CN114441547A - Intelligent household appliance cover plate defect detection method - Google Patents
Intelligent household appliance cover plate defect detection method Download PDFInfo
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- CN114441547A CN114441547A CN202210371774.XA CN202210371774A CN114441547A CN 114441547 A CN114441547 A CN 114441547A CN 202210371774 A CN202210371774 A CN 202210371774A CN 114441547 A CN114441547 A CN 114441547A
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- 238000001514 detection method Methods 0.000 title claims abstract description 90
- 230000007547 defect Effects 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims description 13
- 238000003062 neural network model Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 claims description 8
- 238000001914 filtration Methods 0.000 claims description 7
- 230000002708 enhancing effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000002950 deficient Effects 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 3
- 238000003711 image thresholding Methods 0.000 claims 1
- 238000005336 cracking Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004512 die casting Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007849 functional defect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
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- 238000005498 polishing Methods 0.000 description 1
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- 238000006748 scratching Methods 0.000 description 1
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- 238000011179 visual inspection Methods 0.000 description 1
- 230000037303 wrinkles Effects 0.000 description 1
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- 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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Abstract
The invention discloses a method for detecting the defect of an intelligent household appliance cover plate, which adopts intelligent household appliance cover plate defect detection equipment to detect the household appliance cover plate, and comprises the following steps: s1, respectively carrying out image acquisition on the cover plate on the detection platform by the cameras on the plurality of corresponding stations, and processing; s2, reading the configured detection parameters, performing algorithm detection on the cover plate on the detection platform, and respectively transmitting the cover plate to the corresponding areas according to the detection result; s3, carrying out prediction classification on the cover plates in the suspected areas; and S4, reclassifying the cover plates in the suspected area according to the prediction classification result. The detection method provided by the invention has the advantages that through setting reasonable credibility parameter values, the interferences of oil stains, dirt and the like which cannot be distinguished by the traditional algorithm are eliminated, and the requirement on the accuracy of quality defect detection of industrial parts can be met. The detection method of the invention not only improves the detection quality, but also improves the detection efficiency.
Description
Technical Field
The invention relates to the technical field of visual inspection, in particular to an intelligent household appliance cover plate defect detection method.
Background
In recent years, the technology development of the internet of things is becoming mature, interconnection and intercommunication under the same brand are basically realized as one ring of intelligent household appliances, and the intelligent household appliances have multiple functions of personalized management, equipment interconnection and intercommunication, man-machine interaction and the like. Functional defects such as cracking, hidden cracking, scratching, wrinkles, pressing package, concave-convex points and the like often appear in the production and die-casting process of the cover plate for the intelligent household appliance, the defects can shorten the service life of parts, and potential safety hazards can be brought to products using the parts. Therefore, before the magnetic parts are shipped, the magnetic parts need to be subjected to defect quality detection. At present, the defect detection methods of parts of industrial products mainly comprise a manual detection method, a traditional blob analysis detection method and a detection method based on deep learning. The manual detection efficiency is low, and the operation is usually carried out on an equipment production line, so that certain potential safety hazards are caused; the traditional blob analysis and detection method excessively depends on the polishing imaging effect of product parts, and can not distinguish the interference of oil stain, dirt and the like of partial defects and imaging thereof; the simple deep learning method has low detection accuracy and cannot meet the requirement of 0.1% of missed detection rate in industrial detection.
Disclosure of Invention
The invention aims to provide a method for detecting defects of a cover plate of an intelligent household appliance, which aims to solve the problems in the background technology.
In order to achieve the above object, the present invention provides an intelligent household appliance cover plate defect detection method, which adopts intelligent household appliance cover plate defect detection equipment to detect a household appliance cover plate, the detection equipment comprises a feeding belt, a material grabbing mechanical arm, a driving device, a detection platform, a camera and a PLC, the feeding belt is provided with a limiting block, the feeding belt is butted with a cover plate production line, a cover plate on the production line flows onto the feeding belt, the limiting block on the feeding belt is used for preliminarily positioning the cover plate, the material grabbing mechanical arm is used for grabbing the cover plate from the preliminary positioning area and placing the cover plate on the detection platform, the detection platform is driven to move at a constant speed by a driving device, a plurality of cameras are arranged on a cover plate surface linear array camera station, a cover plate side area array camera station and a cover plate chamfer area array camera station respectively; the detection method comprises the following steps:
s1, when the detection equipment detects that a cover plate needs to be detected on the detection platform, cameras on a cover plate surface linear array camera station, a cover plate side edge area array camera station and a cover plate chamfer area array camera station respectively acquire images of the cover plate on the detection platform, and a detection algorithm of each station is called for processing;
s2, reading the configured detection parameters by the PLC, performing algorithm detection on the cover plate on the detection platform, and respectively transmitting the cover plate to a good product area, a suspected product area and a defective product area according to the detection result;
s3, training a neural network model in an off-line manner, and predicting and classifying the cover plate in the suspected area by using the neural network model trained in the off-line manner;
and S4, according to the prediction classification result, the detection result of the whole cover plate is summarized again, and a corresponding instruction is sent to the PLC, so that the cover plate is put down to a corresponding feed opening.
Further, in step S1, the detection algorithms of the cover plate surface linear array camera station and the cover plate side linear array camera station include filtering and denoising of an image, enhancing an image defect effect, thresholding of the image, and analyzing an image blob; the detection algorithm of the area array camera station at the chamfer position of the cover plate comprises the algorithms of filtering and denoising of images, enhancing the defect effect of the images, thresholding the images, analyzing image blob and matching and positioning the images.
Further, in step S3, the specific steps of off-line training the neural network model are as follows:
s3.1, manually marking the positions and defect types of the defects of the defect images of the cover plate by using a marking tool, and storing the positions and defect types into corresponding file information;
s3.2, the program is compiled to analyze the marking information file of each defect image;
s3.3, loading an initialization model, and providing model input parameters according to the defect position and the category information of each image;
and S3.4, carrying out iterative training on the model until the verification accuracy reaches a preset value, and finishing the offline training of the model.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a method for detecting defects of an intelligent household appliance cover plate, which comprises the steps of firstly carrying out traditional algorithm processing on collected product part images, adding a reliability index to information extracted by processing the product part images, taking a traditional algorithm processing result as a final detection result if the reliability is greater than a set parameter value, otherwise, loading a trained neural network model to carry out classification processing on the product part images, and taking a classification result as a final detection result. The detection method provided by the invention has the advantages that through setting reasonable credibility parameter values, the interferences of oil stains, dirt and the like which cannot be distinguished by the traditional algorithm are eliminated, and the requirement on the accuracy of quality defect detection of industrial parts can be met. The detection method of the invention not only improves the detection quality, but also improves the detection efficiency.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for detecting defects of a cover plate of an intelligent household appliance according to the present invention;
FIG. 2 is a flowchart of the off-line model training process of the present invention.
Detailed Description
Embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways, which are defined and covered by the claims.
Please refer to fig. 1, this embodiment provides an intelligent household appliance cover plate defect detecting method, the detecting method adopts an intelligent household appliance cover plate defect detecting device to detect a household appliance cover plate, the detecting device includes a feeding belt, a grabbing manipulator, a driving device, a detecting platform, a video camera and a PLC, a limiting block is arranged on the feeding belt, the feeding belt is in butt joint with a cover plate production line, a cover plate on the production line flows into the feeding belt, the limiting block on the feeding belt is used for preliminarily positioning the cover plate, the grabbing manipulator is used for grabbing the cover plate from a preliminary positioning area and placing the cover plate on the detecting platform, and the driving device drives the detecting platform to move at a constant speed, the video camera is multiple, and the multiple video cameras are respectively arranged on a cover plate surface linear array camera station, a cover plate side array camera station and a cover plate chamfer position area camera station. The driving device is a servo motor, a sliding rail is arranged below the detection platform, and the detection platform is driven by the servo motor to move on the sliding rail at a constant speed. The detection method comprises the following steps:
s1, when the detection equipment detects that a cover plate needs to be detected on the detection platform, cameras on the cover plate surface linear array camera station, the cover plate side area array camera station and the cover plate chamfer area array camera station respectively acquire images of the cover plate on the detection platform, and a detection algorithm of each station is called for processing; specifically, the detection algorithm of the cover plate surface linear array camera station and the cover plate side edge area camera station comprises filtering and denoising of images, image defect effect enhancement, thresholding of the images and image blob analysis. The detection algorithm of the area array camera station at the chamfer position of the cover plate comprises the algorithms of filtering and denoising of images, enhancing the defect effect of the images, thresholding of the images, analyzing image blob and matching and positioning the images.
S2, reading the configured detection parameters by the PLC, carrying out traditional algorithm detection on the cover plate on the detection platform, and respectively transmitting the cover plate to a good product area, a suspected product area and a defective product area according to the detection result; the system algorithm detection mainly comprises the algorithms of filtering and denoising of images, enhancing image defect effects, thresholding of the images, analyzing image blob, matching and positioning of the images and the like. The cover plate is divided into good products, suspected products and defective products through traditional algorithm detection, and then the cover plate is respectively transmitted to corresponding areas according to detection results.
S3, training a neural network model in an off-line manner, and predicting and classifying the cover plate in the suspected area by using the neural network model trained in the off-line manner; the specific steps of off-line training the neural network model are as follows:
s3.1, manually marking the positions and defect types of the defects of the defect images of the cover plate by using a marking tool, and storing the positions and defect types into corresponding file information;
s3.2, the program is compiled to analyze the marking information file of each defect image;
s3.3, loading an initialization model, and providing model input parameters according to the defect position and the category information of each image;
and S3.4, carrying out iterative training on the model until the verification accuracy reaches a preset value, and finishing the offline training of the model.
And S4, according to the prediction classification result, the detection result of the whole cover plate is summarized again, and a corresponding instruction is sent to the PLC, so that the cover plate is put down to a corresponding feed opening.
According to the detection method, firstly, the collected product part (cover plate) image is processed through a traditional algorithm, a reliability index is added to information extracted through processing, if the reliability is higher than a set parameter value, the processing result of the traditional algorithm is used as a final detection result, otherwise, a trained neural network model is loaded to classify the product part (cover plate) image, and the classified result is used as the final detection result. The detection method provided by the invention has the advantages that through setting reasonable credibility parameter values, the interferences of oil stains, dirt and the like which cannot be distinguished by the traditional algorithm are eliminated, and the missing detection rate requirement of 0.1% on the quality defect detection of industrial parts can be met.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. An intelligent household appliance cover plate defect detection method is characterized in that intelligent household appliance cover plate defect detection equipment is adopted to detect a household appliance cover plate, the detection equipment comprises a feeding belt, a material grabbing mechanical arm, a driving device, a detection platform, a camera and a PLC, the feeding belt is provided with a limiting block, the feeding belt is butted with a cover plate production line, a cover plate on the production line flows onto the feeding belt, the limiting block on the feeding belt is used for preliminarily positioning the cover plate, the material grabbing mechanical arm is used for grabbing the cover plate from the preliminary positioning area and placing the cover plate on the detection platform, the detection platform is driven to move at a constant speed by a driving device, a plurality of cameras are arranged on a cover plate surface linear array camera station, a cover plate side area array camera station and a cover plate chamfer area array camera station respectively; the detection method comprises the following steps:
s1, when the detection equipment detects that a cover plate needs to be detected on the detection platform, cameras on a cover plate surface linear array camera station, a cover plate side edge area array camera station and a cover plate chamfer area array camera station respectively acquire images of the cover plate on the detection platform, and a detection algorithm of each station is called for processing;
s2, reading the configured detection parameters by the PLC, performing algorithm detection on the cover plate on the detection platform, and respectively transmitting the cover plate to a good product area, a suspected product area and a defective product area according to the detection result;
s3, training a neural network model in an off-line manner, and predicting and classifying the cover plate in the suspected area by using the neural network model trained in the off-line manner;
and S4, according to the prediction classification result, the detection result of the whole cover plate is summarized again, and a corresponding instruction is sent to the PLC, so that the cover plate is put down to a corresponding feed opening.
2. The method for detecting defects of a cover plate of an intelligent household appliance according to claim 1, wherein in the step S1, the detection algorithms of the cover plate surface linear array camera station and the cover plate side array camera station include image filtering and denoising, image defect effect enhancement, image thresholding and image blob analysis; the detection algorithm of the area array camera station at the chamfer position of the cover plate comprises the algorithms of filtering and denoising of images, enhancing the defect effect of the images, thresholding the images, analyzing image blob and matching and positioning the images.
3. The method for detecting the defect of the cover plate of the intelligent household appliance according to claim 1, wherein in the step S3, the specific steps of off-line training of the neural network model are as follows:
s3.1, manually marking the positions and defect types of the defects of the defect images of the cover plate by using a marking tool, and storing the positions and defect types into corresponding file information;
s3.2, the program is compiled to analyze the marking information file of each defect image;
s3.3, loading an initialization model, and providing model input parameters according to the defect position and the category information of each image;
and S3.4, carrying out iterative training on the model until the verification accuracy reaches a preset value, and finishing the offline training of the model.
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