CN114202526A - Quality detection method, system, apparatus, electronic device, and medium - Google Patents

Quality detection method, system, apparatus, electronic device, and medium Download PDF

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Publication number
CN114202526A
CN114202526A CN202111514311.6A CN202111514311A CN114202526A CN 114202526 A CN114202526 A CN 114202526A CN 202111514311 A CN202111514311 A CN 202111514311A CN 114202526 A CN114202526 A CN 114202526A
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China
Prior art keywords
target object
image
target
quality detection
storage area
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CN202111514311.6A
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Chinese (zh)
Inventor
余志良
吕雪莹
赵乔
罗倩慧
蒋佳军
赖宝华
陈泽裕
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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Priority to CN202111514311.6A priority Critical patent/CN114202526A/en
Publication of CN114202526A publication Critical patent/CN114202526A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • B25J9/1697—Vision controlled systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]

Abstract

The present disclosure provides a quality detection method, system, apparatus, electronic device, medium, and program product, which relate to the field of artificial intelligence, and in particular to the technical fields of computer vision, image processing, quality detection, and the like. The quality detection method comprises the following steps: acquiring an initial image for a storage area; performing image processing on the initial image to obtain position information of the target object in the storage area; acquiring a target image for the target object based on the position information; and carrying out image processing on the target image to obtain a quality detection result aiming at the target object.

Description

Quality detection method, system, apparatus, electronic device, and medium
Technical Field
The present disclosure relates to the field of artificial intelligence, in particular to the technical fields of computer vision, image processing, quality detection, and the like, and more particularly, to a quality detection method, system, apparatus, electronic device, medium, and program product.
Background
In order to determine whether the target object has quality problems, quality detection of the target object is generally required. The target object includes, for example, a product, an article, and the like. However, the related art has low accuracy and high cost for quality detection of the target object.
Disclosure of Invention
The present disclosure provides a quality detection method, system, apparatus, electronic device, medium, and program product.
According to an aspect of the present disclosure, there is provided a quality detection method including: acquiring an initial image for a storage area; performing image processing on the initial image to obtain position information of a target object in the storage area; acquiring a target image for the target object based on the position information; and carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
According to another aspect of the present disclosure, there is provided a quality detection system including: the image capturing device comprises electronic equipment, a first image capturing device, a grabbing device and a second image capturing device. The first image acquisition device is in communication connection with the electronic equipment and is configured to acquire an initial image aiming at a storage area under the control of the electronic equipment; the grabbing device is in communication connection with the electronic equipment and is configured to grab a target object from the storage area based on the position information of the target object in the storage area under the control of the electronic equipment; the second image acquisition device is in communication connection with the electronic equipment and is configured to acquire a target image aiming at the target object under the control of the electronic equipment; wherein the electronic device is configured to perform: acquiring an initial image for a storage area acquired by the first image acquisition device; performing image processing on the initial image to obtain position information of the target object in the storage area; controlling the grabbing device to grab the target object from the storage area based on the position information; acquiring a target image for the target object acquired by the second image acquisition device; and carrying out image processing on the target image to obtain a quality detection result aiming at the target object. .
According to another aspect of the present disclosure, there is provided a quality detection apparatus including: the device comprises a first acquisition module, a first processing module, a second acquisition module and a second processing module. The first acquisition module is used for acquiring an initial image aiming at the storage area; the first processing module is used for carrying out image processing on the initial image to obtain the position information of the target object in the storage area; a second obtaining module, configured to obtain a target image for the target object based on the position information; and the second processing module is used for carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the quality detection method described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the above-described quality detection method.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the quality detection method described above.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 schematically illustrates a flow diagram of a quality detection method according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow diagram of a quality detection method according to another embodiment of the present disclosure;
FIG. 3 schematically illustrates a schematic diagram of a quality detection method according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a schematic diagram of a quality detection system according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a quality detection apparatus according to an embodiment of the present disclosure; and
FIG. 6 is a block diagram of an electronic device for performing quality detection used to implement an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
An embodiment of the present disclosure provides a quality detection method, including: and acquiring an initial image aiming at the storage area, and performing image processing on the initial image to obtain the position information of the target object in the storage area. Then, based on the position information, a target image for the target object is acquired. And then, carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
The disclosed embodiments provide a quality detection method, and a quality detection method according to an exemplary embodiment of the present disclosure is described with reference to fig. 1 to 3.
Fig. 1 schematically shows a flow chart of a quality detection method according to an embodiment of the present disclosure.
As shown in fig. 1, the quality detection method 100 of the embodiment of the present disclosure may include, for example, operations S110 to S140. The quality detection method 100 of the disclosed embodiments may be performed, for example, by an electronic device, which may include a computer, a server, and the like.
In operation S110, an initial image for a storage region is acquired.
In operation S120, image processing is performed on the initial image to obtain location information of the target object in the storage area.
In operation S130, a target image for the target object is acquired based on the position information.
In operation S140, image processing is performed on the target image to obtain a quality detection result for the target object.
Illustratively, the storage area stores, for example, a plurality of objects, the storage area includes, for example, a material box, a shelf, and the like, and the objects include, for example, products, items, and the like.
For example, after the electronic device acquires an initial image for the storage area, the initial image including, for example, a plurality of objects, each object in the initial image can be identified by performing image processing on the initial image. For a target object of the plurality of objects, location information of the target object in the storage area may be determined based on its location in the initial image.
Next, based on the position information of the target object in the storage area, the target object is taken out of the storage area, and a target image for the target object is acquired. Then, the target image is subjected to image recognition to obtain a quality detection result for the target object, wherein the quality detection result represents whether the target object has cracks, unfilled corners, stains, damages and the like.
According to the embodiment of the disclosure, the position information of the target object in the storage area is obtained by acquiring the initial image aiming at the storage area and processing the initial image, and the target object is taken from the storage area based on the position information so as to acquire the target image aiming at the target object. Then, the target image is subjected to image processing to obtain a quality detection result for the target object. It can be understood that, with the embodiments of the present disclosure, the target object can be quickly and accurately retrieved from the storage area based on image processing to perform quality detection on the target object, thereby improving accuracy of quality detection, improving automation of quality detection, and reducing cost of quality detection.
Fig. 2 schematically shows a flow chart of a quality detection method according to another embodiment of the present disclosure.
As shown in fig. 2, the quality detection method 200 of the embodiment of the present disclosure may include, for example, operations S201 to S210. The quality detection method 200 of the disclosed embodiments may be performed, for example, by an electronic device, which may include a computer, a server, and the like.
In operation S201, an initial image for a storage region is acquired.
In operation S202, trained first deep learning model data is acquired.
Illustratively, the trained first learning model data includes, for example, instance segmentation model data including, for example and without limitation, a Mask-CNN model.
In operation S203, the trained first deep learning model data is converted into reference model data.
In operation S204, the initial image is processed based on the reference model data using the inference acceleration engine, resulting in location information of the target object in the storage area.
Illustratively, the reference model data is compressed data of the first deep learning model data, for example, and the speed at which the reference model data runs on the CPU is fast. After the reference model data is obtained, the reference model data may be run on the CPU using an inference acceleration engine to process the initial image through the reference model data to obtain the position information, thereby speeding up the image processing.
Illustratively, the inference acceleration engine includes, for example, an openvino engine, which is an inference acceleration engine for a deep learning model. The second deep learning model data is compressed to obtain the reference model data, so that the reference model data can be operated on the CPU for image recognition, and the model operation speed is improved while the model accuracy is ensured.
In operation S205, a target image for the target object is acquired based on the position information.
In operation S206, the target image is detected to obtain a target region in the target image based on the trained second deep learning model.
In operation S207, a quality detection result for the target object is determined based on the target region.
Illustratively, the trained second deep learning model includes, for example, a target detection model, which may include a YOLO model including, but not limited to, a PP-yollov 2 model. The second deep learning model is used for detecting at least one target area in the target image, and the target area is used for representing whether quality abnormity exists in the target object or not. For example, the target area includes a crack area, a unfilled corner area, a stain area, a damaged portion area, and the like of the target object. Based on the target area, a quality detection result of the target object may be determined, the quality detection result characterizing whether the target object has defects such as cracks, missing corners, stains, damages, and the like.
In operation S208, it is determined whether the target object has a quality problem based on the quality detection result.
Illustratively, if it is determined that the target object has a quality problem, operation S209 is performed, and if it is determined that the target object has no quality problem, operation S210 is performed.
In operation S209, the target object is classified into a first classification.
In operation S210, the target object is classified into a second classification.
Illustratively, the first classification includes objects having quality issues and the second classification includes objects having no quality issues.
According to the embodiment of the disclosure, the target object is subjected to quality detection through a target detection algorithm, so that the accuracy of quality detection is improved, and classification is performed based on a quality detection result, so that the accuracy of classification is improved.
FIG. 3 schematically illustrates a schematic diagram of a quality detection method according to an embodiment of the present disclosure.
As in fig. 3, with respect to a storage area 301 for storing a plurality of objects, an initial image 302 of the storage area 301 is acquired. The initial image 302 is subjected to image recognition by using the first deep learning model, and the position information 303 of the target object in the storage area 301 is obtained.
Then, the target object 304 is fetched from the storage area 301 based on the position information 303, and a target image 305 for the target object 304 is acquired. The target image 305 is processed using the second deep learning model to obtain a quality detection result 306. The target object 304 is classified into a first classification 307 or a second classification 308 based on the quality detection result 306. The first classification 307 for example comprises objects having quality problems and the second classification 308 for example comprises objects having no quality problems.
Fig. 4 schematically illustrates a schematic diagram of a quality detection system according to an embodiment of the present disclosure.
As shown in fig. 4, the quality detection system includes, for example, at least an electronic device, a first image capturing apparatus 401, a grasping apparatus 402, and a second image capturing apparatus 403.
The electronic device includes, for example, a computer, a server, and the like, and is, for example, in communication connection with each of the first image capturing apparatus 401, the capturing apparatus 402, and the second image capturing apparatus 403.
For example, the first image capturing device 401 is connected to an electronic device in communication, and the first image capturing device 401 captures an initial image of the storage area 404 under the control of the electronic device. The first image capture device 401 may include an industrial camera, a webcam, or the like.
For example, the grasping apparatus 402 is communicatively connected to the electronic device, and the grasping apparatus 402 grasps the target object from the storage area 404 based on the position information of the target object in the storage area 404 under the control of the electronic device. The gripping device 402 comprises, for example, a robotic arm or a robot arm.
For example, the second image capturing device 403 is connected to an electronic device in communication, and under the control of the electronic device, the second image capturing device 403 captures a target image of a target object. The second image capture device 403 may include an industrial camera, a webcam, and the like.
For example, after acquiring an initial image of the storage area 404 acquired by the first image acquisition device 401, the electronic device performs image processing on the initial image to obtain the position information of the target object in the storage area 404. Then, the electronic apparatus controls the grasping means 402 to grasp the target object from the storage area 404 based on the position information.
After the target object is transferred to the second image capturing device 403, the electronic device acquires a target image for the target object captured by the second image capturing device 403. Next, the electronic device performs image processing on the target image to obtain a quality detection result for the target object.
According to the embodiment of the disclosure, the quality detection system performs quality detection on the target object through the mutual cooperation of the electronic equipment, the first image acquisition device, the grabbing device and the second image acquisition device, so that the automation of quality detection is improved, and the cost of quality detection is reduced. In addition, the quality detection system is combined with the deep learning model to carry out quality detection, and the integration of the traditional quality detection system and the deep learning technology is realized.
Illustratively, the quality detection system may further comprise a rotating device 405, the rotating device 405 may comprise a turntable, and the rotating device 405 is in communication with the electronic device.
After the grasping means 402 grasps the target object from the storage area 404, the grasped target object is placed on the rotating means 405. Under the control of the electronic device, the rotating device 405 transfers the target object to the second image capturing device 403, so that the second image capturing device 403 captures a target image for the target object.
After the second image capturing device 403 captures the target image, the rotating device 405 transfers the target object from the second image capturing device 403 to the grasping device 402 under the control of the electronic device.
Next, the grasping means 402 grasps the target object to the first classification area 406 or the second classification area 407 based on the quality detection result under the control of the electronic device. For example, if the quality detection result indicates that the target object has a quality problem, the grabbing device 402 grabs the target object to the first classification area 406. If the quality detection result indicates that the target object does not have a quality problem, the grabbing device 402 grabs the target object to the second classification area 407.
The electronic device may also synchronize the detection result to the mobile terminal, which may be a mobile phone, for example, so as to obtain the quality detection result from the mobile terminal in time.
After acquiring the target image for the target object, the electronic device may process the target image for quality detection by executing a target detection algorithm by an embedded computing unit, for example, including an NVIDIA-Jetson-NX edge chip.
Fig. 5 schematically illustrates a block diagram of a quality detection apparatus according to an embodiment of the present disclosure.
As shown in fig. 5, the quality detection apparatus 500 of the embodiment of the present disclosure includes, for example, a first obtaining module 510, a first processing module 520, a second obtaining module 530, and a second processing module 540.
The first acquisition module 510 may be used to acquire an initial image for a storage area. According to an embodiment of the present disclosure, the first obtaining module 510 may, for example, perform operation S110 described above with reference to fig. 1, which is not described herein again.
The first processing module 520 may be configured to perform image processing on the initial image to obtain location information of the target object in the storage area. According to the embodiment of the present disclosure, the first processing module 520 may, for example, perform operation S120 described above with reference to fig. 1, which is not described herein again.
The second acquiring module 530 may be configured to acquire a target image for the target object based on the position information. According to the embodiment of the present disclosure, the second obtaining module 530 may, for example, perform operation S130 described above with reference to fig. 1, which is not described herein again.
The second processing module 540 may be configured to perform image processing on the target image to obtain a quality detection result for the target object. According to the embodiment of the present disclosure, the second processing module 540 may, for example, perform operation S140 described above with reference to fig. 1, which is not described herein again.
According to an embodiment of the present disclosure, the first processing module 520 includes: a conversion submodule and a processing submodule. A conversion sub-module for converting the trained first deep learning model data into reference model data; and the processing submodule is used for processing the initial image based on the datum model data by utilizing the reasoning acceleration engine to obtain the position information of the target object in the storage area.
According to an embodiment of the present disclosure, the second processing module 540 includes: a detection submodule and a determination submodule. The detection submodule is used for detecting the target image to obtain a target area in the target image based on the trained second deep learning model, wherein the target area represents whether the target object has abnormal quality; a determination submodule for determining a quality detection result for the target object based on the target area.
According to an embodiment of the present disclosure, the apparatus 500 may further include: and the classification module is used for classifying the target object based on the quality detection result.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 6 is a block diagram of an electronic device for performing quality detection used to implement an embodiment of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device 600 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the quality detection method. For example, in some embodiments, the quality detection method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the quality detection method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the quality detection method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable quality detection apparatus such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (15)

1. A method of quality detection, comprising:
acquiring an initial image for a storage area;
performing image processing on the initial image to obtain position information of a target object in the storage area;
acquiring a target image for the target object based on the position information; and
and carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
2. The method of claim 1, wherein the image processing the initial image to obtain the position information of the target object in the storage area comprises:
converting the trained first deep learning model data into reference model data; and
and processing the initial image based on the datum model data by using an inference acceleration engine to obtain the position information of the target object in the storage area.
3. The method of claim 1, wherein the image processing the target image to obtain the quality detection result for the target object comprises:
detecting the target image to obtain a target area in the target image based on the trained second deep learning model, wherein the target area represents whether the target object has quality abnormity; and
based on the target area, a quality detection result for the target object is determined.
4. The method of any of claims 1-3, further comprising:
classifying the target object based on the quality detection result.
5. A quality detection system, comprising:
an electronic device;
the first image acquisition device is in communication connection with the electronic equipment and is configured to acquire an initial image aiming at a storage area under the control of the electronic equipment;
the grabbing device is in communication connection with the electronic equipment and is configured to grab a target object from the storage area based on the position information of the target object in the storage area under the control of the electronic equipment; and
the second image acquisition device is in communication connection with the electronic equipment and is configured to acquire a target image aiming at the target object under the control of the electronic equipment;
wherein the electronic device is configured to perform:
acquiring an initial image for a storage area acquired by the first image acquisition device;
performing image processing on the initial image to obtain position information of the target object in the storage area;
controlling the grabbing device to grab the target object from the storage area based on the position information;
acquiring a target image for the target object acquired by the second image acquisition device; and
and carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
6. The system of claim 5, further comprising:
and the rotating device is in communication connection with the electronic equipment and is configured to transfer the target object grabbed by the grabbing device to the second image acquisition device under the control of the electronic equipment.
7. The system of claim 6, wherein the rotating device is further configured to:
transferring the target object from the second image capturing device to the grasping device under control of the electronic device.
8. The system of claim 7, wherein the grasping device is further configured to:
under the control of the electronic equipment, capturing the target object to a first classification area or a second classification area based on the quality detection result.
9. A mass spectrometry apparatus comprising:
the first acquisition module is used for acquiring an initial image aiming at the storage area;
the first processing module is used for carrying out image processing on the initial image to obtain the position information of the target object in the storage area;
a second obtaining module, configured to obtain a target image for the target object based on the position information; and
and the second processing module is used for carrying out image processing on the target image to obtain a quality detection result aiming at the target object.
10. The apparatus of claim 9, wherein the first processing module comprises:
a conversion sub-module for converting the trained first deep learning model data into reference model data; and
and the processing submodule is used for processing the initial image based on the datum model data by utilizing an inference acceleration engine to obtain the position information of the target object in the storage area.
11. The apparatus of claim 9, wherein the second processing module comprises:
the detection submodule is used for detecting the target image to obtain a target area in the target image based on the trained second deep learning model, wherein the target area represents whether the target object has abnormal quality; and
a determination sub-module for determining a quality detection result for the target object based on the target area.
12. The apparatus of any of claims 9-11, further comprising:
and the classification module is used for classifying the target object based on the quality detection result.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-4.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-4.
CN202111514311.6A 2021-12-10 2021-12-10 Quality detection method, system, apparatus, electronic device, and medium Pending CN114202526A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114750154A (en) * 2022-04-25 2022-07-15 贵州电网有限责任公司 Dynamic target identification, positioning and grabbing method for distribution network live working robot
CN116612168A (en) * 2023-04-20 2023-08-18 北京百度网讯科技有限公司 Image processing method, device, electronic equipment, image processing system and medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110779928A (en) * 2019-11-19 2020-02-11 汪科道 Defect detection device and method
CN111650204A (en) * 2020-05-11 2020-09-11 安徽继远软件有限公司 Transmission line hardware defect detection method and system based on cascade target detection
CN113221687A (en) * 2021-04-28 2021-08-06 南京南瑞继保电气有限公司 Training method of pressing plate state recognition model and pressing plate state recognition method
CN113744247A (en) * 2021-09-03 2021-12-03 西安建筑科技大学 PCB welding spot defect identification method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110779928A (en) * 2019-11-19 2020-02-11 汪科道 Defect detection device and method
CN111650204A (en) * 2020-05-11 2020-09-11 安徽继远软件有限公司 Transmission line hardware defect detection method and system based on cascade target detection
CN113221687A (en) * 2021-04-28 2021-08-06 南京南瑞继保电气有限公司 Training method of pressing plate state recognition model and pressing plate state recognition method
CN113744247A (en) * 2021-09-03 2021-12-03 西安建筑科技大学 PCB welding spot defect identification method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114750154A (en) * 2022-04-25 2022-07-15 贵州电网有限责任公司 Dynamic target identification, positioning and grabbing method for distribution network live working robot
CN116612168A (en) * 2023-04-20 2023-08-18 北京百度网讯科技有限公司 Image processing method, device, electronic equipment, image processing system and medium

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