CN117575382A - Automatic intelligent image inspection quality control management system - Google Patents
Automatic intelligent image inspection quality control management system Download PDFInfo
- Publication number
- CN117575382A CN117575382A CN202311483510.4A CN202311483510A CN117575382A CN 117575382 A CN117575382 A CN 117575382A CN 202311483510 A CN202311483510 A CN 202311483510A CN 117575382 A CN117575382 A CN 117575382A
- Authority
- CN
- China
- Prior art keywords
- image
- module
- quality control
- remote
- control management
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 33
- 238000003908 quality control method Methods 0.000 title claims abstract description 31
- 238000012545 processing Methods 0.000 claims abstract description 64
- 238000007726 management method Methods 0.000 claims abstract description 33
- 230000005540 biological transmission Effects 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 238000004891 communication Methods 0.000 claims abstract description 13
- 230000002159 abnormal effect Effects 0.000 claims abstract description 11
- 238000005516 engineering process Methods 0.000 claims abstract description 8
- 230000007547 defect Effects 0.000 claims abstract description 7
- 238000010191 image analysis Methods 0.000 claims abstract description 7
- 238000013461 design Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 4
- 238000003709 image segmentation Methods 0.000 claims description 4
- 238000010801 machine learning Methods 0.000 claims description 4
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 238000012552 review Methods 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/11—File system administration, e.g. details of archiving or snapshots
- G06F16/113—Details of archiving
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Quality & Reliability (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention provides an automatic intelligent image inspection quality control management system, and relates to the technical field of quality management. The automatic intelligent image inspection quality control management system comprises an image acquisition and transmission module, wherein the image acquisition and transmission module is connected with an image processing and analysis module. The automatic intelligent image inspection quality control management system can overcome the technical defects through functions of remote communication and cooperation, real-time feedback and adjustment and the like, can acquire more accurate judgment and processing advice through real-time communication and cooperation with remote experts, solves the quality problem under complex or special conditions, can timely discover and process unqualified products and abnormal conditions through the real-time feedback and adjustment function of the system, improves the efficiency and accuracy of quality control and management, and can reduce the situations of false alarm and false judgment through the automatic image analysis and recognition technology of the system, and improve the accuracy and consistency of detection.
Description
Technical Field
The invention relates to the technical field of quality management, in particular to an automatic intelligent image inspection quality control management system.
Background
The image inspection quality control management system is a system for monitoring, analyzing and managing quality problems in the image inspection process by utilizing an automatic intelligent technology. The method detects possible quality problems by analyzing and identifying the image data in real time and provides corresponding processing suggestions and feedback.
The system can be applied to various image inspection fields, such as medical image inspection, industrial product inspection, safety monitoring and the like. The method utilizes advanced image processing and recognition algorithms to automatically evaluate and analyze the quality of the image data. The system can detect some common quality problems such as image blurring, noise, artifacts and the like, provide corresponding processing suggestions, help operators correct the problems in time, and improve the accuracy and reliability of image inspection.
In addition, the image inspection quality control management system also has the functions of remote communication and cooperation, and can communicate and cooperate with a remote expert in real time to acquire more accurate judgment and processing advice. The system can also record and analyze quality data, generate quality reports and statistical analysis, and provide support for quality management and improvement.
In general, the image inspection quality control management system utilizes an automatic intelligent technology to monitor and manage quality problems in an image inspection process by analyzing and recognizing image data in real time, and provides accurate processing suggestions and feedback to improve quality and efficiency of image inspection.
The general quality control management system is easy to have limitation when processing the quality problems under complex or special conditions, is difficult to provide accurate judgment and processing advice, is easy to delay in real-time feedback and adjustment, is difficult to discover and process unqualified products and abnormal conditions in time, and is also possible to have false alarms and false judgment conditions due to the automation property of the system, so that unnecessary intervention and correction are caused, and the quality control and management efficiency and accuracy are poor.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an automatic and intelligent image inspection quality control management system, which solves the problem of poor quality control and management efficiency and accuracy.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the automatic intelligent image inspection quality control management system comprises an image acquisition and transmission module, wherein the image acquisition and transmission module is connected with an image processing and analysis module, the image processing and analysis module is connected with a decision support module, the decision support module is connected with a real-time feedback and adjustment module, the real-time feedback and adjustment module is connected with a remote notification and collaboration module, the remote notification and collaboration module is connected with a generation report and recording module, and the image acquisition and transmission module, the image processing and analysis module, the decision support module, the real-time feedback and adjustment module, the remote notification and collaboration module and the generation report and recording module are sequentially connected.
Preferably, the image acquisition and transmission module comprises an image acquisition device and a data transmission protocol, and the image acquisition device is connected to the data transmission protocol.
Preferably, the image processing and analyzing module comprises image recognition, target detection, feature extraction and anomaly detection.
Preferably, the decision support module includes quality decisions, adjustment recommendations, and protocol recommendations.
Preferably, the real-time feedback and adjustment module includes an alarm system, an automatic adjustment, a design plan, and a history plan.
Preferably, the remote notification and collaboration module includes a communication collaboration platform and a remote expert collaboration.
Preferably, the generating report and recording module includes processing result record archiving and processing report generation.
An automatic intelligent image inspection quality control management method specifically comprises the following steps:
s1, image acquisition
The image data for image acquisition of the product can be a static image and a dynamic video by using a camera and other image acquisition equipment;
s2, image preprocessing
Preprocessing the acquired image data, including denoising, enhancement and color correction, can improve the image quality and reduce noise and distortion;
s3, image analysis
Analyzing the preprocessed image, including target detection, image segmentation and feature extraction, so that key information and features in the image can be extracted;
s4, image processing
According to the result of image analysis, image processing such as image restoration, morphological operation and filtering is performed, so that the quality and definition of the image can be improved;
s5, image identification
The targets in the images are identified and classified by using machine learning and a mode identification algorithm, so that defects and abnormal conditions in products can be automatically identified;
s6, decision support
According to the image processing and analysis results, the system provides decision support, including quality judgment, adjustment suggestion and plan recommendation, so that an operator can be helped to make an accurate decision;
s7, feeding back and adjusting in real time
If the unqualified abnormal phenomenon is found, the system alarms and gives a warning to an operator, and the operator carries out corresponding adjustment according to warning information provided by the system;
s8, judging threshold time
If the operator does not respond within the set threshold time, the system automatically judges according to the preset design plan, the history plan and the remote collaboration and automatically processes the judgment;
s9, remote notification and collaboration
The system carries out remote notification on the processing situation and cooperates with a remote expert to acquire more accurate judgment and processing advice, which can be realized through remote communication and cooperation technologies such as video conference, instant messaging and remote desktop;
s10, recording a processing result
The system generates process reports and records, including quality issues, process results, and remote collaboration scenarios, which may be recorded and archived using data storage and report generation techniques for subsequent analysis and review.
(III) beneficial effects
The invention provides an automatic and intelligent image inspection quality control management system. The beneficial effects are as follows:
1. the invention provides an automatic intelligent image inspection quality control management system, which can immediately alarm and warn an operator when detecting unqualified abnormal phenomena, and can correspondingly adjust according to warning information provided by the system, if the operator does not respond within a set threshold time, the system can automatically judge and automatically process according to a preset design plan, a history plan and remote collaboration, and the automatic intelligent image inspection quality control management system can realize real-time feedback and adjustment, alarm and process unqualified abnormal phenomena, and improve the efficiency and accuracy of quality control and management.
2. The invention provides an automatic intelligent image inspection quality control management system, which can report the processing condition remotely and cooperate with a remote expert to acquire more accurate judgment and processing advice, and finally, the system can generate a processing report and record including quality problems, processing results and remote cooperation condition, and communicate and cooperate with the remote expert in real time through remote communication and cooperation to provide more accurate judgment and processing advice.
Drawings
FIG. 1 is a schematic diagram of a system architecture according to the present invention;
FIG. 2 is a schematic diagram of the image acquisition and transmission module according to the present invention;
FIG. 3 is a schematic diagram showing the components of the image processing and analyzing module according to the present invention;
FIG. 4 is a schematic diagram of the decision support module of the present invention;
FIG. 5 is a schematic diagram of the real-time feedback and adjustment module of the present invention;
FIG. 6 is a schematic diagram of the remote notification and collaboration module composition of the present invention;
FIG. 7 is a schematic diagram of the composition of the report and record generating module of the present invention.
1, an image acquisition and transmission module; 2. an image processing and analyzing module; 3. a decision support module; 4. a real-time feedback and adjustment module; 5. a remote notification and collaboration module; 6. generating a report and recording module; 7. an image acquisition device; 8. a data transmission protocol; 9. identifying an image; 10. detecting a target; 11. extracting features; 12. abnormality detection; 13. judging quality; 14. adjusting the advice; 15. recommending a plan; 16. an alarm system; 17. automatically adjusting; 18. designing a plan; 19. history plans; 20. a communication collaboration platform; 21. remote expert collaboration; 22. recording and archiving the processing result; 23. and (5) processing report generation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
as shown in fig. 1-7, an embodiment of the present invention provides an automatic intelligent image inspection quality control management system, which includes an image acquisition and transmission module 1, wherein the image acquisition and transmission module 1 is connected with an image processing and analysis module 2, the image processing and analysis module 2 is connected with a decision support module 3, the decision support module 3 is connected with a real-time feedback and adjustment module 4, the real-time feedback and adjustment module 4 is connected with a remote notification and collaboration module 5, the remote notification and collaboration module 5 is connected with a report generation and recording module 6, and the image acquisition and transmission module 1, the image processing and analysis module 2, the decision support module 3, the real-time feedback and adjustment module 4, the remote notification and collaboration module 5 and the report generation and recording module 6 are sequentially connected.
The image acquisition and transmission module 1 comprises an image acquisition device 7 and a data transmission protocol 8, the image acquisition device 7 is connected to the data transmission protocol 8, the image processing and analysis module 2 comprises an image identification 9, a target detection 10, a feature extraction 11 and an abnormality detection 12, the decision support module 3 comprises a quality judgment 13, an adjustment suggestion 14 and a plan recommendation 15, the real-time feedback and adjustment module 4 comprises an alarm system 16, an automatic adjustment 17, a design plan 18 and a history plan 19, the remote notification and collaboration module 5 comprises a communication collaboration platform 20 and a remote expert collaboration 21, and the generation report and recording module 6 comprises a processing result record archive 22 and a processing report generation.
Image acquisition and transmission module 1: and the system is responsible for collecting the image data of the product and transmitting the image data to a subsequent processing module. The module may use a high resolution camera, webcam or other image acquisition device 7 to transmit the acquired image data to the image processing and analysis module 2 via a data transmission protocol 8.
Image processing and analyzing module 2: the acquired image data is processed and analyzed, including quality determination 13, anomaly detection 12, and feature extraction 11. The module may analyze and determine the image data using image processing and computer vision techniques such as image recognition 9, object detection 10, feature extraction 11, and anomaly detection 12 algorithms.
Decision support module 3: based on the image processing and analysis results, decision support is provided, including quality decisions 13, adjustment recommendations 14, and protocol recommendations 15. The module can use a rule engine, machine learning and data mining technology to carry out decision support according to image processing and analysis results and generate corresponding suggestions and plans.
Real-time feedback and adjustment module 4: and alarming unqualified abnormal phenomena according to the decision support result, and giving an alarm to an operator. If no response is obtained within the threshold time, the system will automatically determine and automatically process based on design plans 18, history plans 19, and remote collaboration. The module can use the alarm system 16, remote communication and remote cooperation technology to realize real-time feedback and adjustment, and improve response speed and processing efficiency.
An automatic intelligent image inspection quality control management method specifically comprises the following steps:
s1, image acquisition
Using a camera and other image acquisition equipment to acquire images of the product, wherein the acquired image data can be a static image and a dynamic video, and using a high-resolution camera to acquire the images of the surface of the product;
s2, image preprocessing
Preprocessing the acquired image data, including denoising, enhancement and color correction, wherein the preprocessing steps can be realized by using a digital image processing algorithm, denoising is performed by using a Gaussian filter, and histogram equalization is performed to enhance the image contrast;
s3, image analysis
Analyzing the preprocessed image, including target detection, image segmentation and feature extraction, wherein the analysis steps can be realized by using a computer vision algorithm, the target detection is performed by using a convolutional neural network, the image segmentation is performed by using a clustering algorithm, and key features are extracted by using a feature descriptor;
s4, image processing
According to the result of image analysis, image processing such as image restoration, morphological operation and filtering is carried out, wherein the processing steps can be realized by using an image processing algorithm, damaged image areas can be restored by using the image restoration algorithm, missing details are filled by using the morphological operation, and the image is smoothed by using a filter;
s5, image identification
The targets in the images are identified and classified by using machine learning and a mode identification algorithm, so that defects and abnormal conditions in products can be automatically identified, and the classification and identification of the defects of the products are performed by using a convolutional neural network;
s6, decision support
According to the image processing and analysis results, the system provides decision support, including quality judgment, adjustment suggestion and plan recommendation, so that an operator can be helped to make an accurate decision, quality judgment can be carried out according to the severity and influence range of the product defects, and corresponding adjustment suggestion and plan recommendation are provided;
s7, feeding back and adjusting in real time
If the unqualified abnormal phenomenon is found, the system gives an alarm and gives an alarm to an operator, the operator carries out corresponding adjustment according to the alarm information provided by the system, real-time feedback can be carried out by displaying the alarm information and the sound prompt, and corresponding adjustment guidance is provided;
s8, judging threshold time
If the operator does not respond within the set threshold time, the system automatically judges and processes according to the preset design plan, the history plan and the remote collaboration, and can judge whether the automatic processing is needed or not according to the set time threshold and the history data, and perform corresponding processing operation according to the preset design plan;
s9, remote notification and collaboration
The system carries out remote notification on the processing situation and cooperates with a remote expert to acquire more accurate judgment and processing advice, which can be realized through remote communication and cooperation technologies such as video conference, instant messaging and remote desktop, and can carry out real-time communication and cooperation with the remote expert through the video conference and a shared screen;
s10, recording a processing result
The system generates process reports and records, including quality problems, process results, and remote collaboration scenarios, which may be recorded and archived using data storage and report generation techniques for subsequent analysis and review, and may save the process results to a database and generate corresponding reports and records for subsequent analysis and review.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (8)
1. An automatic intelligent image inspection quality control management system comprises an image acquisition and transmission module (1), and is characterized in that: the image acquisition and transmission module (1) is connected with the image processing and analysis module (2), the image processing and analysis module (2) is connected with the decision support module (3), the decision support module (3) is connected with the real-time feedback and adjustment module (4), the real-time feedback and adjustment module (4) is connected with the remote notification and collaboration module (5), the remote notification and collaboration module (5) is connected with the generation report and recording module (6), and the image acquisition and transmission module (1), the image processing and analysis module (2), the decision support module (3), the real-time feedback and adjustment module (4), the remote notification and collaboration module (5) and the generation report and recording module (6) are sequentially connected and arranged.
2. The automated intelligent image inspection quality control management system of claim 1, wherein: the image acquisition and transmission module (1) comprises an image acquisition device (7) and a data transmission protocol (8), and the image acquisition device (7) is connected to the data transmission protocol (8).
3. The automated intelligent image inspection quality control management system of claim 1, wherein: the image processing and analyzing module (2) comprises image recognition (9), target detection (10), feature extraction (11) and abnormality detection (12).
4. The automated intelligent image inspection quality control management system of claim 1, wherein: the decision support module (3) comprises a quality decision (13), an adjustment suggestion (14) and a protocol recommendation (15).
5. The automated intelligent image inspection quality control management system of claim 1, wherein: the real-time feedback and adjustment module (4) comprises an alarm system (16), an automatic adjustment (17), a design plan (18) and a history plan (19).
6. The automated intelligent image inspection quality control management system of claim 1, wherein: the remote notification and collaboration module (5) includes a communication collaboration platform (20) and a remote expert collaboration (21).
7. The automated intelligent image inspection quality control management system of claim 1, wherein: the generation report and recording module (6) comprises a process result record archive (22) and a process report generation (23).
8. An automatic intelligent image inspection quality control management method is characterized by comprising the following steps:
s1, image acquisition
The image data for image acquisition of the product can be a static image and a dynamic video by using a camera and other image acquisition equipment;
s2, image preprocessing
Preprocessing the acquired image data, including denoising, enhancement and color correction, can improve the image quality and reduce noise and distortion;
s3, image analysis
Analyzing the preprocessed image, including target detection, image segmentation and feature extraction, so that key information and features in the image can be extracted;
s4, image processing
According to the result of image analysis, image processing such as image restoration, morphological operation and filtering is performed, so that the quality and definition of the image can be improved;
s5, image identification
The targets in the images are identified and classified by using machine learning and a mode identification algorithm, so that defects and abnormal conditions in products can be automatically identified;
s6, decision support
According to the image processing and analysis results, the system provides decision support, including quality judgment, adjustment suggestion and plan recommendation, so that an operator can be helped to make an accurate decision;
s7, feeding back and adjusting in real time
If the unqualified abnormal phenomenon is found, the system alarms and gives a warning to an operator, and the operator carries out corresponding adjustment according to warning information provided by the system;
s8, judging threshold time
If the operator does not respond within the set threshold time, the system automatically judges according to the preset design plan, the history plan and the remote collaboration and automatically processes the judgment;
s9, remote notification and collaboration
The system carries out remote notification on the processing situation and cooperates with a remote expert to acquire more accurate judgment and processing advice, which can be realized through remote communication and cooperation technologies such as video conference, instant messaging and remote desktop;
s10, recording a processing result
The system generates process reports and records, including quality issues, process results, and remote collaboration scenarios, which may be recorded and archived using data storage and report generation techniques for subsequent analysis and review.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311483510.4A CN117575382A (en) | 2023-11-09 | 2023-11-09 | Automatic intelligent image inspection quality control management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311483510.4A CN117575382A (en) | 2023-11-09 | 2023-11-09 | Automatic intelligent image inspection quality control management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117575382A true CN117575382A (en) | 2024-02-20 |
Family
ID=89861583
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311483510.4A Pending CN117575382A (en) | 2023-11-09 | 2023-11-09 | Automatic intelligent image inspection quality control management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117575382A (en) |
-
2023
- 2023-11-09 CN CN202311483510.4A patent/CN117575382A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110084165B (en) | Intelligent identification and early warning method for abnormal events in open scene of power field based on edge calculation | |
| CN110826538A (en) | Abnormal off-duty identification system for electric power business hall | |
| CN110366031A (en) | A kind of monitoring abnormal state and method for diagnosing faults digitizing workshop MES system view-based access control model | |
| CN104966304A (en) | Kalman filtering and nonparametric background model-based multi-target detection tracking method | |
| CN110096945B (en) | Indoor monitoring video key frame real-time extraction method based on machine learning | |
| CN117419814A (en) | Image processing system based on infrared thermal image of electrical equipment | |
| CN113378648A (en) | Artificial intelligence port and wharf monitoring method | |
| CN115802027A (en) | Online monitoring equipment operation abnormity detection method based on image processing | |
| WO2020247358A1 (en) | Method and system for connected advanced flare analytics | |
| CN117152161B (en) | Shaving board quality detection method and system based on image recognition | |
| EP3751245A1 (en) | Smart sensor measurement system | |
| CN110611793B (en) | Supply chain information acquisition and data analysis method and device based on industrial vision | |
| CN116168019B (en) | Power grid fault detection method and system based on machine vision technology | |
| CN117114420A (en) | Image recognition-based industrial and trade safety accident risk management and control system and method | |
| CN116840240A (en) | Visual detection system of power supply distributor | |
| CN117575382A (en) | Automatic intelligent image inspection quality control management system | |
| CN107124577A (en) | A kind of real-time alarm system for guarding against theft based on moving object detection | |
| CN110930362A (en) | Screw safety detection method, device and system | |
| CN115039124A (en) | Image processing method, electronic equipment, automobile data recorder and server | |
| CN115082865B (en) | Bridge crane intrusion dangerous behavior early warning method and system based on visual image recognition | |
| CN108181315B (en) | Image processing-based biscuit damage detection device and detection method | |
| CN111222428B (en) | Detection method for device fastening operation based on monitoring video | |
| US20240104911A1 (en) | Information processing device | |
| CN117723739A (en) | Quality analysis method and system for low-carbon lubricating oil | |
| CN117935102A (en) | Intelligent detection method and system for coal face based on intelligent analysis of streaming media |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |