High-precision Mark point identification method and device

Abstract

The invention discloses a high-precision Mark point identification method and device. The method comprises the following steps: acquiring original image data of a PCB (Printed Circuit Board); preprocessing the PCB original image data to obtain first image data; processing the first image data according to a model matching algorithm to generate Mark point position data; and performing precision processing on the Mark point position data by using a sub-pixel image matching algorithm to obtain second image data. The method solves the problem that in practical application, the positioning precision is not satisfactory due to the limitation of illumination variation, noise interference and a complex background of a PCB image. According to the shape analysis method, geometric features of Mark points, such as a circle or a cross, are used for identification. The method has certain robustness under the condition that the Mark points are regular in shape and simple in background. However, when the Mark point is stained, worn or partially shielded, the identification effect is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent image recognition, and in particular to a high-precision Mark point recognition method and device. Background Art

[0002] With the continuous development of intelligent technology, people are using more and more intelligent devices in their lives, work and study. The use of intelligent technology has improved the quality of people's lives and increased the efficiency of their study and work.

[0003] In the fields of electronic manufacturing, semiconductor production and printed circuit board (PCB) assembly, Mark points are widely used as alignment marks. Traditional Mark point recognition methods usually rely on high-precision optical systems and mechanical devices, but this method is costly and inflexible. With the development of computer vision and image processing technology, the use of image processing algorithms to identify and locate Mark points has become an important research direction. However, the recognition accuracy and efficiency of existing image processing methods under complex backgrounds are still insufficient. The purpose of the present invention is to provide a PCB board Mark point positioning and recognition method based on image processing, which can efficiently, accurately and customize the area to identify Mark points under complex backgrounds, and has high adaptability and low cost. The Mark point recognition method based on edge detection in the prior art usually extracts the edge features of the image and then applies a specific shape analysis algorithm to identify the Mark point. This method works well in simple environments, but in practical applications, it is limited by illumination changes, noise interference and the complex background of the PCB board image, and its positioning accuracy is often unsatisfactory. The shape analysis method uses the geometric features of the Mark point, such as a circle or a cross, for recognition. This method has a certain robustness when the Mark point shape is regular and the background is simple. However, when the mark points are stained, worn, or partially blocked, the recognition effect is significantly reduced.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a high-precision Mark point recognition method and device to at least solve the problem that in practical applications, the positioning accuracy is often unsatisfactory due to the limitations of illumination changes, noise interference, and the complex background of the PCB board image. The shape analysis method uses the geometric features of the Mark point, such as a circle or a cross, for recognition. This method has a certain robustness when the Mark point has a regular shape and a simple background. However, when the Mark point is stained, worn, or partially blocked, the recognition effect is significantly reduced.

[0006] According to one aspect of an embodiment of the present invention, a high-precision Mark point recognition method is provided, including: acquiring PCB original image data; preprocessing the PCB original image data to obtain first image data; processing the first image data according to a model matching algorithm to generate Mark point position data; and using a sub-pixel image matching algorithm to accurately process the Mark point position data to obtain second image data.

[0007] Optionally, preprocessing the PCB original image data to obtain the first image data includes: using a multi-channel and adaptive filtering algorithm to process the PCB original image data to obtain the first image data.

[0008] Optionally, the number of templates in the model matching algorithm is not less than one.

[0009] Optionally, before using the sub-pixel image matching algorithm to accurately process the Mark point position data to obtain the second image data, the method further includes: using an affine transformation model to transform the template image data in the model matching algorithm.

[0010] According to another aspect of an embodiment of the present invention, a high-precision Mark point recognition device is also provided, including: an acquisition module, used to acquire PCB original image data; a preprocessing module, used to preprocess the PCB original image data to obtain first image data; a matching module, used to process the first image data according to a model matching algorithm to generate Mark point position data; and a precision module, used to use a sub-pixel image matching algorithm to refine the Mark point position data to obtain second image data.

[0011] Optionally, the preprocessing module includes: a processing unit, used to process the PCB original image data using a multi-channel and adaptive filtering algorithm to obtain the first image data.

[0012] Optionally, the number of templates in the model matching algorithm is not less than one.

[0013] Optionally, the device further includes: a transformation module, configured to transform the template image data in the model matching algorithm using an affine transformation model.

[0014] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute a high-precision mark point recognition method.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions execute a high-precision mark point recognition method when running.

[0016] In an embodiment of the present invention, the method of acquiring original image data of PCB; preprocessing the original image data of PCB to obtain first image data; processing the first image data according to a model matching algorithm to generate Mark point position data; and using a sub-pixel image matching algorithm to accurately process the Mark point position data to obtain second image data solves the problem that in practical applications, the positioning accuracy is often unsatisfactory due to the limitations of illumination changes, noise interference, and the complex background of the PCB board image. The shape analysis method uses the geometric features of the Mark point, such as a circle or a cross, for recognition. This method has a certain robustness when the Mark point has a regular shape and a simple background. However, when the Mark point is stained, worn, or partially blocked, the technical problem that the recognition effect is significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a high-precision Mark point recognition method according to an embodiment of the present invention;

[0019] Figure 2 is a structural block diagram of a high-precision Mark point recognition device according to an embodiment of the present invention;

[0020] Figure 3 is a block diagram of a terminal device for executing a method according to an embodiment of the present invention;

[0021] Figure 4 It is a storage unit for holding or carrying a program code for implementing a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present invention, a method embodiment of a high-precision mark point recognition method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Embodiment 1

[0026] Figure 1 FIG. 1 is a flow chart of a high-precision Mark point recognition method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0027] Step S102, obtaining PCB original image data.

[0028] Step S104, preprocessing the PCB original image data to obtain first image data.

[0029] Step S106: Process the first image data according to a model matching algorithm to generate Mark point position data.

[0030] Step S108, using a sub-pixel image matching algorithm to accurately process the Mark point position data to obtain second image data.

[0031] Optionally, preprocessing the PCB original image data to obtain the first image data includes: using a multi-channel and adaptive filtering algorithm to process the PCB original image data to obtain the first image data.

[0032] Optionally, the number of templates in the model matching algorithm is not less than one.

[0033] Optionally, before using the sub-pixel image matching algorithm to accurately process the Mark point position data to obtain the second image data, the method further includes: using an affine transformation model to transform the template image data in the model matching algorithm.

[0034] Through the above embodiments, the problem that in practical applications, the positioning accuracy is often unsatisfactory due to the limitations of illumination changes, noise interference and the complex background of the PCB board image is solved. The shape analysis method uses the geometric features of the Mark point, such as a circle or a cross, for recognition. This method has a certain robustness when the Mark point has a regular shape and a simple background. However, when the Mark point is stained, worn or partially blocked, the technical problem that the recognition effect is significantly reduced.

[0035] Embodiment 2

[0036] Figure 2 is a structural block diagram of a high-precision Mark point recognition device according to an embodiment of the present invention. Figure 2 As shown, the device comprises:

[0037] The acquisition module 20 is used to acquire the original image data of the PCB.

[0038] The preprocessing module 22 is used to preprocess the PCB original image data to obtain first image data.

[0039] The matching module 24 is used to process the first image data according to a model matching algorithm to generate Mark point position data.

[0040] The precision module 26 is used to use a sub-pixel image matching algorithm to perform precision processing on the Mark point position data to obtain second image data.

[0041] Optionally, the preprocessing module includes: a processing unit, used to process the PCB original image data using a multi-channel and adaptive filtering algorithm to obtain the first image data.

[0042] Optionally, the number of templates in the model matching algorithm is not less than one.

[0043] Optionally, the device further includes: a transformation module, configured to transform the template image data in the model matching algorithm using an affine transformation model.

[0044] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute a high-precision mark point recognition method.

[0045] Specifically, the above method includes: acquiring PCB original image data; preprocessing the PCB original image data to obtain first image data; processing the first image data according to a model matching algorithm to generate Mark point position data; and using a sub-pixel image matching algorithm to refine the Mark point position data to obtain second image data. Optionally, the preprocessing of the PCB original image data to obtain the first image data includes: using a multi-channel and adaptive filtering algorithm to process the PCB original image data to obtain the first image data. Optionally, the number of templates in the model matching algorithm is not less than one. Optionally, before using a sub-pixel image matching algorithm to refine the Mark point position data to obtain the second image data, the method also includes: using an affine transformation model to transform the template image data in the model matching algorithm.

[0046] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions execute a high-precision mark point recognition method when running.

[0047] Specifically, the above method includes: acquiring PCB original image data; preprocessing the PCB original image data to obtain first image data; processing the first image data according to a model matching algorithm to generate Mark point position data; and using a sub-pixel image matching algorithm to refine the Mark point position data to obtain second image data. Optionally, the preprocessing of the PCB original image data to obtain the first image data includes: using a multi-channel and adaptive filtering algorithm to process the PCB original image data to obtain the first image data. Optionally, the number of templates in the model matching algorithm is not less than one. Optionally, before using a sub-pixel image matching algorithm to refine the Mark point position data to obtain the second image data, the method also includes: using an affine transformation model to transform the template image data in the model matching algorithm.

[0048] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0049] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0051] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] in addition, Figure 3 This is a schematic diagram of the hardware structure of a terminal device provided in one embodiment of the present application. Figure 3 As shown, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33 and at least one communication bus 34. The communication bus 34 is used to realize the communication connection between the components. The memory 33 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory. Various programs can be stored in the memory 33 to complete various processing functions and implement the method steps of this embodiment.

[0053] Optionally, the processor 31 may be implemented as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 via a wired or wireless connection.

[0054] Optionally, the input device 30 may include multiple input devices, for example, it may include at least one of a user interface for users, a device interface for devices, a programmable interface for software, a camera, and a sensor. Optionally, the device interface for devices may be a wired interface for data transmission between devices, or a hardware insertion interface for data transmission between devices (such as a USB interface, a serial port, etc.); Optionally, the user interface for users may be, for example, a control button for users, a voice input device for receiving voice input, and a touch sensing device for users to receive user touch input (such as a touch screen with a touch sensing function, a touch pad, etc.); Optionally, the programmable interface for the software may be, for example, an entry for users to edit or modify programs, such as an input pin interface or an input interface of a chip; Optionally, the transceiver may be a radio frequency transceiver chip with communication function, a baseband processing chip, and a transceiver antenna, etc. Audio input devices such as microphones may receive voice data. The output device 32 may include output devices such as displays and speakers.

[0055] In this embodiment, the processor of the terminal device includes functions for executing each module of the data processing device in each device. The specific functions and technical effects can be referred to the above embodiments and will not be repeated here.

[0056] Figure 4 A schematic diagram of the hardware structure of a terminal device provided in another embodiment of the present application. Figure 4 Yes Figure 3 A specific embodiment in the implementation process. Figure 4 As shown, the terminal device of this embodiment includes a processor 41 and a memory 42.

[0057] The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above embodiment.

[0058] The memory 42 is configured to store various types of data to support operations on the terminal device. Examples of such data include instructions for any application or method used to operate on the terminal device, such as messages, pictures, videos, etc. The memory 42 may include a random access memory (RAM) and may also include a non-volatile memory, such as at least one disk storage.

[0059] Optionally, the processor 41 is provided in the processing component 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input / output interface 47 and / or a sensor component 48. The specific components included in the terminal device are set according to actual needs, and this embodiment does not limit this.

[0060] The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 40 may include one or more modules to facilitate the interaction between the processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate the interaction between the multimedia component 45 and the processing component 40.

[0061] The power supply component 44 provides power to various components of the terminal device. The power supply component 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the terminal device.

[0062] The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0063] The audio component 46 is configured to output and / or input audio signals. For example, the audio component 46 includes a microphone (MIC), and when the terminal device is in an operating mode, such as a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 42 or sent via the communication component 43. In some embodiments, the audio component 46 also includes a speaker for outputting audio signals.

[0064] The input / output interface 47 provides an interface between the processing component 40 and the peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include but are not limited to: volume buttons, start buttons, and lock buttons.

[0065] The sensor assembly 48 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor assembly 48 can detect the open / closed state of the terminal device, the relative positioning of the components, and the presence or absence of contact between the user and the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera, etc.

[0066] The communication component 43 is configured to facilitate wired or wireless communication between the terminal device and other devices. The terminal device can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card, so that the terminal device can log in to the GPRS network and establish communication with the service end through the Internet.

[0067] From the above, we can see that Figure 4 The communication component 43, the audio component 46, the input / output interface 47, and the sensor component 48 involved in the embodiment can all be used as Figure 3 Implementation method of the input device in the embodiment.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0070] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0072] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A high-precision Mark point recognition method, characterized in that: include: Get the original image data of PCB; Preprocessing the PCB original image data to obtain first image data; Processing the first image data according to a model matching algorithm to generate Mark point position data; The Mark point position data is processed precisely using a sub-pixel image matching algorithm to obtain second image data.

2. The method according to claim 1, characterized in that The preprocessing of the PCB original image data to obtain the first image data comprises: The PCB original image data is processed using a multi-channel and adaptive filtering algorithm to obtain the first image data.

3. The method according to claim 1, characterized in that The number of templates in the model matching algorithm is not less than one.

4. The method according to claim 1, characterized in that: Before the Mark point position data is processed accurately by using a sub-pixel image matching algorithm to obtain the second image data, the method further includes: The template image data in the model matching algorithm is transformed using an affine transformation model.

5. A high-precision Mark point recognition device, characterized in that: include: An acquisition module is used to acquire the original image data of the PCB; A preprocessing module, used for preprocessing the original image data of the PCB to obtain first image data; A matching module, used to process the first image data according to a model matching algorithm to generate Mark point position data; The precision module is used to use a sub-pixel image matching algorithm to precisely process the Mark point position data to obtain second image data.

6. The device according to claim 5, characterized in that The preprocessing module comprises: The processing unit is used to process the PCB original image data by adopting a multi-channel and adaptive filtering algorithm to obtain the first image data.

7. The device according to claim 5, characterized in that The number of templates in the model matching algorithm is not less than one.

8. The device according to claim 5, characterized in that The device also includes: The transformation module is used to transform the template image data in the model matching algorithm using an affine transformation model.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein the program controls the device where the non-volatile storage medium is located to execute the method according to any one of claims 1 to 4 when the program is executed.

10. An electronic device, characterized in that: It comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method described in any one of claims 1 to 4 when executed.

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