blade image comprises a first image and / or a second image, the first image comprises a first direction view facing a blade gap between two blades of the nail cutter, and the second image comprises a second direction view facing two side edges of the two blades;

[0033] a determination module configured to determine a defect detection parameter of the nail cutter according to the blade image, wherein the defect detection parameter is used to represent a defect detection result of the nail cutter.

[0034] As an optional implementation, in the second aspect of the present application, the specific manner in which the collection module collects the blade image of the nail cutter comprises:

[0035] a first image collection device facing the blade gap between the two blades of the nail cutter is used to collect a gap transmission image of the blade gap as the first direction view, wherein an angle between a lens optical axis of the first image collection device and a central axis of the nail cutter is less than a first deviation angle threshold; and / or,

[0036] a second image collection device facing the two side edges of the two blades is used to collect a blade side reflection image of the two blades as the second direction view.

[0037] As an optional implementation, in the second aspect of the present application, the device further comprises:

[0038] a light control module configured to control at least one first light source of the two side edges of the two blades of the nail cutter to light the blade gap between the two blades before the collection module collects the gap transmission image of the blade gap as the first direction view based on the first image collection device facing the blade gap between the two blades of the nail cutter, so that a light spot of the first light source covers the blade gap, and light rays of the first light source propagate from the inside of the nail cutter to the outside through the blade gap when the two blades are in an unclosed state.

[0039] The light control module is further configured to control a second light source of the two blade sides of the nail cutter to light the two blade sides so that a light spot of the second light source covers the corner points of the blade edges of the two blade sides before the acquisition module acquires the reflection image of the blade edge sides of the two blades based on a second image acquisition device facing the two blade sides of the nail cutter, as a second direction view, wherein the second image acquisition device for image acquisition and the second light source are located on the same side of the two blades, and the corner points of the blade edges are the sharp ends of the blade edge sides of the blades.

[0040] As an optional implementation, in the second aspect of the present application, the defect detection parameters include first defect parameters determined based on the first direction view and / or second defect parameters determined based on the second direction view, the first defect parameters include parallelism of the blade edges of the two blades and / or smoothness of the blade edges of each blade, and the second defect parameters include alignment of the corner points of the blade edges on the same side of the two blades.

[0041] As an optional implementation, in the second aspect of the present application, the second defect parameters are determined by the determination module in the following manner:

[0042] determining a vertical distance between the corner points of the blade edges on the same side of each blade in the second direction view and a reference line as a vertical position corresponding to the corner points of the blade edges of the blade, wherein the reference line includes any straight line perpendicular to a dividing line between the two blades in the second direction view;

[0043] determining alignment of the corner points of the blade edges on the same side of the two blades according to the vertical positions corresponding to the corner points of the blade edges on the same side of the two blades as the second defect parameters.

[0044] As an optional implementation, in the second aspect of the present application, the determination module is further configured to perform image calibration on the second direction view before performing the operation of determining a vertical distance between the corner points of the blade edges on the same side of each blade in the second direction view and a reference line as a vertical position corresponding to the corner points of the blade edges of the blade, to obtain an image calibration result, and correct the second direction view according to the image calibration result, so that a direction of a dividing line between the two blades in the second direction view is changed to a preset direction.

[0045] wherein the specific manner in which the determination module performs image calibration on the second direction view to obtain an image calibration result includes:

[0046] determining coordinate values of one or more key structure points of the nail cutter in the second direction view;

[0047] According to the coordinate values of all the key structure points, an inclination angle between a split line direction between two blades in the second direction view and a preset direction is determined as an image calibration result.

[0048] As an optional implementation, in the second aspect of the present application, the first defect parameter is determined by the determining module in the following way:

[0049] According to actual blade edge curves of the two blades extracted from the first direction view, a parallel degree of the blade edges of the two blades is determined, or according to a light transmission degree of the blade gap in the first direction view, the parallel degree of the blade edges of the two blades is determined; and / or,

[0050] For each blade, an actual blade edge curve of the blade is compared with a standard blade edge curve corresponding to the blade to obtain a smooth degree of the blade edge of the blade, the actual blade edge curve of each blade includes a blade edge curve of the blade extracted from the first direction view, or according to a light transmission degree of the blade gap in the first direction view, the smooth degree of the blade edge of the two blades is determined.

[0051] As an optional implementation, in the second aspect of the present application, for each blade, the specific way in which the determining module compares an actual blade edge curve of the blade with a standard blade edge curve corresponding to the blade to obtain a smooth degree of the blade edge of the blade includes:

[0052] The actual blade edge curve of the blade and the standard blade edge curve corresponding to the blade are placed in the same plane;

[0053] A first distance between a plurality of first pixel point pairs in the actual blade edge curve and the standard blade edge curve is determined, wherein each first pixel point pair includes one actual pixel point in the actual blade edge curve and a standard pixel point in the standard blade edge curve matched with the actual pixel point;

[0054] A discrete degree of a first distance set composed of all the first distances is determined as the smooth degree of the blade edge of the blade.

[0055] The third aspect of the present application discloses another blade defect detection device, the device comprising:

[0056] A memory storing executable program codes;

[0057] A processor coupled with the memory;

[0058] The processor invokes the executable program code stored in the memory to execute the nail clipper defect detection method disclosed in the first aspect of the present application.

[0059] The fourth aspect of the present application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are invoked, the computer instructions are used to execute the nail clipper defect detection method disclosed in the first aspect of the present application.

[0060] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0061] In the embodiments of the present application, a blade image of a nail clipper is collected, the blade image includes a first image and / or a second image, the first image includes a first direction view facing a gap between two blades of the nail clipper, and the second image includes a second direction view facing side edges of the two blades; and a defect detection parameter of the nail clipper is determined according to the blade image, and the defect detection parameter is used to represent a defect detection result of the nail clipper. It can be seen that, by implementing the present application, the defect detection parameter of the nail clipper can be analyzed according to the first direction view facing the gap between the two blades of the nail clipper and the second direction view facing the side edges of the two blades of the nail clipper, and the defect detection result can be represented by the defect detection parameter, without the need for a quality inspector to manually detect defects of the nail clipper, thereby improving the accuracy and efficiency of the defect detection of the nail clipper, and improving the intuitiveness of the defect detection result, which is conducive to improving the accuracy of the correction of the production parameters of the nail clipper. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0063] Figure 1 is a flowchart of a nail clipper defect detection method disclosed by the embodiments of the present application;

[0064] Figure 2 is a schematic diagram of a defect detection parameter determination method disclosed by the embodiments of the present application;

[0065] Figure 3 is another schematic diagram of a defect detection parameter determination method disclosed by the embodiments of the present application;

[0066] Figure 4 is another flowchart of a nail clipper defect detection method disclosed by the embodiments of the present application;

[0067] Figure 5 is a scene diagram of a nail clipper defect detection method disclosed by the embodiments of the present application;

[0068] Figure 6 is another scene schematic view of a nail clipper defect detection method disclosed by the embodiment of the present application;

[0069] Figure 7 is a structural schematic view of a nail clipper defect detection device disclosed by the embodiment of the present application;

[0070] Figure 8 is another structural schematic view of a nail clipper defect detection device disclosed by the embodiment of the present application;

[0071] Figure 9 is still another structural schematic view of a nail clipper defect detection device disclosed by the embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the personnel in the art better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor are within the scope of protection of the present application.

[0073] The terms "first", "second", and the like in the specification of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or end including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or end.

[0074] In this document, the reference to "embodiments" means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment is referred to, nor does it mean that other embodiments are mutually exclusive or alternative to the embodiments described. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0075] This invention discloses a method and apparatus for detecting defects in nail clippers. It can analyze defect detection parameters of nail clippers based on a first-direction view facing the blade seam and a second-direction view facing the side of the nail clipper blade. The defect detection results are characterized by these parameters, eliminating the need for manual inspection by quality control personnel. This improves the accuracy and efficiency of nail clipper defect detection and enhances the intuitiveness of the results, thus facilitating the correction of nail clipper production parameters. Detailed descriptions follow.

[0076] Example 1

[0077] Please see Figure 1 , Figure 1 This is a schematic flowchart of a nail clipper defect detection method disclosed in an embodiment of the present invention. Figure 1 The described nail clipper defect detection method can be applied not only to nail clipper defect detection, but also to defect detection of any interlocking structure (including but not limited to pliers, tweezers, etc.) with two parallel interlocking edges. This invention does not limit the application of this method. Figure 1 As shown, the nail clipper defect detection method may include the following operations:

[0078] 101. Collect images of the nail clipper blade.

[0079] Optionally, the blade image may include a first image and / or a second image. The first image may include a first-direction view facing the slit between the two blades of the nail clipper, and the second image may include a second-direction view facing the sides of the two blades. Further optionally, the first-direction view may include a light-transmitting image of the slit (i.e., an image of the front of the two blades facing away from the light), and the second-direction view may include a light-reflecting image of the sides of the two blades (i.e., an image of the sides of the two blades facing the light).

[0080] 102. Determine the defect detection parameters of the nail clippers based on the blade image. The defect detection parameters are used to characterize the defect detection results of the nail clippers.

[0081] Optionally, the defect detection parameters can include first defect parameters determined based on the first direction view and / or second defect parameters determined based on the second direction view, the first defect parameters can include a parallel degree of the knife edges of the two blades and / or a smooth degree of the knife edges of each blade, and the second defect parameters can include an alignment degree of the knife edge corners of the two blades on the same side, the knife edge corners including the tip of the side edge of the blade. Further optionally, the second defect parameters can further include one or more of a sharp degree of the two blades, a thickness of the two blades, etc. In this way, the parallel degree of the knife edges and the smooth degree of the knife edges of the blades are determined through the first direction view facing the knife edge gap, and the alignment degree of the knife edge corners of the blades on the same side is determined through the second direction view facing the side edge of the knife, thereby improving the comprehensiveness, diversity and accuracy of the defect detection parameter analysis.

[0082] As an optional implementation, the first defect parameters are determined in the following way:

[0083] According to the actual knife edge curve of the two blades extracted from the first direction view, the parallel degree of the knife edges of the two blades is determined, or according to the light transmission degree of the knife edge gap in the first direction view, the parallel degree of the knife edges of the two blades is determined; and / or,

[0084] For each blade, the actual knife edge curve of the blade is compared with the standard knife edge curve corresponding to the blade to obtain the smooth degree of the knife edge of the blade, the actual knife edge curve of each blade including the knife edge curve of the blade extracted from the first direction view, or the smooth degree of the knife edges of the two blades is determined according to the light transmission degree of the knife edge gap in the first direction view.

[0085] In the embodiment of the present application, the standard knife edge curve corresponding to each blade is the ideal knife edge curve when the blade has no defects.

[0086] It can be seen that the optional implementation can determine the parallel degree of the knife edges and the smooth degree of the knife edges of the two blades according to the actual knife edge curve of the two blades or the light transmission degree of the knife edge gap in the first direction view, so as to determine the parallel degree of the knife edges and the smooth degree of the knife edges of the two blades in the case of closing or not closing of the two blades, thereby improving the flexibility, diversity and accuracy of the determination method of the parallel degree of the knife edges and the smooth degree of the knife edges, and in addition, by comparing the actual knife edge curve of the blade with the standard knife edge curve, the difference between the shape of the actual knife edge of the blade and the ideal knife edge shape can be accurately analyzed, thereby improving the accuracy of the determination of the smooth degree of the knife edges.

[0087] In the optional implementation, optionally, Figure 2As shown, for each blade, comparing the actual cutting edge curve of the blade with the standard cutting edge curve corresponding to the blade to obtain the cutting edge smoothness of the blade can include:

[0088] placing the actual cutting edge curve (such as curve C2 in FIG. 1B) of the blade and the standard cutting edge curve (such as curve C1 in FIG. 1B) corresponding to the blade in the same plane; Figure 2 Figure 2

[0089] determining a first distance between each of a plurality of first pixel pairs in the actual cutting edge curve and the standard cutting edge curve, wherein each first pixel pair includes one actual pixel in the actual cutting edge curve and one standard pixel in the standard cutting edge curve that matches the actual pixel;

[0090] determining a dispersion degree of a first distance set composed of all the first distances as the cutting edge smoothness of the blade.

[0091] In the embodiment of the present application, the greater the dispersion degree of the first distance, the rougher the cutting edge of the blade, and the smaller the dispersion degree of the first distance, the smoother the cutting edge of the blade. Optionally, the dispersion degree of the first distance can be represented by one or a combination of the variance, standard deviation, average difference, heterogeneity ratio, coefficient of variation, etc. of the first distance, which is not limited in the embodiment of the present application, and preferably, the variance is used to represent the dispersion degree of the first distance.

[0092] Specifically, if the variance is used to represent the dispersion degree of the first distance, the variance of the first distance can be determined by the following method:

[0093] calculating an average value corresponding to the first distance between all the first pixel pairs:

[0094]

[0095] calculating the variance of the first distance according to the first distance and the average value between all the first pixel pairs:

[0096]

[0097] wherein S 2 is used to represent the variance, is used to represent the average value, xi is used to represent the first distance, and n is used to represent the number of the first pixel pairs.

[0098] ​​It can be seen that the optional implementation can also compare the pixel points in the actual blade edge curve and the standard blade edge curve one by one to determine the distance therebetween, and calculate the smoothness of the blade edge by calculating the dispersion degree of the distance between a plurality of pixel point pairs, which is beneficial to accurately identify the notch and the protrusion of the blade edge, and improve the accuracy of calculating the smoothness of the blade edge.

[0099] In the optional implementation, before determining the parallel degree of the blade edges of the two blades according to the light transmission degree of the blade gap in the first direction view, and / or determining the smoothness of the blade edges of the two blades according to the light transmission degree of the blade gap in the first direction view, the method can further comprise:

[0100] Determining the light transmission degree of the blade gap according to the gray value of the pixel point in the blade gap region in the first direction view.

[0101] In the optional implementation, the lower the gray value of the pixel point, the stronger the light transmission degree of the blade gap, the less parallel the blade edges of the two blades, or the rougher the blade edges.

[0102] It can be seen that this can improve the complexity of determining the parallel degree of the blade edges and the smoothness of the blade edges, and improve the efficiency of determining the parallel degree of the blade edges and the smoothness of the blade edges.

[0103] In the optional implementation, optionally, determining the parallel degree of the blade edges of the two blades according to the actual blade edge curves of the two blades extracted from the first direction view in advance can comprise:

[0104] Determining a second distance between a plurality of second pixel point pairs in the actual blade edge curves of the two blades extracted from the first direction view in advance, wherein each second pixel point pair comprises one first actual pixel point in one of the actual blade edge curves and a second actual pixel point in the other actual blade edge curve matched with the first pixel point;

[0105] Determining the parallel degree of the blade edges of the two blades according to the second distances between all second pixel point pairs.

[0106] In the optional implementation, further optionally, determining the parallel degree of the blade edges of the two blades according to the interval distances between all second pixel point pairs can comprise:

[0107] Determining the difference degree of all second distances as the parallel degree of the blade edges of the two blades.

[0108] In the embodiment of the present application, the greater the difference degree of the second distances, the more non-parallel the cutting edges of the two blades are, and the smaller the difference degree of the second distances, the more parallel the cutting edges of the two blades are. Optionally, the difference degree of the second distances can include a dispersion degree of the second distances, and the dispersion degree of the second distances can be represented by one or a combination of a variance, a standard deviation, an average difference, a heterogeneity ratio, a coefficient of variation, etc. of the second distances, which is not limited in the embodiment of the present application, and preferably, the variance is used to represent the dispersion degree of the second distances.

[0109] It can be seen that the optional implementation can also compare the pixel points in the actual cutting edge curves of the two blades one by one to determine the distances therebetween, and calculate the parallel degree of the cutting edges of the two blades by calculating the dispersion degree of the distances between the pixel point pairs, which is beneficial to accurately identifying the width and narrowness between different parts of the two blades, so as to improve the accuracy of calculating the parallel degree of the cutting edges.

[0110] In the optional implementation, optionally, the method can further include:

[0111] judging whether the extrusion force degree for extruding the two blades when the first direction view is collected is greater than or equal to a preset force degree, wherein the preset force degree is a minimum force degree for making the cutting edges of the two blades in a closed state;

[0112] when the judgment result is yes, triggering the operation of determining the parallel degree of the cutting edges of the two blades according to the light transmission degree of the cutting edge gap in the first direction view, and / or the operation of determining the smooth degree of the cutting edges of the two blades according to the light transmission degree of the cutting edge gap in the first direction view;

[0113] when the judgment result is no, triggering the operation of determining the parallel degree of the cutting edges of the two blades according to the actual cutting edge curves of the two blades extracted from the first direction view in advance, and / or the operation of comparing the actual cutting edge curve of each blade with the standard cutting edge curve corresponding to the blade to obtain the smooth degree of the cutting edge of the blade.

[0114] That is, when the extrusion force degree is greater than or equal to the preset force degree, the cutting edges of the two blades are in a closed state, and the parallel degree and the smooth degree of the cutting edges can be determined by the light transmission degree of the cutting edge gap, and when the extrusion force degree is less than the preset force degree, the cutting edges of the two blades are in a non-closed state, and the parallel degree and the smooth degree of the cutting edges can be determined by the actual cutting edge curves.

[0115] It can be seen that the closing state of the two blade edges can be determined according to the extrusion degree of the two blades, and the parallel degree and the smooth degree of the blade edges can be determined according to the closing state, so as to meet the requirement of analyzing the defect detection parameters of the blade edges in the closed and non-closed states, and further improve the accuracy and flexibility of determining the parallel degree and the smooth degree of the blade edges.

[0116] As another optional implementation, as shown in Figure 3 The second defect parameter is determined by the following method:

[0117] The vertical distance between each blade edge corner point on the same side of the two blades in the second direction view and the reference line is determined as the vertical position corresponding to the blade edge corner point, and the reference line can include any straight line perpendicular to the split line between the two blades in the second direction view.

[0118] According to the vertical positions corresponding to the blade edge corner points on the same side of the two blades, the alignment degree of the blade edge corner points on the same side of the two blades is determined as the second defect parameter.

[0119] In this optional implementation, optionally, the split line between the two blades is the center axis of the nail clipper in the second direction view, and if the two blades are symmetrical, the split line between the two blades is the symmetry axis between the two blades in the second direction view.

[0120] It can be seen that the alignment degree of the blade edge corner points can be determined according to the vertical distance between the blade edge corner points on the same side of the two blades and the standard reference line, so that the nail clipper blade misplacement defect can be identified, and the accuracy and reliability of determining the defect detection parameters are improved.

[0121] In this optional implementation, optionally, before determining the vertical distance between each blade edge corner point on the same side of the two blades in the second direction view and the reference line as the vertical position corresponding to the blade edge corner point, the method can further include:

[0122] Image calibration is performed on the second direction view to obtain an image calibration result;

[0123] The second direction view is corrected according to the image calibration result, so that the direction of the split line between the two blades in the second direction view is changed to a preset direction.

[0124] Further optionally, the image calibration on the second direction view to obtain the image calibration result can include:

[0125] The coordinate values of one or more key structure points of the nail clipper in the second direction view are determined;

[0126] According to the coordinate values of all key structural points, an inclination angle between the direction of the split line between the two blades in the second direction view and the preset direction is determined as an image calibration result.

[0127] Optionally, the preset direction is a horizontal direction or a vertical direction. Specifically, if the direction of the split line is changed to the horizontal direction, the reference line is a vertical line, and if the direction of the split line is changed to the vertical direction, the reference line is a horizontal line. In this way, it is beneficial to directly use the grid lines or the default coordinate system in the image analysis software to locate the coordinate values of the corner points of the cutting edges of the two blades. Further optionally, if the image calibration result indicates that the inclination angle between the direction of the split line and the preset direction is less than or equal to a preset angle threshold, it is not necessary to correct the second direction view.

[0128] It can be seen that by implementing the optional embodiment, the direction of the split line between the two blades in the second direction view can be changed to the preset direction through image calibration before determining the alignment degree of the corner points of the cutting edges of the two blades, thereby reducing the case that the accuracy of the defect detection parameter is low due to the inclination of the nail clippers in the second direction view.

[0129] It can be seen that by implementing the embodiments of the present application, the defect detection parameter of the nail clippers can be analyzed according to the first direction view facing the cutting edge gap of the nail clippers and the second direction view facing the side edges of the blades of the nail clippers, and the defect detection result can be represented by the defect detection parameter, without the need for manual detection of the defects of the nail clippers by the quality inspectors, thereby improving the accuracy and efficiency of the defect detection of the nail clippers, and improving the intuitiveness of the defect detection result, which is beneficial to improving the accuracy of the correction of the production parameters of the nail clippers.

[0130] Embodiment Two

[0131] Please refer to Figure 4 , Figure 4 is a flowchart of another nail clipper defect detection method disclosed by the embodiments of the present application. Among them, Figure 4 The nail clipper defect detection method described can not only be applied to the implementation of nail clipper defect detection, but also can be applied to the defect detection of any engageable structure having two mutually parallel engaging edges, including but not limited to pliers, tweezers, etc., which are not limited by the embodiments of the present application. As Figure 4 shown, the nail clipper defect detection method can include the following operations:

[0132] 201, based on a first image acquisition device facing the cutting edge gap between the two blades of the nail clippers, a gap light transmission image of the cutting edge gap is acquired as a first direction view (as Figure 5 shown), and / or, based on a second image acquisition device facing the side edges of the two blades of the nail clippers, a cutting edge side reflection image of the two blades is acquired as a second direction view (as Figure 6 shown).

[0133] Optionally, the angle between the first plane in which the lens optical axis of the first image acquisition device is located and the target plane in which the center axis of the nail cutter is located is less than a preset first plane angle threshold, and the angle between the second plane in which the lens optical axis of the second image acquisition device is located and the target plane is less than a preset second plane angle threshold, and preferably, the first plane and the second plane both overlap with the target plane.

[0134] Optionally, as shown in Figure 5 the angle between the lens optical axis of the first image acquisition device (i.e., L1) and the center axis of the nail cutter is less than a first deviation angle threshold, and preferably, the lens optical axis of the first image acquisition device is parallel to the center axis of the nail cutter, and further, the lens optical axis of the first image acquisition device overlaps with the center axis of the nail cutter, that is, the first image acquisition device is directly opposite the gap of the cutting edge. This can improve the probability that the light transmitted by the gap of the cutting edge is collected by the first image acquisition device, and improve the contrast between the gap of the cutting edge and other regions in the blade image.

[0135] Optionally, as shown in Figure 6 the angle between the lens optical axis of the second image acquisition device (i.e., L2) and the center axis of the nail cutter is greater than a second deviation angle threshold, so that the shooting range of the second image acquisition device covers the cutting edge side edges of the two blades and can receive the light reflected by the cutting edge side edges of the two blades, and preferably, the lens optical axis of the second image acquisition device is perpendicular to the center axis, and the second image acquisition device is directly opposite the cutting edge side edges of the two blades. This can improve the probability that the light reflected by the cutting edge side edges is collected by the second image acquisition device, and improve the contrast between the cutting edge side edges and other regions in the blade image.

[0136] In the embodiment of the application, optionally, the first image acquisition device and the second image acquisition device can include any device with image acquisition function, including but not limited to cameras, video cameras, mobile phones, CCDs (Charge-coupled Devices), scanners, etc., and preferably, CCDs are used as the first image acquisition device and the second image acquisition device, so as to adjust the positions of the lens optical axis, the nail cutter and the light source in real time.

[0137] 202. Determine the defect detection parameter of the nail cutter according to the blade image, the defect detection parameter being used to represent the defect detection result of the nail cutter, and the blade image can include a first direction view and / or a second direction view.

[0138] In the embodiment of the application, for other descriptions of steps 201 and 202, please refer to the detailed description of steps 101 and 102 in Embodiment I, and the embodiment of the application will not be described again.

[0139] It can be seen that by implementing the embodiments of the present application, the defect detection parameters of the nail clippers can be analyzed by collecting the gap transmission image of the gap of the nail clipper blade and the side reflection image of the side of the nail clipper blade, and the defect detection results can be characterized by the defect detection parameters, without the need for quality inspectors to manually detect the defects of the nail clippers, thereby improving the accuracy and efficiency of the defect detection of the nail clippers, and improving the intuitiveness of the defect detection results, which is conducive to improving the accuracy of the nail clipper production parameter correction, and is conducive to improving the intuitiveness of the two blade edge lines and the blade notch in the blade image, improving the contrast between the side of the blade and other areas, thereby facilitating accurate identification of the blade structure of the nail clippers, and further facilitating the convenience and accuracy of the defect detection parameter analysis.

[0140] In an optional embodiment, as shown in Figure 5 Before the gap transmission image of the gap of the blade is collected as the first direction view based on the first image collection device facing the gap between the two blades of the nail clipper, the method can further include:

[0141] The at least one first light source of the two blade sides of the nail clipper is controlled to light the gap between the two blades, so that the light spot of the first light source covers the gap, and the light rays of the first light source propagate from the inside of the nail clipper to the outside through the gap when the two blades are in a non-closed state.

[0142] It can be seen that the optional embodiment uses the light sources on both sides of the nail clipper to light the gap of the blade, so that the light spot of the light source covers the gap, which is conducive to improving the intensity of the transmitted light of the gap, further improving the contrast between the gap and other areas in the blade image, and improving the completeness of the two blade edge lines in the blade image, thereby further improving the accuracy and reliability of the defect detection of the nail clipper.

[0143] In this optional embodiment, optionally, the light ray angle between the optical axis of the first light source and the central axis of the nail clipper is greater than or equal to a first preset angle threshold and less than or equal to a second preset angle threshold, wherein the first preset angle threshold is less than the second preset angle threshold; optionally, the first preset angle threshold is the minimum angle that allows all light spots of the first light source to overlap and cover the gap between the two blades, and the second preset angle threshold is the maximum angle that allows the light rays transmitted by the gap to be collected by the first image collection device; preferably, the light ray angle between the optical axis of the first light source and the central axis of the nail clipper is equal to the first preset angle threshold. It can be seen that this can meet the requirement that the light spot of the first light source covers the gap while the optical axis of the first light source is as parallel as possible to the camera optical axis of the image collection device.

[0144] In this optional embodiment, preferably, as shown in Figure 5As shown, two first light sources (S1) can be used to illuminate the crevices of the nail clipper. The two first light sources are respectively arranged on both sides of the nail clipper. Preferably, the angles between the optical axes of the two first light sources and the central axis are equal, for example, both are 45°. This can increase the probability that the crevices of the nail clipper are covered by light spots.

[0145] In another alternative embodiment, such as Figure 6 As shown, before acquiring reflective images of the blade edges of the two nail clippers as a second directional view using a second image acquisition device facing the two blade sides, the method may further include:

[0146] A second light source controls the two sides of the nail clipper blades to illuminate the two sides of the blades so that the light spot of the second light source covers the blade edge corners of the two blade sides. The second image acquisition device for image acquisition is located on the same side of the two blades as the second light source, and the blade edge corners include the tips of the blade edge sides.

[0147] As can be seen, implementing this optional embodiment uses light sources on both sides of the nail clipper to illuminate the sides of the blade, so that the light spots of the light sources cover the sides of the blade. This helps to increase the intensity of the reflected light from the sides of the blade, further improves the contrast between the sides of the blade and other areas in the blade image, and improves the integrity of the two sides of the blade in the blade image. This helps to further improve the accuracy and reliability of nail clipper defect detection.

[0148] In this optional embodiment, optionally, such as Figure 6 As shown, the angle between the optical axis of the second light source (S2) and the central axis of the nail clipper is greater than or equal to a third preset angle threshold and less than or equal to 90°. Optionally, the third preset angle threshold is the minimum angle at which the light spot of the second light source covers the corner points of the two blade edges and the light reflected from the blade edges is received by the second image acquisition device (L2). This satisfies the requirement that the blade edges be covered by the light spot of the second light source while ensuring that the light reflected from the blade edges is as parallel as possible to the optical axis of the lens of the image acquisition device.

[0149] In this optional embodiment, optionally, an image acquisition component assembly (including a second image acquisition device and a second light source) is arranged on each side of the nail clipper, such as... Figure 6L2(a) and S2(a) are the same set of image acquisition component combination, L2(b) and S2(b) are another set of image acquisition component combination, and the two sets of image acquisition component combinations are respectively used to acquire the reflected light images of the blade edges on both sides of the nail cutter. Further, when the light ray included angle between the optical axis of the second light source and the central axis of the nail cutter is greater than or equal to the third preset angle threshold and less than the fourth preset angle threshold, the reflected light images of the blade edges on both sides can be acquired simultaneously, which can improve the efficiency of nail cutter defect detection; when the light ray included angle between the optical axis of the second light source and the central axis of the nail cutter is greater than or equal to the fourth preset angle threshold and less than or equal to 90°, the reflected light images of the blade edges on both sides of the nail cutter need to be acquired one by one, wherein the fourth preset angle threshold is the minimum angle at which the light ray of the second light source on one side of the nail cutter is collected by the second image acquisition device on the other side, which can reduce the situation that the light source on one side affects the image acquisition quality on the other side.

[0150] In yet another optional embodiment, before acquiring the first direction view and the second direction view, the method can further include: controlling the extrusion device to extrude the blade bodies of the two blades at a certain extrusion degree of force, so as to make the two blade edges close together.

[0151] Optionally, the two blade edges can be in a closed state or in a non-closed state when acquiring the first direction view, and the embodiment of the present application does not make any limitation; when acquiring the second direction view, the two blade edges can be in a non-closed state, which can reduce the situation that the blade edge angle points of the two blade edges cannot be distinguished due to the two blade edges being too close together.

[0152] In this way, by making the blade edges close together, not only can the blade image of the blade edge gap in a closed state be directly obtained, but also the close-together degree of the two blade edge edges and the close-together degree of the blade edge angle points of the blade edge side in the image acquired when the blade edge gap is in a non-closed state can be improved, thereby facilitating the comparison and analysis of the two blades and improving the analysis accuracy of the defect detection parameters.

[0153] Further, if the two blade edges need to be in a non-closed state when acquiring the first direction view, the close-together distance of the two blade edges is greater than or equal to a predetermined close-together distance threshold, and the close-together distance threshold is the minimum close-together distance at which the diffraction intensity of the blade edge gap is not greater than a predetermined diffraction intensity, and preferably, the close-together distance threshold is greater than the wavelength of the first light source. In this way, the influence of light diffraction on the blade image quality can be reduced as much as possible, which is beneficial to improve the accuracy and reliability of defect detection.

[0154] Embodiment Three

[0155] Please refer to Figure 7 , Figure 7It is a structure schematic view of a nail clippers defect detection device disclosed by the embodiment of the present application. Among them, Figure 7 The described nail clippers defect detection device can not only be applied to the implementation of nail clippers defect detection, but also can be applied to the defect detection of any occludable structure having two occlusion edges parallel to each other, including but not limited to forceps, tweezers, etc., which are not limited by the embodiment of the present application. As shown in Figure 7 The nail clippers defect detection device can include:

[0156] The acquisition module 301 is configured to acquire a blade image of the nail clippers. The blade image can include a first image and / or a second image. The first image can include a first direction view facing a blade gap between two blades of the nail clippers. The second image can include a second direction view facing side edges of the two blades.

[0157] The determination module 302 is configured to determine a defect detection parameter of the nail clippers according to the blade image. The defect detection parameter is used to represent a defect detection result of the nail clippers.

[0158] As can be seen, the embodiment Figure 7 The described device can analyze the defect detection parameter of the nail clippers according to the first direction view facing the blade gap of the nail clippers and the second direction view facing the side edges of the blades of the nail clippers, and represent the defect detection result through the defect detection parameter, without the need for the quality inspector to manually detect the defects of the nail clippers, thereby improving the accuracy and efficiency of the nail clippers defect detection, and improving the intuitiveness of the defect detection result, which is conducive to improving the accuracy of the nail clippers production parameter correction.

[0159] In an optional embodiment, as shown in Figure 7 The specific way in which the acquisition module 301 acquires the blade image of the nail clippers can include:

[0160] Based on the first image acquisition device facing the blade gap between the two blades of the nail clippers, a gap light transmission image of the blade gap is acquired as the first direction view. Optionally, the angle between the lens optical axis of the first image acquisition device and the central axis of the nail clippers is less than a first deviation angle threshold; and / or,

[0161] Based on the second image acquisition device facing the side edges of the two blades of the nail clippers, a blade gap side reflection image of the two blades is acquired as the second direction view.

[0162] As can be seen, the embodiment Figure 7The described device can also facilitate the improvement of the intuitiveness of the two-blade blade notch edge lines and blade notch in the blade image, the improvement of the contrast between the blade notch side and other areas, and the accurate identification of the notch structure of the nail clipper, thereby facilitating the convenience and accuracy of the defect detection parameter analysis, by taking the notch gap light transmission image of the notch gap as the first direction view and taking the notch side reflection image as the second direction view.

[0163] In another optional embodiment, as shown in Figure 8 The device can also include:

[0164] The light control module 303 is configured to control at least one first light source on the side of the two blades of the nail clipper to light the notch gap between the two blades before the acquisition module 301 acquires the notch gap light transmission image of the notch gap based on the first image acquisition device facing the notch gap between the two blades of the nail clipper, so that the light spot of the first light source covers the notch gap, and the light of the first light source propagates from the inside of the nail clipper to the outside through the notch gap when the two blades are in an open state.

[0165] The light control module 303 is also configured to control a second light source on the side of the two blades of the nail clipper to light the side of the two blades before the acquisition module 301 acquires the notch side reflection image of the two blades based on the second image acquisition device facing the side of the two blades of the nail clipper, so that the light spot of the second light source covers the notch corner of the side of the two blades, wherein the second image acquisition device for image acquisition and the second light source are located on the same side of the two blades, and the notch corner includes the notch side tip of the blade.

[0166] It can be seen that the implementation Figure 8 The described device uses light sources on both sides of the nail clipper to light the notch gap and the blade side, respectively, so that the light spots of the light sources cover the notch gap and the notch side, which facilitates the improvement of the intensity of the notch gap transmission light and the notch side reflection light, further improves the contrast between the notch gap and the notch side and other areas in the blade image, and improves the completeness of the two-blade notch edge and the notch side in the blade image, thereby further improving the accuracy and reliability of the nail clipper defect detection.

[0167] In yet another optional embodiment, as shown in Figure 8 The defect detection parameters can include first defect parameters determined based on the first direction view and / or second defect parameters determined based on the second direction view, the first defect parameters can include the parallelism of the notch of the two blades and / or the smoothness of the notch of each blade, and the second defect parameters can include the alignment of the notch corners on the same side of the two blades, and the notch corner includes the notch side tip of the blade.

[0168] It can be seen that the implementation Figure 8 The described device can also determine the parallel degree of the cutting edges of the two blades and the smooth degree of the cutting edges of the two blades through the first direction view facing the cutting edge gap, determine the alignment degree of the cutting edge corner points on the same side of the two blades through the second direction view facing the cutting edge side, and improve the comprehensiveness, diversity and accuracy of the defect detection parameter analysis.

[0169] In yet another optional embodiment, as Figure 8 The first defect parameter is determined by the determination module 302 in the following manner:

[0170] According to the actual cutting edge curve of the two blades extracted from the first direction view in advance, the parallel degree of the cutting edges of the two blades is determined, or according to the light transmission degree of the cutting edge gap in the first direction view, the parallel degree of the cutting edges of the two blades is determined; and / or,

[0171] For each blade, the actual cutting edge curve of the blade is compared with the standard cutting edge curve corresponding to the blade to obtain the smooth degree of the cutting edge of the blade, and the actual cutting edge curve of each blade includes the cutting edge curve of the blade extracted from the first direction view in advance, or the smooth degree of the cutting edges of the two blades is determined according to the light transmission degree of the cutting edge gap in the first direction view.

[0172] It can be seen that the implementation Figure 8 The described device can also determine the parallel degree of the cutting edges of the two blades and the smooth degree of the cutting edges of the two blades according to the actual cutting edge curve of the two blades or the light transmission degree of the cutting edge gap in the first direction view, so that the parallel degree of the cutting edges of the two blades and the smooth degree of the cutting edges of the two blades can be determined in the case of closing or not closing of the two blades, improving the flexibility, diversity and accuracy of the determination method of the parallel degree of the cutting edges and the smooth degree of the cutting edges. In addition, by comparing the actual cutting edge curve of the blade with the standard cutting edge curve, the difference between the shape of different regions of the actual cutting edge of the blade and the ideal cutting edge shape can be accurately analyzed, thereby improving the accuracy of the determination of the smooth degree of the cutting edge.

[0173] In yet another optional embodiment, as Figure 8 For each blade, the determination module 302 compares the actual cutting edge curve of the blade with the standard cutting edge curve corresponding to the blade to obtain the specific manner of determining the smooth degree of the cutting edge of the blade, which can include:

[0174] The actual cutting edge curve of the blade and the standard cutting edge curve corresponding to the blade are placed in the same plane;

[0175] Determine a first distance between multiple pairs of first pixels in the actual cutting edge curve and the standard cutting edge curve, wherein each pair of first pixels includes one actual pixel in the actual cutting edge curve and a standard pixel in the standard cutting edge curve that matches the actual pixel.

[0176] The degree of discreteness of the first distance set, which is composed of all the first distance combinations, is determined as the smoothness of the blade edge.

[0177] It is evident that implementation Figure 8 The described device can also compare the pixels in the actual cutting edge curve and the standard cutting edge curve one by one to determine the distance between them, and calculate the smoothness of the blade edge by calculating the dispersion of the distance between multiple pixel pairs, which is beneficial for accurately identifying notches and protrusions of the cutting edge and improving the accuracy of calculating the smoothness of the cutting edge.

[0178] In yet another alternative embodiment, such as Figure 8 As shown, the second defect parameter is determined by the determining module 302 in the following manner:

[0179] Determine the vertical distance between the corner point of the blade on the same side of each blade in the second direction view and the reference line, as the vertical position corresponding to the corner point of the blade. The reference line may include any straight line perpendicular to the dividing line between two blades in the second direction view.

[0180] The alignment degree of the cutting edge corners on the same side of the two blades is determined based on their vertical positions, and is used as the second defect parameter.

[0181] It is evident that implementation Figure 8 The described device can also determine the alignment of the blade corners based on the vertical distance between the corners of the blades on the same side and the standard reference line, thereby identifying nail clipper blade misalignment and improving the accuracy and reliability of determining defect detection parameters.

[0182] In yet another alternative embodiment, such as Figure 8 As shown, the determining module 302 is further configured to perform image calibration on the second direction view before performing the above-described operation of determining the vertical distance between the blade corner point on the same side of each blade in the second direction view and the reference line as the vertical position corresponding to the blade corner point, obtain the image calibration result, and correct the second direction view according to the image calibration result so that the direction of the dividing line between the two blades in the second direction view is changed to a preset direction.

[0183] The specific method by which the determining module 302 performs image calibration on the second-direction view to obtain the image calibration result may include:

[0184] determining coordinate values of one or more key structure points of the nail clipper in the second direction view;

[0185] According to the coordinate values of all the key structure points, an inclination angle between a direction of a split line between the two blades in the second direction view and a preset direction is determined as an image calibration result.

[0186] It can be seen that, by implementing the above method, the second direction view is converted into the preset direction through image calibration before determining the alignment degree of the cutting edge angle points of the two blades, so that the case that the accuracy of the defect detection parameter is reduced due to the inclination of the nail clipper in the second direction view is reduced. Figure 9 The described device can also convert the direction of the split line between the two blades in the second direction view into the preset direction through image calibration before determining the alignment degree of the cutting edge angle points of the two blades, so that the case that the accuracy of the defect detection parameter is reduced due to the inclination of the nail clipper in the second direction view is reduced.

[0187] Embodiment Four

[0188] Please refer to Figure 9 , Figure 9 is another structural schematic diagram of a nail clipper defect detection device disclosed by the embodiments of the present application. As shown in the figure, the nail clipper defect detection device can include: ​

[0189] a memory 401 storing executable program codes;

[0190] a processor 402 coupled with the memory 401;

[0191] The processor 402 invokes the executable program codes stored in the memory 401 to execute the steps in the nail clipper defect detection method described in Embodiment One or Embodiment Two of the present application.

[0192] Embodiment Five

[0193] The embodiments of the present application disclose a computer storage medium storing computer instructions, which, when invoked, are used to execute the steps in the nail clipper defect detection method described in Embodiment One or Embodiment Two of the present application.

[0194] Embodiment Six

[0195] The embodiments of the present application disclose a computer program product including a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the nail clipper defect detection method described in Embodiment One or Embodiment Two.

[0196] ​The apparatus embodiments described above are only illustrative, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0197] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0198] Finally, it should be noted that: the nail clippers defect detection method and device disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A nail clipper defect detection method characterized by, The method comprises: collecting blade images of a nail cutter, the blade images comprising a first image and a second image, the first image comprising a first direction view facing a blade gap between two blades of the nail cutter, and the second image comprising a second direction view facing side edges of the two blades; determining defect detection parameters of the nail cutter according to the blade images, the defect detection parameters being used to represent a defect detection result of the nail cutter; wherein the defect detection parameters comprise first defect parameters determined based on the first direction view and second defect parameters determined based on the second direction view, the first defect parameters comprising a parallel degree of blade edges of the two blades and / or a smooth degree of blade edges of each of the blades, and the second defect parameters comprising an alignment degree of blade corner points on the same side of the two blades, the blade corner points comprising blade edge tip points of the blades; wherein the first defect parameters are determined by: determining the parallel degree of blade edges of the two blades according to actual blade edge curves of the two blades extracted from the first direction view, or determining the parallel degree of blade edges of the two blades according to a light transmission degree of the blade gap in the first direction view; and / or, for each of the blades, comparing an actual blade edge curve of the blade with a standard blade edge curve corresponding to the blade to obtain a smooth degree of blade edges of the blade, the actual blade edge curve of each of the blades comprising a blade edge curve of the blade extracted from the first direction view, or being determined according to the light transmission degree of the blade gap in the first direction view; the second defect parameters are determined by: determining a vertical distance between each of the blade corner points on the same side of the two blades in the second direction view and a reference line as a vertical position corresponding to the blade corner point, the reference line comprising an arbitrary straight line perpendicular to a division line between the two blades in the second direction view; and determining the alignment degree of the blade corner points on the same side of the two blades according to the vertical positions corresponding to the blade corner points on the same side of the two blades.

2. The nail clipper defect detection method according to claim 1, characterized by, The collecting of the blade images of the nail cutter comprises: collecting a gap light transmission image of a blade gap between two blades of a nail cutter as a first direction view based on a first image collection device facing the blade gap, wherein an included angle between a lens optical axis of the first image collection device and a central axis of the nail cutter is less than a first deviation angle threshold; and collecting blade edge reflection images of the two blades as a second direction view based on a second image collection device facing side edges of the two blades.

3. The nail clipper defect detection method according to claim 2, characterized in that, Before the collecting of the gap light transmission image of the blade gap between the two blades of the nail cutter as the first direction view based on the first image collection device facing the blade gap, the method further comprises: controlling at least one first light source facing two side edges of the two blades of the nail cutter to light the gap between the two blades, so that the light spot of the first light source covers the gap, wherein the light of the first light source propagates from the inside of the nail cutter to the outside through the gap when the two blades are in an open state; and, before capturing the reflection image of the side edges of the gap between the two blades as the second direction view, the method further comprises: controlling a second light source facing the two side edges of the two blades to light the two side edges, so that the light spot of the second light source covers the corner points of the gap between the two blades, wherein the second image capturing device for image capturing and the second light source are located on the same side edge of the two blades, and the corner points of the gap include the sharp ends of the side edges of the gap between the two blades.

4. The nail clipper defect detection method according to any one of claims 1 to 3, characterized in that, before determining the perpendicular distance between the corner points of the gap on the same side of each blade in the second direction view and the reference line as the corresponding perpendicular position of the corner points of the gap of the blade, the method further comprises: performing image calibration on the second direction view to obtain an image calibration result; correcting the second direction view according to the image calibration result, so that the direction of the dividing line between the two blades in the second direction view is changed to a preset direction; wherein the image calibration on the second direction view to obtain the image calibration result comprises: determining the coordinate values of one or more key structure points in the second direction view; determining the inclination angle between the direction of the dividing line between the two blades in the second direction view and the preset direction as the image calibration result according to the coordinate values of all the key structure points.

5. The nail clipper defect detection method according to any one of claims 1 to 3, characterized in that, For each blade, the comparison of the actual edge curve of the gap of the blade with the corresponding standard edge curve of the gap of the blade to obtain the smoothness of the gap of the blade comprises: placing the actual edge curve of the gap of the blade and the corresponding standard edge curve of the gap of the blade in the same plane; determining the first distance between a plurality of first pixel point pairs in the actual edge curve and the standard edge curve, wherein each first pixel point pair includes one actual pixel point in the actual edge curve and one standard pixel point in the standard edge curve that matches the actual pixel point; determining the discrete degree of the first distance set composed of all the first distances as the smoothness of the gap of the blade.

6. A nail clipper defect detection apparatus characterized by comprising: The device is used to implement the nail cutter defect detection method according to any one of claims 1-5, and the device comprises: an image capturing module configured to capture blade images of a nail cutter, wherein the blade images include a first image and a second image, the first image includes a first direction view facing a gap between two blades of the nail cutter, and the second image includes a second direction view facing two side edges of the two blades; and an image calibration module configured to perform image calibration on the second direction view to obtain an image calibration result. A determining module is configured to determine a defect detection parameter of the nail clipper according to the blade image, wherein the defect detection parameter is used to represent a defect detection result of the nail clipper. The defect detection parameter includes a first defect parameter determined based on the first direction view and a second defect parameter determined based on the second direction view, the first defect parameter includes parallel degree of the blade edges of the two blades and / or smooth degree of the blade edge of each blade, and the second defect parameter includes alignment degree of the corner points of the blade edges on the same side of the two blades, wherein the corner points of the blade edges include tip points of side edges of the blade edges.

7. A nail clipper defect detection apparatus characterized by comprising: The device includes: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the method for detecting defects of a nail clipper according to any one of claims 1-5.

Images