CN117197036B - Image detection method and device - Google Patents
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Abstract
The embodiment of the specification provides an image detection method and device, wherein the image detection method comprises the following steps: obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. The method comprises the steps of obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. Thereby improving the detection accuracy of the cutter gap.
Description
Technical Field
The embodiment of the specification relates to the technical field of image processing, in particular to an image detection method.
Background
The cutting of the battery pole piece is an important part of the production process of the power battery of the new energy automobile, and the size and the number of burrs generated by the battery pole piece are influenced by the notch of the cutting tool. The cutting tool can generate openings with different sizes and shapes due to unqualified grinding, installation, use and the like. The pole piece cutting process requires a micron-sized high-precision standard, and a cutting tool exceeding a notch threshold (such as a length or a depth of 5 microns) is used for cutting, so that the pole piece generates larger burrs, and the produced battery has extremely high short circuit risk due to the larger burrs of the pole piece. At present, an artificial naked eye is commonly adopted in the industry to observe a cutter opening image on an optical microscope, and the method has the defects of low detection efficiency, high labor cost, low reliability of detection results and the like, so that the power battery production workshop is urgently required to screen the cutter to be abandoned rapidly with high precision by the cutter opening detection method, and the production quality is improved.
Disclosure of Invention
In view of this, the present embodiment provides an image detection method. One or more embodiments of the present specification relate to an image detection apparatus, a computing device, a computer-readable storage medium, and a computer program that solve the technical drawbacks existing in the prior art.
According to a first aspect of embodiments of the present specification, there is provided an image detection method, including:
acquiring a target edge image to be detected, and determining at least two peak points according to the target edge image;
dividing the target edge image into a plurality of image groups according to at least two peak points;
constructing an envelope fitting equation based on the plurality of image groups, and determining an initial edge image according to the envelope fitting equation;
and detecting opening information according to the initial edge image and the target edge image.
In one possible implementation, determining at least two peak points from the target edge image includes:
acquiring at least two edge points in the target edge image, and matching the edge points with the peak point determination conditions to obtain a matching result;
and determining at least two peak points from the at least two edge points according to the matching result.
In one possible implementation, dividing the target edge image into a plurality of image groups according to at least two peak points includes:
determining an initial peak point from the at least two peak points, and determining a target peak point adjacent to the initial peak point from the at least two peak points;
and determining an image group according to the initial peak point and the target peak point.
In one possible implementation, constructing an envelope fit equation based on the plurality of image sets and determining an initial edge image from the envelope fit equation includes:
determining a spline function according to the plurality of image groups;
determining spline curve coefficients according to spline functions;
establishing an envelope fitting equation according to spline curve coefficients;
an initial edge image is determined from the envelope fitting equation.
In one possible implementation, detecting the gap information from the initial edge image and the target edge image includes:
determining a target area of the opening according to the initial edge image and the target edge image;
determining a correlation peak point corresponding to the target area, and determining the position information of the notch according to the correlation peak point;
and determining the opening information according to the position information of the opening.
In one possible implementation manner, determining the position information of the gap according to the correlation peak value point includes:
and performing binary search on the target edge image according to the correlation peak point to determine the position information of the notch.
In one possible implementation, determining the opening information according to the position information of the opening includes:
determining a target depth and a target length according to the position information of the notch;
and determining the area information of the notch according to the target depth and the target length.
According to a second aspect of embodiments of the present specification, there is provided an image detection apparatus comprising:
the image acquisition module is configured to acquire a target edge image to be detected, and at least two peak points are determined according to the target edge image;
an image dividing module configured to divide the target edge image into a plurality of image groups according to at least two peak points;
an image determination module configured to construct an envelope fit equation based on the plurality of image sets and determine an initial edge image from the envelope fit equation;
and the image detection module is configured to detect the opening information according to the initial edge image and the target edge image.
According to a third aspect of embodiments of the present specification, there is provided a computing device comprising:
a memory and a processor;
the memory is used for storing computer executable instructions and the processor is used for executing the computer executable instructions, and the computer executable instructions realize the steps of the image detection method when being executed by the processor.
According to a fourth aspect of embodiments of the present specification, there is provided a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the steps of the above-described image detection method.
According to a fifth aspect of embodiments of the present specification, there is provided a computer program, wherein the computer program, when executed in a computer, causes the computer to perform the steps of the above-described image detection method.
The embodiment of the specification provides an image detection method and device, wherein the image detection method comprises the following steps: obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. The method comprises the steps of obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. Thereby improving the detection accuracy of the cutter gap.
Drawings
Fig. 1 is a schematic view of a scene of an image detection method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of an image detection method according to one embodiment of the present disclosure;
FIG. 3 is a hardware system topology diagram of an image detection method according to an embodiment of the present disclosure;
FIG. 4 is a schematic view of edge detection of a tool according to one embodiment of the present disclosure;
fig. 5 is a schematic diagram of a gap detection result of an image detection method according to an embodiment of the present disclosure;
fig. 6 is a schematic structural view of an image detection device according to an embodiment of the present disclosure;
FIG. 7 is a block diagram of a computing device provided in one embodiment of the present description.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many other forms than described herein and similarly generalized by those skilled in the art to whom this disclosure pertains without departing from the spirit of the disclosure and, therefore, this disclosure is not limited by the specific implementations disclosed below.
The terminology used in the one or more embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the specification. As used in this specification, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
In the present specification, an image detection method is provided, and the present specification relates to an image detection apparatus, a computing device, and a computer-readable storage medium, which are described in detail in the following embodiments one by one.
Referring to fig. 1, fig. 1 is a schematic view of a scene of an image detection method according to an embodiment of the present disclosure.
In the application scenario of fig. 1, the computing device 101 may acquire a target edge image 102 to be detected. The computing device 101 may then determine at least two peak points 103 from the target edge image 102. Thereafter, the computing device 101 divides the target edge image into a plurality of image groups 104 according to at least two peak points 103. The computing device 101 constructs an envelope fit equation 105 based on the plurality of image sets 104. And an initial edge image 106 is determined according to the envelope fitting equation 105. Finally, the computing device 101 may detect the breach information 107 from the initial edge image 106 and the target edge image 102.
The computing device 101 may be hardware or software. When the computing device 101 is hardware, it may be implemented as a distributed cluster of multiple servers or terminal devices, or as a single server or single terminal device. When the computing device 101 is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
Referring to fig. 2, fig. 2 shows a flowchart of an image detection method according to an embodiment of the present disclosure, which specifically includes the following steps.
Step 201: and acquiring a target edge image to be detected, and determining at least two peak points according to the target edge image.
In practical applications, to acquire the edge image of the slitting tool, a complete image acquisition system is generally used, which mainly comprises three parts, namely a graphics workstation, an optical imaging system and a motion control system, and the specific topological structure is shown in fig. 3. The graphic workstation sends a control signal through system software, the control signal is transmitted to the servo controller through the motion control card, and finally, a motor is driven by the servo controller to finish a motion instruction. The motion control system consists of a motion control card, three servo controllers and a motor, mainly controls the focusing of the imaging system, the assembling and disassembling of the cutter and the rotation of the cutter, and the optical imaging system mainly comprises an industrial camera, a detection lens, a parallel light source and a light source controller. The cutter edge image obtained by the acquisition system is shown in fig. 4. The darker areas are edges of the slitting tool, and the areas with higher gray values are image backgrounds. The square mark is the opening of the slitting tool, and the image is the main detection object of the invention.
Furthermore, as the opening of the slitting tool has a unidirectional reverse growth rule, the initial edge and the actual edge of the cutting tool are enveloped. And by pre-reasoning the initial edge of the cutter and calculating the numerical integration between the initial edge and the actual edge, the opening area of the edge of the slitting cutter exceeding the defect threshold can be accurately positioned. However, envelope fitting often occurs because of lack of fitting information, overshooting, undershooting, and end point flying wing [7-8] And the like, which will seriously affect the accuracy of the detection of the opening of the slitting tool. In order to solve the problems, the embodiment of the specification improves the detection method of the envelope fitting opening, enhances the characteristic information of the initial edge in the image of the slitting tool through cubic spline polynomial interpolation, avoids the envelope fitting problem existing in the process of pre-reasoning the initial edge of the slitting tool, and improves the accuracy of the opening detection of the slitting tool.
In one possible implementation, determining at least two peak points from the target edge image includes: at least two edge points in the target edge image are obtained, the edge points are matched with the peak point determining conditions to obtain a matching result, and at least two peak points are determined from the at least two edge points according to the matching result.
In particular, there are knife edgesPeak points, peak points satisfying the conditionThen a plurality of peak points may be determined from the target edge image.
For example, if it is set that there are 3 peak points, 3 peak points satisfying the condition from the target edge image. A, B and C, respectively.
Step 202: the target edge image is divided into a plurality of image groups according to at least two peak points.
In one possible implementation, dividing the target edge image into a plurality of image groups according to at least two peak points includes: an initial peak point is determined from the at least two peak points, a target peak point adjacent to the initial peak point is determined from the at least two peak points, and an image set is determined from the initial peak point and the target peak point.
In particular, the cutter edge is provided withPeak points, dividing the edge into +.>Combination of two or more kinds of materialsPeak point satisfies the condition->And the cutter edge curve is smooth, i.e. +.>Continuous.
For example, if it is set that there are 3 peak points, 3 peak points satisfying the condition from the target edge image. A, B and C, respectively. Then the image can be divided into AB and BC segments.
Step 203: an envelope fit equation is constructed based on the plurality of image sets and an initial edge image is determined from the envelope fit equation.
In one possible implementation, constructing an envelope fit equation based on the plurality of image sets and determining an initial edge image from the envelope fit equation includes: determining a spline function according to the plurality of image groups, determining spline curve coefficients according to the spline function, establishing an envelope fitting equation according to the spline curve coefficients, and determining an initial edge image according to the envelope fitting equation.
In particular, the spline function may be constructed as follows:
(1)
because the initial edge of the reasoning cutter accords with the fixed boundary condition, the end points are respectively set asAnd->I.e.Then:
(2)
let the step length be，/>The method can be deduced:
(3)
the linear equation is performedDecomposing into a unit lower triangular matrix and a unit upper triangular matrix, and calculating spline curve coefficients as follows:
(4)
peak intervals at each tool edgeIn which an envelope fitting equation can be established:
(5)
wherein the fitted edge envelope of the slitting tool is the pre-inferred initial edge of the slitting tool.
Step 204: and detecting opening information according to the initial edge image and the target edge image.
In one possible implementation, detecting the gap information from the initial edge image and the target edge image includes: and determining a target area of the opening according to the initial edge image and the target edge image, determining a correlation peak point corresponding to the target area, determining position information of the opening according to the correlation peak point, and determining opening information according to the position information of the opening.
In practical application, main area detection of different openings can be realized by calculating the distance between the initial edge and the actual edge and performing threshold screening.
In one possible implementation manner, determining the position information of the gap according to the correlation peak value point includes: and performing binary search on the target edge image according to the correlation peak point to determine the position information of the notch.
In practical application, binary search is carried out on peak points near the main area of the opening by designing a growth operator, and the position of the complete opening area is obtained through calculation.
For example, a specific algorithm is as follows:
in one possible implementation, determining the opening information according to the position information of the opening includes: and determining the target depth and the target length according to the position information of the opening, and determining the area information of the opening according to the target depth and the target length.
In practical application, the maximum depth and the maximum length of the opening can be obtained according to the identification result of the opening area. The calculation of the opening area of the slitting tool can be converted into the calculation of the difference between the trapezoid integral of the pre-reasoning edge and the actual edge, and the Riemann sum of n continuous small trapezoids approximates the opening area. Is provided with a notch area inWithin the range, the edge curve function is +.>The trapezoidal integral is:
(6)
in order to verify the accuracy of the gap detection algorithm designed in the embodiment of the specification, a PReNet detection model with relatively good cutter detection results is selected for comparison experiments. And randomly selecting a plurality of cutting tool edge image samples containing the gaps from the cutting tool edge image data set, detecting and analyzing the maximum length, the maximum depth and the maximum area of the gaps by using the two methods, and the experimental results are shown in table 1.
TABLE 1
Further, after the method of the embodiment of the specification identifies the main area of the opening, a growth operator is designed to search nearby peak points by two branches, so that the opening identification area is optimized. Therefore, the maximum length value of the gap calculated by the embodiment of the specification is closer to the actually marked Ref value than the PReNet, and the maximum length detection error is controlled within 2.7 percent. The method of the embodiment of the specification effectively restores the reverse growth process of the notch through pre-reasoning of the initial edge of the slitting tool. Therefore, in the maximum depth detection of the notch, the maximum depth result detected by the method in the embodiment of the specification is closer to the actually marked Ref value, and the maximum depth detection error is controlled within 2.2%. The detection results of the notch 5 and the notch 8 show that the method in the embodiment of the specification accurately restores the depth of the notch, and the error is 0. In the detection of the opening area, the detection accuracy of the opening length and the opening depth is higher, and the opening area detected by the method in the embodiment of the specification is closer to the marked Ref value.
In order to verify the high efficiency of the gap detection algorithm designed in the embodiment of the specification, a PReNet training model with the model size and the parameter quantity of 665.9KB and 0.169M is selected for comparison verification experiments. 400 pictures are randomly selected in the slitting tool image dataset, repeated experiments are carried out in 8 groups, the detection time consumption of single images is counted, and the experimental results are shown in table 2. According to analysis, the detection time consumption of the method of the embodiment of the specification is shortened by more than 40% on the basis of a PReNet training model, so that the method of the embodiment of the specification is more efficient.
TABLE 2
In order to intuitively show the detection result of the opening, in the embodiment of the present disclosure, 4 edge graphs of the cutter are randomly selected in the data set to detect, and the detection effect graph is shown in fig. 5, where N, L, D, A in the graph respectively represents the number of the openings, the maximum opening length, the maximum opening depth and the maximum opening area. The first row is a cutter edge image, the second row is an effect diagram of edge extraction and envelope fitting by the method of the embodiment of the present specification, wherein the red solid line is the cutter edge extraction result, and the dotted line is the initial edge of the slitting cutter pre-inferred by using the method of the embodiment of the present specification. Third and fourth rows show the detection results of the method and the PReNet model according to the embodiment of the present disclosure. In (a), (b) and (c), the partial gap is in a shallow depth characteristic, so that a great amount of omission occurs in PReNet, but the omission does not occur in the method of the embodiment of the specification. According to analysis, the method of the embodiment of the specification adopts a mode of pre-reasoning the initial edge of the cutter to detect the notch, so that the condition of missing detection of the notch with low shallow depth is effectively avoided. The problem that the notch detection result area is concave and the problem that the notch end point is convex are solved by optimizing the pre-reasoning edge by means of the cubic spline interpolation operator, so that the upper boundary of the final notch detection area is a smooth curve which accords with the overall edge trend. Thereby improving the problem of small gap depth and gap area in the PReNet-like model detection result. By combining the results and analysis, the method disclosed by the embodiment of the specification realizes more efficient detection of the opening and better effect.
In summary, the embodiments of the present disclosure provide an image detection method and apparatus, where the image detection method includes: obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. The method comprises the steps of obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. The problems of low detection precision, low reliability of detection results and low overall detection speed of the cutting tool opening are solved, and the precision of a cutting tool opening detection system is improved. And the initial edge of the inference tool is improved by introducing cubic spline polynomial interpolation, so that the detection accuracy of the opening area, depth and width is improved, and the sensitivity to small openings is higher. Furthermore, the original edge of the cutter is completely restored, and the reliability and the stability of the cutter gap high-precision detection system are enhanced on the premise of considering the calculated amount and the precision. The detection of the opening can be realized by only occupying a small amount of CPU computer resources, which is beneficial to the floor of the actual production workshop.
Corresponding to the above method embodiments, the present disclosure further provides an image detection apparatus embodiment, and fig. 6 shows a schematic structural diagram of an image detection apparatus provided in one embodiment of the present disclosure. As shown in fig. 6, the apparatus includes:
an image acquisition module 601 configured to acquire a target edge image to be detected, and determine at least two peak points according to the target edge image;
an image division module 602 configured to divide the target edge image into a plurality of image groups according to at least two peak points;
an image determination module 603 configured to construct an envelope fit equation based on the plurality of image sets and determine an initial edge image from the envelope fit equation;
the image detection module 604 is configured to detect the notch information from the initial edge image and the target edge image.
In one possible implementation, the image acquisition module 601 is further configured to:
acquiring at least two edge points in the target edge image, and matching the edge points with the peak point determination conditions to obtain a matching result;
and determining at least two peak points from the at least two edge points according to the matching result.
In one possible implementation, the image partitioning module 602 is further configured to:
determining an initial peak point from the at least two peak points, and determining a target peak point adjacent to the initial peak point from the at least two peak points;
and determining an image group according to the initial peak point and the target peak point.
In one possible implementation, the image determination module 603 is further configured to:
determining a spline function according to the plurality of image groups;
determining spline curve coefficients according to spline functions;
establishing an envelope fitting equation according to spline curve coefficients;
an initial edge image is determined from the envelope fitting equation.
In one possible implementation, the image detection module 604 is further configured to:
determining a target area of the opening according to the initial edge image and the target edge image;
determining a correlation peak point corresponding to the target area, and determining the position information of the notch according to the correlation peak point;
and determining the opening information according to the position information of the opening.
In one possible implementation, the image detection module 604 is further configured to:
and performing binary search on the target edge image according to the correlation peak point to determine the position information of the notch.
In one possible implementation, the image detection module 604 is further configured to:
determining a target depth and a target length according to the position information of the notch;
and determining the area information of the notch according to the target depth and the target length.
The embodiment of the specification provides an image detection method and device, wherein the image detection device comprises: obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. The method comprises the steps of obtaining a target edge image to be detected, determining at least two peak points according to the target edge image, dividing the target edge image into a plurality of image groups according to the at least two peak points, constructing an envelope fitting equation based on the plurality of image groups, determining an initial edge image according to the envelope fitting equation, and detecting opening information according to the initial edge image and the target edge image. Thereby improving the detection accuracy of the cutter gap.
The above is a schematic solution of an image detection apparatus of the present embodiment. It should be noted that, the technical solution of the image detection device and the technical solution of the image detection method belong to the same conception, and details of the technical solution of the image detection device, which are not described in detail, can be referred to the description of the technical solution of the image detection method.
Fig. 7 illustrates a block diagram of a computing device 700 provided in accordance with one embodiment of the present description. The components of computing device 700 include, but are not limited to, memory 710 and processor 720. Processor 720 is coupled to memory 710 via bus 730, and database 750 is used to store data.
Computing device 700 also includes access device 740, access device 740 enabling computing device 700 to communicate via one or more networks 760. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, local Area Network), wide area networks (WAN, wide Area Network), personal area networks (PAN, personal Area Network), or combinations of communication networks such as the internet. The access device 740 may include one or more of any type of network interface, wired or wireless, such as a network interface card (NIC, network interface controller), such as an IEEE802.11 wireless local area network (WLAN, wireless Local Area Network) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, near field communication (NFC, near Field Communication).
In one embodiment of the present description, the above-described components of computing device 700, as well as other components not shown in FIG. 7, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 7 is for exemplary purposes only and is not intended to limit the scope of the present description. Those skilled in the art may add or replace other components as desired.
Computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 500 may also be a mobile or stationary server.
Wherein the processor 520 is configured to execute computer-executable instructions that, when executed by the processor, perform the steps of the data processing method described above. The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the image detection method belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the image detection method.
An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the image detection method described above.
The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the image detection method belong to the same concept, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the image detection method.
An embodiment of the present specification also provides a computer program, wherein the computer program, when executed in a computer, causes the computer to perform the steps of the image detection method described above.
The above is an exemplary version of a computer program of the present embodiment. It should be noted that, the technical solution of the computer program and the technical solution of the image detection method belong to the same conception, and details of the technical solution of the computer program, which are not described in detail, can be referred to the description of the technical solution of the image detection method.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The computer instructions include computer program code that may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the embodiments are not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the embodiments of the present disclosure. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the embodiments described in the specification.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The preferred embodiments of the present specification disclosed above are merely used to help clarify the present specification. Alternative embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the teaching of the embodiments. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. This specification is to be limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. An image detection method, comprising:
acquiring a target edge image to be detected, and determining at least two peak points according to the target edge image;
dividing the target edge image into a plurality of image groups according to the at least two peak points;
constructing an envelope fitting equation based on the plurality of image groups, and determining an initial edge image according to the envelope fitting equation;
detecting opening information according to the initial edge image and the target edge image;
the dividing the target edge image into a plurality of image groups according to the at least two peak points includes:
determining an initial peak point from the at least two peak points, and determining a target peak point adjacent to the initial peak point from the at least two peak points;
determining an image group according to the initial peak point and the target peak point;
the constructing an envelope fitting equation based on the plurality of image groups, and determining an initial edge image according to the envelope fitting equation, includes:
determining a spline function according to the plurality of image groups;
determining spline curve coefficients according to the spline function;
establishing the envelope fitting equation according to the spline curve coefficient;
determining the initial edge image according to the envelope fitting equation;
the detecting the opening information according to the initial edge image and the target edge image includes:
determining a target area of the notch according to the initial edge image and the target edge image;
determining a correlation peak point corresponding to the target area, and determining the position information of the notch according to the correlation peak point;
and determining the opening information according to the position information of the opening.
2. The method of claim 1, wherein said determining at least two peak points from said target edge image comprises:
acquiring at least two edge points in the target edge image, and matching the edge points with the peak point determination conditions to obtain a matching result;
and determining at least two peak points from the at least two edge points according to the matching result.
3. The method of claim 1, wherein the determining the location information of the gap from the correlation peak point comprises:
and performing binary search on the target edge image according to the correlation peak point, and determining the position information of the notch.
4. The method of claim 1, wherein the determining the breach information based on the location information of the breach comprises:
determining a target depth and a target length according to the position information of the notch;
and determining the area information of the gap according to the target depth and the target length.
5. An image detection apparatus, comprising:
the image acquisition module is configured to acquire a target edge image to be detected, and at least two peak points are determined according to the target edge image;
an image dividing module configured to divide the target edge image into a plurality of image groups according to the at least two peak points;
an image determination module configured to construct an envelope fit equation based on the plurality of image sets and determine an initial edge image from the envelope fit equation;
an image detection module configured to detect opening information from the initial edge image and the target edge image;
the dividing the target edge image into a plurality of image groups according to the at least two peak points includes:
determining an initial peak point from the at least two peak points, and determining a target peak point adjacent to the initial peak point from the at least two peak points;
determining an image group according to the initial peak point and the target peak point;
the constructing an envelope fitting equation based on the plurality of image groups, and determining an initial edge image according to the envelope fitting equation, includes:
determining a spline function according to the plurality of image groups;
determining spline curve coefficients according to the spline function;
establishing the envelope fitting equation according to the spline curve coefficient;
determining the initial edge image according to the envelope fitting equation;
the detecting the opening information according to the initial edge image and the target edge image includes:
determining a target area of the notch according to the initial edge image and the target edge image;
determining a correlation peak point corresponding to the target area, and determining the position information of the notch according to the correlation peak point;
and determining the opening information according to the position information of the opening.
6. A computing device, comprising:
a memory and a processor;
the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which when executed by the processor, implement the steps of the image detection method of any one of claims 1 to 4.
7. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the image detection method of any one of claims 1 to 4.
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