CN110567974A - Cloud artificial intelligence based surface defect detection system - Google Patents
Cloud artificial intelligence based surface defect detection system Download PDFInfo
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- CN110567974A CN110567974A CN201910937955.2A CN201910937955A CN110567974A CN 110567974 A CN110567974 A CN 110567974A CN 201910937955 A CN201910937955 A CN 201910937955A CN 110567974 A CN110567974 A CN 110567974A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8883—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges involving the calculation of gauges, generating models
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
Abstract
the invention belongs to the field of image processing, and discloses a cloud artificial intelligence based surface defect detection system. The system comprises an edge end module and a cloud data processing module, wherein the edge end module comprises a data acquisition unit and a result feedback unit; the cloud data processing module comprises an intelligent marking unit, a training unit and a detection unit, wherein the intelligent marking unit is used for intelligently marking good, defective and defect information of the image to be marked, the marked image is used as data to be trained and input into the training unit, and the training unit is used for establishing a prediction model and transmitting the prediction model to the detection unit; the detection unit is used for detecting the image to be detected by utilizing the prediction model so as to obtain the detection result of the image to be detected, then feeding the detection result back to the result feedback unit, and displaying the detection result through the result feedback unit. By the method and the device, the calculation cost of edge end detection is reduced, the detection efficiency and the detection precision are improved, and the product yield is improved.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a cloud artificial intelligence based surface defect detection system.
Background
In the field of industrial manufacturing, the quality of raw materials varies, the manufacturing process is complicated, and surface defects may occur on the surface of products, such as steel, wood, textiles, tiles, and novel display devices, such as TFT-LCD, OLED, etc. Surface defects refer to local areas that differ from the surrounding texture and pattern, or local areas with irregular brightness variations. These surface defects can directly degrade product quality and affect user experience. Surface defect detection is the basis and key for the entire manufacturing industry because all types of surface defects should be effectively controlled during the manufacturing process in order to improve production quality.
Automated optical inspection refers to the use of machines to automatically inspect, measure and analyze objects in lieu of the human eye, and is commonly used in industrial production environments. Automatic optical detection uses one or more cameras to shoot target images, and an image processing system is adopted to analyze and process the target images to obtain information such as the state, color, position and the like of the target, so as to further guide equipment operation, such as tool bit movement, quality judgment, manipulator grabbing and the like. Since the automatic optical detection has the characteristics of non-contact, high speed, high robustness and high precision, the automatic optical detection is widely applied to industrial manufacturing. The automatic optical detection can work in a severe industrial environment, visual fatigue caused by long-time work of human eyes can be avoided, the production efficiency can be greatly improved, the detection result can be quantized, and the product process optimization can be guided.
Most of the existing automatic optical detection equipment is a single-machine system, namely one piece of equipment works independently, and all the equipment needs to be provided with a computer with enough computing power and needs to complete all the work of image acquisition, data collection, detection process calculation and the like, so that the manufacturing cost is high, and the detection efficiency is low; the existing automatic optical detection equipment adopts a traditional visual detection algorithm, so that the detection rate of the surface defects of various industrial products is difficult to have good robustness, and is low, and the use of the equipment on a manufacturing production line is restricted.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a cloud artificial intelligence based surface defect detection system, which aims to detect the surface defects of industrial products by utilizing the powerful data modeling capability of deep learning, and adopts a mode of deploying an AI detection algorithm at the cloud for detection, acquiring images at the edge end and collecting a data set to display the detection result in order to improve the detection efficiency and reduce the manufacturing cost, so that the detection precision and efficiency can be greatly improved, and the adaptability of the products to the surface defect detection of different types of industrial products can be improved.
In order to achieve the above object, according to the present invention, there is provided a cloud-based artificial intelligence surface defect detection system, which is characterized in that the system comprises an edge module and a cloud data processing module, wherein,
the edge end module comprises a data acquisition unit and a result feedback unit, the data acquisition unit is used for acquiring an image of an object to be detected and transmitting the image to the cloud data processing module, and the result feedback unit is used for receiving and displaying a detection result obtained after the cloud data processing module performs data processing according to the acquired image;
The cloud data processing module comprises an intelligent labeling unit, a training unit and a detection unit, for the intelligent labeling unit, learning is carried out according to good product, defective product and defect information of an image to be labeled manually, then labeling is carried out on the image to be labeled according to the learning result, so that the good product, the defective product and the defect information of the image to be labeled are determined, and then the labeling result of the intelligent labeling unit is checked manually and corrected; finally, data of good products, defective products and defect information of the images to be detected, which are obtained after manual inspection, are transmitted to the training unit;
The training unit establishes a prediction model according to the training data and transmits the prediction model to the detection unit;
the detection unit receives the prediction model, detects the image to be detected by using the prediction model so as to obtain a detection result of the image to be detected, namely, whether the image to be detected is a defective product or not and defect information in the defective product are judged, and then the detection unit feeds the detection result back to the result feedback unit for displaying, so that the defect detection is realized.
Further preferably, the edge end modules are arranged on one or more production lines and used for collecting data of the production lines and feeding back results of data processing to the production lines in real time.
Further preferably, the defect information includes a location, a size, and a kind of the defect.
Further preferably, the training module preferably trains the training data using an artificial intelligence algorithm.
Further preferably, the prediction model constructed in the intelligent labeling unit includes a good product and defective product classification model and a defective product defect information prediction model.
Further preferably, the data acquisition unit acquires the image by using an industrial camera or a computer device.
Further preferably, the result feedback unit displays the detection result in a manner of displaying the detection result on each production line individually or displaying the detection result on a plurality of production lines integrally.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. according to the invention, the intelligent marking of the image to be detected is realized by adopting a mode of combining the intelligent marking unit with the manual marking result, so that the manual workload is reduced, and meanwhile, the accuracy of the intelligent marking result is improved by matching with manual inspection;
2. The invention adopts the detection unit to detect the image of the object which needs to be detected actually, adopts the prediction model to automatically detect without human participation, realizes the automation of the detection process, and simultaneously improves the detection accuracy and the timeliness;
3. According to the invention, the edge end module is arranged on the production line, the cloud data processing module is designed at one end far away from the production line, and the remote control and the real-time monitoring of the production line are realized by acquiring the data of each production line in real time and feeding back the detection result in real time.
Drawings
FIG. 1 is a schematic diagram of a cloud-based artificial intelligence surface defect detection system constructed in accordance with a preferred embodiment of the present invention;
fig. 2 is a schematic structural diagram of a cloud data processing module according to a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of the structure of an intelligent labeling unit constructed in accordance with the preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of the operation of the intelligent annotation unit constructed in accordance with the preferred embodiment of the present invention;
fig. 5 is a schematic diagram of the operation of a detection unit constructed in accordance with a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides a cloud-based artificial intelligence surface defect detection system, which includes the following modules:
(1) Edge terminal module
The edge end module comprises a data acquisition module and a detection result feedback display module. Arranging a data acquisition module on an industrial automatic production field, wherein the data acquisition module comprises an industrial camera, computer equipment for assisting calculation and the like required for acquisition; once the production operation starts, the data acquisition module is started, massive data required by training are collected and temporarily stored in a local computer or a factory server, and then the data are uploaded to the cloud independent module for data processing.
(1-1) data acquisition Unit
In actual industrial production requirements, a current artificial intelligence processing algorithm is combined, massive data are used for optimizing a processing model, and compared with a traditional machine learning processing algorithm, the method has great advantages, such as the detection of surface defects and the like with low contrast and the like which are difficult to detect. Different automation line or production factory, the actual detection demand is different, and required surface defect and the product that detects are all inconsistent, need separately handle the collection to subsequent intelligent mark. The data acquisition modules arranged at each edge section are respectively debugged and detected by engineers to carry out full-automatic data acquisition, establish a local database and ensure that the characteristic requirements of the automatic production are met, so that a customized AI (artificial intelligence) algorithm is trained to detect defective products and improve the production efficiency.
(1-2) result feedback Unit
When the good product/defective product detection of the product is carried out, the data to be detected are uploaded to the cloud module for defect detection. When the detection is finished, the detection result is transmitted to the edge end through the network according to the detection rule which is formulated by the system and accords with the production requirement, and the corresponding detection result is displayed on the computer of the edge section, wherein the detection result comprises the position, the defect size and the defect type of the defect in the image, the defect generation reason and the corresponding production line improvement strategy aiming at the defect, so as to guide the actual production.
(2) Cloud data processing module
As shown in fig. 2, the cloud data processing module includes three major modules, namely, an intelligent labeling unit, an AI training unit, and an AI detection unit. The cloud data processing module is used for marking and processing the acquired mass data (including image data of various surface products, such as TFT-LCD, steel, wood, fabric and the like); the module is independent of each large production factory, is not interfered by any automatic production, and provides a data collection and detection interface for each large production factory for use. The automatic production factory can selectively make corresponding configuration requirements (including detection rules, cloud hardware computing power and the like) according to actual production scale requirements, is independent of production, and efficiently and quickly feeds back production problems during production.
(2-1) Intelligent labeling Unit
As shown in fig. 3 and 4It shows that the mass data collected in each large-scale production factory needs to be screened and labeled so as to train a detection model meeting the actual production requirement of the factory. After the data are uploaded to a cloud end system, a large number of labels need to be carried out, the existing AI algorithm at the cloud end is proposed, full-automatic screening and labeling are carried out on the mass data, and meanwhile, a professional engineer is provided for supervision, so that the data screening and labeling are guaranteed to be free of errors. The automatic screening and labeling of the AI algorithm and the supervision strategy of professional engineers are combined, the efficiency of data screening and labeling is greatly improved, the labeling cost is reduced, and the production efficiency is indirectly improved. For the collected data XnaAt expert supervision thetaexpertsin the case of (2), an automatic labeling algorithm f is usedannotationAnd classifying the collected data into c and judging the defects into ides.
c,ides＝fannotation(Xna|θexperts)
(2-2) AI training Unit
The mass data processed by the intelligent marking unit can be directly imported into an AI training unit for model training. In the module, training rules, judgment rules and the like are customized according to production requirements of various large production factories so as to train an AI processing algorithm meeting actual production requirements, meet requirements of different industries and improve data processing model capacity. When training is finished, the model is stored in a cloud system, a data input interface is provided, and the model is used by each large-scale production factory, so that the equivalent detection precision can be achieved in the short-time input actual production, and the production efficiency is rapidly improved. For good product sample X marked with category cpFor training AI algorithm model fmodelWith a parameter of thetamodelThe updated model parameters are trained according to the following formula.
(2-3) AI detection Unit
As shown in FIG. 5, the AI test unit is provided to each of the MPDs to test the call module. When the module is usedEach large-scale production factory can select corresponding calculation engines, defect detection models, GPU computing resources and the like according to the requirements of the large-scale production factory, meanwhile, a scheduling algorithm of intelligent adjustment and load balance is provided, and the production efficiency is improved. After the detection is finished, providing the position, the defect size and the defect type of the defect in the image, the defect generation reason, a corresponding production line improvement strategy and other important production information aiming at the defect so as to guide the actual production. For the sample X to be detecteddIs transmitted into the trained detection model f through a data interfacemodeland outputting the detection result defect according to the following formula.
defect＝|fmodel(Xd,c|θmodel)-Xd|
The cloud artificial intelligence surface defect detection system provided by the invention develops a cloud independent surface defect detection algorithm independent of each large production factory by means of a strong network and cloud service functions, so as to customize the algorithm, calculate the power and other resources, meet various production requirements, help enterprises to greatly reduce the product inspection time on line, directly improve the productivity and promote the continuous process quality improvement.
it will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A cloud-based artificial intelligence surface defect detection system is characterized by comprising an edge module and a cloud data processing module, wherein,
The edge end module comprises a data acquisition unit and a result feedback unit, the data acquisition unit is used for acquiring an image of an object to be detected and transmitting the image to the cloud data processing module, and the result feedback unit is used for receiving and displaying a detection result obtained after the cloud data processing module performs data processing according to the acquired image;
The cloud data processing module comprises an intelligent labeling unit, a training unit and a detection unit, for the intelligent labeling unit, learning is carried out according to good product, defective product and defect information of an image to be labeled manually, then labeling is carried out on the image to be labeled according to the learning result, so that the good product, the defective product and the defect information of the image to be labeled are determined, and then the labeling result of the intelligent labeling unit is checked manually and corrected; finally, data of good products, defective products and defect information of the images to be detected, which are obtained after manual inspection, are transmitted to the training unit;
The training unit establishes a prediction model according to the training data and transmits the prediction model to the detection unit;
the detection unit receives the prediction model, detects the image to be detected by using the prediction model so as to obtain a detection result of the image to be detected, namely, whether the image to be detected is a defective product or not and defect information in the defective product are judged, and then the detection unit feeds the detection result back to the result feedback unit for displaying, so that the defect detection is realized.
2. The cloud-based artificial intelligence surface defect detection system of claim 1, wherein the edge modules are disposed on one or more production lines and configured to collect production line data and feed back data processing results to the production lines in real time.
3. The cloud-based artificial intelligence surface defect detection system of claim 1 or 2, wherein the defect information comprises a location, a size, and a type of defect.
4. The cloud-based artificial intelligence surface defect detection system of claim 1, wherein the training module trains the training data, preferably using an artificial intelligence algorithm.
5. The cloud-based artificial intelligence surface defect detection system of claim 1, wherein the prediction models constructed in the intelligent labeling unit comprise good product and defective product classification models and defective product defect information prediction models.
6. the cloud-based artificial intelligence surface defect detection system of claim 1, wherein the data acquisition unit acquires images using an industrial camera or a computer device.
7. the cloud-based artificial intelligence surface defect detection system of any one of claims 1 to 6, wherein the result feedback unit displays the detection result in a manner of displaying the detection result on each production line individually or displaying the detection result on a plurality of production lines integrally.
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| CN111431986A (en) * | 2020-03-18 | 2020-07-17 | 宁波智诚祥科技发展有限公司 | Industrial intelligent quality inspection system based on 5G and AI cloud edge cooperation |
| CN112712504A (en) * | 2020-12-30 | 2021-04-27 | 广东粤云工业互联网创新科技有限公司 | Workpiece detection method and system based on cloud and computer-readable storage medium |
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| CN112734703A (en) * | 2020-12-28 | 2021-04-30 | 佛山市南海区广工大数控装备协同创新研究院 | PCB defect optimization method by utilizing AI cloud collaborative detection |
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| CN116954181A (en) * | 2023-09-21 | 2023-10-27 | 深圳市兴万达电子科技有限公司 | Intelligent module control system and method based on Internet of things |
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