CN117315365A - Camshaft surface damage detecting system based on visual analysis - Google Patents
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- 238000012549 training Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 41
- 238000004364 calculation method Methods 0.000 claims abstract description 25
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- 238000012360 testing method Methods 0.000 claims description 8
- 230000010354 integration Effects 0.000 claims description 7
- 230000007797 corrosion Effects 0.000 claims description 6
- 238000005260 corrosion Methods 0.000 claims description 6
- 238000003706 image smoothing Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
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- 238000005299 abrasion Methods 0.000 claims description 3
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G06T2207/20081—Training; Learning
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
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Abstract
The invention discloses a cam shaft surface damage detection system based on visual analysis, which comprises: the data collection subsystem is used for collecting historical camshaft surface image data; the data processing subsystem is used for processing the historical camshaft surface image data to obtain a processed image data set; the model construction subsystem is used for constructing a machine vision model, and training the machine vision model through the processing image data set to obtain a damage detection model; the result generation subsystem is used for acquiring real-time camshaft surface image data, inputting the real-time camshaft surface image data into the damage detection model for calculation, and obtaining a detection result. The invention processes the cam shaft through visual analysis technology, can extract tiny details and characteristics in the image, and can accurately classify and detect through a machine learning algorithm. This makes it possible to detect minute damage to the camshaft surface, improving the sensitivity and accuracy of detection.
Description
Technical Field
The invention belongs to the field of visual analysis, and particularly relates to a cam shaft surface damage detection system based on visual analysis.
Background
Visual analysis techniques can detect without contacting the camshaft surface, without using sensors or equipment to directly contact the shaft surface. This helps to avoid potential damage or interference from contact. With high resolution image acquisition devices and computer processing techniques, visual analysis is able to rapidly process large amounts of image data in a short time. This makes automated inspection in a mass production environment possible, improving the efficiency of the production line. Visual analysis techniques may be applied to camshafts of different types and shapes, whether those of automotive engines or those in other industries. Only the algorithm and model need be adapted to accommodate different shaft surfaces and lesion characteristics.
Extraction of meaningful features from images of camshaft surfaces is important for detection of damage. However, certain types of damage may cause feature blurring or blurring, making feature extraction difficult. This may require manual design of more complex algorithms or the use of other sensor aids, while the visual analysis system may generate false positives when performing damage detection, such as false positives of normal surfaces as damage, or false negatives, such as failure to detect actual damage, which may require debugging and training to reduce the probability of false positives and false negatives, but still have some error rate.
Disclosure of Invention
The invention aims to provide a cam shaft surface damage detection system based on visual analysis, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a camshaft surface damage detection system based on visual analysis, comprising:
the data collection subsystem is used for collecting historical camshaft surface image data;
the data processing subsystem is connected with the data collecting subsystem and is used for processing the historical camshaft surface image data to obtain a processed image data set;
the model construction subsystem is connected with the data processing subsystem and is used for constructing a machine vision model, and training the machine vision model through the processed image data set to obtain a damage detection model;
the result generation subsystem is connected with the model construction subsystem and used for acquiring real-time camshaft surface image data, inputting the real-time camshaft surface image data into the damage detection model for calculation, and obtaining a detection result.
Preferably, the data collection subsystem comprises:
the camshaft external acquisition module is used for collecting historical camshaft external damage image data and carrying out image annotation to obtain external annotation data;
the camshaft internal acquisition module is used for collecting historical camshaft internal damage image data and carrying out image annotation to obtain internal annotation data;
and the integration module is used for integrating the external annotation data and the internal annotation data to obtain the historical camshaft surface image data.
Preferably, the data processing subsystem comprises:
the data screening module is used for carrying out data screening on the historical camshaft surface image data to obtain screened data;
and the image processing module is used for carrying out image processing on the screened data to obtain the processed image data set.
Preferably, the image processing module includes:
the image enhancement unit is used for performing super-resolution processing on the screened data to obtain an enhanced image data set;
the image noise reduction unit is used for carrying out residual noise weighting processing on the enhanced image data set to obtain a noise reduction image data set;
and the image smoothing unit is used for carrying out image smoothing processing on the noise reduction image data set to obtain the processing image data set.
Preferably, the model building subsystem comprises:
the machine model building module is used for building the machine vision model based on a feature extraction method;
and the training module is used for inputting the processed image data set into the machine vision model for training to obtain the damage detection model.
Preferably, the training module comprises:
the data set processing unit is used for extracting the processed image data set to obtain a training set and a testing set;
the model training unit is used for inputting the training set into the machine vision model for training to obtain a training model;
and the adjusting unit is used for inputting the test set into the training model to perform performance evaluation, obtaining a performance evaluation result, and performing parameter adjustment on the training model based on the performance evaluation result to obtain the damage detection model.
Preferably, the adjusting unit includes:
the extraction unit is used for randomly extracting the processed image data set to obtain a random data set;
the verification unit is used for inputting the random data set into the training model for verification and obtaining the performance evaluation result;
and the model fine-tuning unit is used for adjusting parameters in the training model based on the performance evaluation result to obtain the damage detection model.
Preferably, the result generation subsystem comprises:
the real-time data acquisition module is used for shooting a camshaft to be detected through the camera equipment and acquiring a real-time camshaft surface image dataset;
the result generation module is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree and the chemical damage degree;
and the result fitting module is used for fitting the physical damage calculation result and the chemical damage calculation result to obtain a cam shaft damage degree distinguishing degree fitting curve.
Preferably, the result generation module includes:
the influence factor calculation unit is used for calculating stress change factors in the cam shaft and obtaining the influence correlation degree and the chemical strain correlation degree of the cam shaft structure;
the damage calculation unit is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree of the camshaft and the chemical damage degree of the camshaft;
the damage correlation calculation unit is used for calculating the physical damage degree of the cam shaft through the structural influence correlation of the cam shaft to obtain the physical damage degree, and calculating the chemical damage degree of the cam shaft through the chemical strain correlation to obtain the chemical damage degree;
the physical damage degree includes a degree of abrasion, a degree of corrosion, a degree of crack, a degree of scratch and cut, a degree of dent or bump, and a degree of welding defect;
the chemical damage degree includes corrosion degree and reaction degree.
The invention has the technical effects that:
the invention processes the cam shaft through visual analysis technology, can extract tiny details and characteristics in the image, and can accurately classify and detect the images through a machine learning algorithm. This makes it possible to detect minute damage to the camshaft surface, improving the sensitivity and accuracy of detection. The visual analysis technology does not damage the tested cam shaft and does not need to perform physical destructive testing. This is very advantageous for the detection of high value camshafts or samples, and additional costs and resource investments can be avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a schematic diagram of a system for detecting surface damage of a camshaft in an embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1, in this embodiment, a camshaft surface damage detection system based on visual analysis is provided, including:
the data collection subsystem is used for collecting historical camshaft surface image data;
the data processing subsystem is connected with the data collecting subsystem and is used for processing the historical camshaft surface image data to obtain a processed image data set;
the model construction subsystem is connected with the data processing subsystem and is used for constructing a machine vision model, and training the machine vision model through the processed image data set to obtain a damage detection model;
the result generation subsystem is connected with the model construction subsystem and used for acquiring real-time camshaft surface image data, inputting the real-time camshaft surface image data into the damage detection model for calculation, and obtaining a detection result.
Further optimizing scheme, the data collection subsystem includes:
the camshaft external acquisition module is used for collecting and marking historical camshaft external damage data to obtain external marking data;
the camshaft internal acquisition module is used for collecting and marking historical camshaft internal damage data to obtain internal marking data;
and the integration module is used for integrating the external annotation data and the internal annotation data to obtain the historical camshaft surface image data.
Ensuring the record and archiving of the damage condition of the camshaft in the using process. This may include the type, location, extent of damage, and other relevant information such as conditions of use, mileage, etc. The present embodiment creates a database or record table that collates and stores the data.
Through the collected historical data, the present embodiment performs statistics and analysis to understand the trend and characteristics of camshaft damage. The present embodiment uses data analysis tools to identify common types of damage, locations of occurrence, association of damage with conditions of use, and the like. This helps to improve product design, product quality control, and repair and maintenance strategies.
The database is in an xml format;
constructing an XML database typically requires the following steps:
determining a data structure based on the requirements: the data in the XML database is stored in the form of XML documents, so it is necessary to determine the data structure and define Document Type Definitions (DTD) or XML Schemas (XSD) to describe the format and rules of the data. These templates include elements, attributes, namespaces, etc.
From the data structure, an XML document template is created and designed, i.e., the elements, attributes, and relationships between them in the XML document are defined.
An appropriate XML database system, such as eXist-db, baseX, markLogic, etc., is selected according to application requirements. Some database management systems also support data importation and querying in XML format, such as Oracle and microsoft sqlserver.
Data is inserted into the database using a provided API or query language, such as XQuery or XPath, and the data is retrieved and queried from the database. It is noted here that the XML data stored in the database should conform to a pre-defined DTD or XSD specification.
The database is periodically backed up and maintained, and query sentences and indexes are optimized to improve the index efficiency and query speed.
Further optimizing scheme, the data processing subsystem includes:
the data screening module is used for carrying out data screening on the historical camshaft surface image data to obtain screened data;
and the image processing module is used for carrying out image processing on the screened data to obtain the processed image data set.
Further, the image processing module includes:
the image enhancement unit is used for carrying out image enhancement processing on the screened data to obtain an enhanced image data set;
the image noise reduction unit is used for carrying out image noise reduction processing on the enhanced image data set to obtain a noise reduction image data set;
and the image smoothing unit is used for carrying out image smoothing processing on the noise reduction image data set to obtain the processing image data set.
Dividing an original image to obtain a plurality of original sub-images; obtaining a segmentation edge of the original sub-image, and extending the segmentation edge to generate an extended sub-image; performing super-resolution processing on the extended sub-image, and amplifying the extended sub-image by n2 times to generate a super-resolution sub-image; combining the super-resolution sub-images, determining an overlapping region, extracting overlapping sub-images from the overlapping region, and acquiring information of the overlapping sub-images; and calculating the average channel value of each pixel point in the overlapping region according to the overlapping sub-image information, and generating a averaged sub-image.
Data integration refers to the collection, cleansing, conversion, and combination of data from different data sources and formats to generate consistent, accurate, reliable data sets. The following is the data integration procedure of this embodiment:
and (3) data collection: data to be integrated is collected from various data sources. These data sources may include databases, files, APIs, sensors, and the like.
Data cleaning: the collected data is initially cleaned and filtered to remove duplicate, erroneous or invalid data, and the eligible data is retained and stored in a separate data warehouse or directory.
Data conversion: and converting the data in different formats so as to adapt to the integrated data structure. This may involve conversion of date formats, unit conversion, data type conversion, etc.
Data integration: the cleaned and converted data are integrated into a single data set. This may be accomplished by way of concatenation, merging, aggregation, etc., to facilitate subsequent analysis and querying.
Data validation and testing: and verifying and testing the integrated data to ensure the quality and accuracy of the integrated data. This may require the use of tools and methods to detect outliers, missing values, inconsistent data, etc.
Data distribution and sharing: and publishing and sharing the integrated data to related users or systems. This may be accomplished by web services, APIs, shared files, etc. to facilitate querying and analysis by the user.
Data maintenance and update: and maintaining and updating the integrated data so as to keep the real-time performance and the correctness of the integrated data. This may require regular data cleaning, conversion and integration processes.
Further optimizing scheme, the model construction subsystem comprises:
the machine model building module is used for building the machine vision model based on a feature extraction method;
constructing a machine model based on a principal component analysis method;
a suitable machine learning algorithm or model architecture is selected and the model is trained using a training set. Selecting a proper algorithm, such as a decision tree, a support vector machine, a neural network and the like, according to the requirements of the problem, wherein the neural network is adopted in the embodiment;
the trained model is evaluated using the validation set to learn its performance on the new data.
And the training module is used for inputting the processed image data set into the machine vision model for training to obtain the damage detection model.
Further optimizing scheme, training module includes:
the data set processing unit is used for extracting the processed image data set to obtain a training set and a testing set;
the model training unit is used for inputting the training set into the machine vision model for training to obtain a training model;
and the adjusting unit is used for inputting the test set into the training model to perform performance evaluation, obtaining a performance evaluation result, and performing parameter adjustment on the training model based on the performance evaluation result to obtain the damage detection model.
Further, the adjusting unit includes:
the extraction unit is used for randomly extracting the processed image data set to obtain a random data set;
the verification unit is used for inputting the random data set into the training model for verification and obtaining the performance evaluation result;
and the model fine-tuning unit is used for adjusting parameters in the training model based on the performance evaluation result to obtain the damage detection model.
Further optimizing scheme, the result generation subsystem comprises:
the real-time data acquisition module is used for shooting a camshaft to be detected through the camera equipment and acquiring a real-time camshaft surface image dataset;
the result generation module is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree and the chemical damage degree;
and the result fitting module is used for fitting the physical damage calculation result and the chemical damage calculation result to obtain a cam shaft damage degree distinguishing degree fitting curve.
Further, the result generating module includes:
the influence factor calculation unit is used for calculating stress change factors in the cam shaft and obtaining the influence correlation degree and the chemical strain correlation degree of the cam shaft structure;
the damage calculation unit is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree of the camshaft and the chemical damage degree of the camshaft;
the damage correlation calculation unit is used for calculating the physical damage degree of the cam shaft through the structural influence correlation of the cam shaft to obtain the physical damage degree, and calculating the chemical damage degree of the cam shaft through the chemical strain correlation to obtain the chemical damage degree;
the physical damage degree includes a degree of abrasion, a degree of corrosion, a degree of crack, a degree of scratch and cut, a degree of dent or bump, and a degree of welding defect;
the chemical damage degree includes corrosion degree and reaction degree. The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A camshaft surface damage detection system based on visual analysis, comprising the steps of:
the data collection subsystem is used for collecting historical camshaft surface image data;
the data processing subsystem is connected with the data collecting subsystem and is used for processing the historical camshaft surface image data to obtain a processed image data set;
the model construction subsystem is connected with the data processing subsystem and is used for constructing a machine vision model, and training the machine vision model through the processed image data set to obtain a damage detection model;
the result generation subsystem is connected with the model construction subsystem and used for acquiring real-time camshaft surface image data, inputting the real-time camshaft surface image data into the damage detection model for calculation, and obtaining a detection result.
2. The visual analysis-based camshaft surface damage detection system of claim 1, wherein the data collection subsystem includes:
the camshaft external acquisition module is used for collecting historical camshaft external damage image data and carrying out image annotation to obtain external annotation data;
the camshaft internal acquisition module is used for collecting historical camshaft internal damage image data and carrying out image annotation to obtain internal annotation data;
and the integration module is used for integrating the external annotation data and the internal annotation data to obtain the historical camshaft surface image data.
3. The visual analysis-based camshaft surface damage detection system of claim 1, wherein the data processing subsystem includes:
the data screening module is used for carrying out data screening on the historical camshaft surface image data to obtain screened data;
and the image processing module is used for carrying out image processing on the screened data to obtain the processed image data set.
4. The visual analysis-based camshaft surface damage detection system of claim 3, wherein the image processing module includes:
the image enhancement unit is used for performing super-resolution processing on the screened data to obtain an enhanced image data set;
the image noise reduction unit is used for carrying out residual noise weighting processing on the enhanced image data set to obtain a noise reduction image data set;
and the image smoothing unit is used for carrying out image smoothing processing on the noise reduction image data set to obtain the processing image data set.
5. The visual analysis-based camshaft surface damage detection system of claim 1, wherein the model building subsystem comprises:
the machine model building module is used for building the machine vision model based on a feature extraction method;
and the training module is used for inputting the processed image data set into the machine vision model for training to obtain the damage detection model.
6. The visual analysis-based camshaft surface damage detection system of claim 5, wherein the training module comprises:
the data set processing unit is used for extracting the processed image data set to obtain a training set and a testing set;
the model training unit is used for inputting the training set into the machine vision model for training to obtain a training model;
and the adjusting unit is used for inputting the test set into the training model to perform performance evaluation, obtaining a performance evaluation result, and performing parameter adjustment on the training model based on the performance evaluation result to obtain the damage detection model.
7. The visual analysis-based camshaft surface damage detection system of claim 6, wherein the adjustment unit includes:
the extraction unit is used for randomly extracting the processed image data set to obtain a random data set;
the verification unit is used for inputting the random data set into the training model for verification and obtaining the performance evaluation result;
and the model fine-tuning unit is used for adjusting parameters in the training model based on the performance evaluation result to obtain the damage detection model.
8. The visual analysis-based camshaft surface damage detection system of claim 1, wherein the result generation subsystem includes:
the real-time data acquisition module is used for shooting a camshaft to be detected through the camera equipment and acquiring a real-time camshaft surface image dataset;
the result generation module is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree and the chemical damage degree;
and the result fitting module is used for fitting the physical damage calculation result and the chemical damage calculation result to obtain a cam shaft damage degree distinguishing degree fitting curve.
9. The visual analysis-based camshaft surface damage detection system of claim 8, wherein the result generation module includes:
the influence factor calculation unit is used for calculating stress change factors in the cam shaft and obtaining the influence correlation degree and the chemical strain correlation degree of the cam shaft structure;
the damage calculation unit is used for inputting the real-time camshaft surface influence data set into the damage detection model for calculation to obtain the physical damage degree of the camshaft and the chemical damage degree of the camshaft;
the damage correlation calculation unit is used for calculating the physical damage degree of the cam shaft through the structural influence correlation of the cam shaft to obtain the physical damage degree, and calculating the chemical damage degree of the cam shaft through the chemical strain correlation to obtain the chemical damage degree;
the physical damage degree includes a degree of abrasion, a degree of corrosion, a degree of crack, a degree of scratch and cut, a degree of dent or bump, and a degree of welding defect;
the chemical damage degree includes corrosion degree and reaction degree.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117593301A (en) * | 2024-01-18 | 2024-02-23 | 深圳市奥斯珂科技有限公司 | Machine vision-based memory bank damage rapid detection method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117593301A (en) * | 2024-01-18 | 2024-02-23 | 深圳市奥斯珂科技有限公司 | Machine vision-based memory bank damage rapid detection method and system |
| CN117593301B (en) * | 2024-01-18 | 2024-04-30 | 深圳市奥斯珂科技有限公司 | Machine vision-based memory bank damage rapid detection method and system |
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