CN111738322A - Method, device, equipment and medium for detecting surface defects of article - Google Patents
Method, device, equipment and medium for detecting surface defects of article Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, equipment and a medium for detecting surface defects of an article, and relates to the fields of artificial intelligence computer vision, deep learning and cloud computing, in particular to an image recognition technology. The specific implementation scheme is as follows: inputting the surface image of the object to be detected into a defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks; extracting medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model; matching the intermediate image features with intermediate image features in a preset intermediate image feature library; and correcting the defect type determined by the defect detection model according to the matching result. According to the defect detection method and device, when the newly added defect type is identified, the defect detection model does not need to be updated, and the defect detection model can be directly matched through the preset intermediate image feature library, so that the detection efficiency is further improved.
Description
Technical Field
The embodiment of the application relates to the fields of artificial intelligence computer vision, deep learning and cloud computing, in particular to an image recognition technology.
Background
In the field of manufacturing, there is a need for surface defect detection of manufactured products. For example, in the case of textile cloth, the defect detection is required to be performed on the surface defects of the textile cloth, and the defect types are required to be distinguished.
Several defect detection methods adopted at present have strong pertinence to the detection of specific types of defects, and once new types of defects appear, the original detection accuracy is reduced, or the detection model for deep learning needs to be retrained to detect the new types of defects. Therefore, the training cost of the detection model is high, and the detection requirement of new defects cannot be met in time.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for detecting surface defects of an article.
According to a first aspect, there is provided a method of detecting surface defects of an article, comprising:
inputting the surface image of the object to be detected into a defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks;
extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model;
matching the intermediate image features with intermediate image features in a preset intermediate image feature library;
correcting the defect type determined by the defect detection model according to the matching result; and the intermediate image features in the preset intermediate image feature library are features extracted from at least one layer of feature extraction network after the defect detection model is input based on the surface image of the article marked with the defect type.
According to a second aspect, there is provided an apparatus for detecting surface defects of an article, comprising:
the defect type identification module is used for inputting the surface image of the object to be detected into the defect detection model so as to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks;
the image feature extraction module is used for extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model;
the image feature matching module is used for matching the intermediate image features with intermediate image features in a preset intermediate image feature library;
the defect type correction module is used for correcting the defect type determined by the defect detection model according to the matching result; and the intermediate image features in the preset intermediate image feature library are features extracted from at least one layer of feature extraction network after the defect detection model is input based on the surface image of the article marked with the defect type.
According to a third aspect, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method for detecting surface defects of an article as provided in any of the embodiments of the present application.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method of detecting surface defects of an article as provided in any of the embodiments of the present application.
The technology according to the application improves the detection efficiency of the surface image of the object.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic flow chart of a method for detecting surface defects of an article according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of another method for detecting surface defects of an article according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an anomaly detection sliding window located according to defect types according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating defect location information of an image of a surface of an article detected according to a defect detection model according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an apparatus for detecting surface defects of an article according to an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a method for detecting surface defects of an article according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic flow chart of a method for detecting a defect on a surface of an article according to an embodiment of the present application, which is applicable to a case of detecting a defect on a surface image of an article. The method can be executed by a device for detecting the surface defects of the article, which can be realized by adopting a hardware and/or software mode and can be configured in electronic equipment. The method specifically comprises the following steps:
s110, inputting the surface image of the object to be detected into a defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks.
In the embodiment of the application, the defect detection model is a non-artificial network model constructed based on deep learning and is mainly used for identifying the defects of the surface image of the article to be detected so as to identify the defect types of the surface image of the article to be detected; the defect type of the surface image of the object to be detected can be different for different object surfaces. For example, for textiles, defect types may include holes, broken needles, or running threads, among others.
Specifically, the defect detection model in the embodiment of the application is configured to include at least two layers of feature extraction networks, and can extract feature information of different layers of the surface image of the object to be detected by finely dividing the surface image of the object to be detected at different layers, and extract feature information of each layer of the surface image of the object to be detected with different complexity, so that the extracted feature information can comprehensively represent features of the surface image of the object to be detected.
And S120, extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model.
In the embodiment of the application, the intermediate-level image features are high-dimensional information of the surface image of the object to be detected extracted through the feature extraction network layer of the defect detection model, and the surface image of the object to be detected can be represented more completely, so that the defect type determined by the defect detection model can be accurately judged according to the intermediate-level image features quickly and effectively in the follow-up process. For example, a feature extraction network, which is a convolutional layer, outputs a feature after processing an object surface image through the convolutional layer, which is a medium-level image feature. For the defect identification model, one or more layers of feature extraction networks may be generally included, for example, a U-net model composed of a plurality of convolution layers, where each convolution layer may serve as a feature extraction network for extracting a middle-level image feature, and it may be determined from which network layer the middle-level image feature is extracted if necessary.
And S130, matching the intermediate image features with the intermediate image features in a preset intermediate image feature library.
In the embodiment of the application, different defect types and corresponding different intermediate-level image features are stored in a preset intermediate-level image feature library, each intermediate-level image feature is associated with one defect type, when the preset intermediate-level image feature library is used for matching the intermediate-level image features, the same intermediate-level image features can be searched in the preset intermediate-level image feature library in a traversing manner through the intermediate-level image features, and the defect type associated with the intermediate-level image features with the matching degree reaching the set conditions is used as the defect type matched according to the intermediate-level image features. The preset intermediate image feature library can input the defect images with known defect types into a defect detection model, and extract the intermediate image features extracted by the network from each feature. The intermediate image features output by different feature extraction networks can be respectively stored and compared and matched.
Specifically, if the defect type of the surface image of the object to be detected belongs to a new type, that is, the image sample of the new defect type is not trained in the defect detection model, at this time, the accuracy of the defect type determined only by the defect detection model is low, the embodiment of the application can directly extract the medium-level image features of the surface image of the object to be detected, and perform matching in the preset medium-level image feature library to obtain the accurate defect type of the surface image of the object to be detected. In the preset intermediate-level image feature library, images of known new defect types can be input into the defect detection model and extracted from the feature extraction network layer. Compared with the training defect detection model, the method can extract the intermediate-level image features of several groups of new defect types through fewer new defect type image samples, and therefore, the method is equivalent to the method capable of quickly establishing the identification mode of the new defect types.
S140, correcting the defect type determined by the defect detection model according to the matching result; the medium-level image features in the preset medium-level image feature library are features extracted from at least one layer of feature extraction network after inputting a defect detection model based on the surface image of the article marked with the defect types.
In the embodiment of the application, the surface image of the article needs to be manually labeled, then the intermediate-level image features are output according to the defect detection model and stored in the preset intermediate-level image feature library, so that the intermediate-level image features stored in the preset intermediate-level image feature library and the associated defect types have higher accuracy.
Specifically, matching the intermediate image characteristics with a preset intermediate image characteristic library to obtain defect types, and comparing the defect types determined by the defect detection model; and correcting the defect types determined by the defect detection model according to the comparison result. According to the embodiment, after the defect type of the surface image of the object to be detected is determined through the defect detection model, the detection result of the defect detection model is corrected through the preset intermediate-level image feature library, and the accuracy of the defect type of the surface image of the object to be detected is effectively improved.
According to the technical scheme of the embodiment, the defect type of the surface image of the object to be detected is identified through a defect detection model, and the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model are extracted; matching the intermediate image features with intermediate image features in a preset intermediate image feature library; and correcting the defect type determined by the defect detection model according to the matching result. According to the method and the device, after the defect type of the surface image of the object to be detected is determined through the defect detection model, the detection result of the defect detection model is corrected through the preset intermediate image feature library, and the accuracy of the defect type of the surface image of the object to be detected is effectively improved; when the newly added defect types are identified, a large number of image samples of the new defect types do not need to be collected, a large amount of time is spent on retraining the defect detection model, and the defect detection model can be directly matched through a preset intermediate image feature library, so that the detection efficiency is further improved.
The embodiment of the application also provides a preferable implementation mode of the method for detecting the surface defects of the article, and the method can be used for rapidly determining the surface image of the article to be detected according to the abnormal points. Fig. 2 is a schematic flow chart of another method for detecting surface defects of an article according to an embodiment of the present application. The method specifically comprises the following steps:
s210, identifying abnormal points in the surface image of the original object to be detected based on an abnormal point identification algorithm.
When the initial position of the defect is determined conventionally, a sliding window mode is adopted, the image position is traversed continuously in the surface image of the original article to be detected, and a plurality of pseudo windows can be found out in the traversal result, so that the calculation amount of the whole detection process is increased, and the detection process is complicated. In order to solve the problem, an abnormal point identification algorithm is adopted in the embodiment of the application, and a rectangular frame of which the position is to be accurately determined is generated according to the known defect type; and according to the determined rectangular frame, identifying abnormal points in the surface image of the original object to be detected.
And S220, determining candidate defect positions according to the identified abnormal point positions.
In the embodiment of the application, an image containing the position of an abnormal point in an image of the surface of an original article to be detected is sent to a detection network for learning by using a deep learning supervision technology, so that a regressive detection frame position (namely a candidate defect position) is obtained. Wherein, at least one detection frame position may be included in one original article surface image to be detected. As shown in fig. 4, the odd columns are defective images, and the even columns determine the detection positions of the defective images.
S230, shearing the surface image of the original article to be detected according to the candidate defect position to obtain the surface image of the article to be detected; the surface image of the object to be measured comprises at least one outlier.
In the embodiment of the application, in order to avoid the problem that the detection process is complex due to the fact that multiple defect types in one original article surface image to be detected are detected simultaneously, the original article surface image to be detected is cut, multiple article surface images to be detected are obtained, and the detection efficiency can be effectively improved. Further, by the cropping, it is also possible to match the size of the defect image with the input size of the defect detection model.
According to the method and the device, based on the abnormal point identification algorithm, the abnormal points are positioned in the surface image of the original article to be detected through the determined detection frame, the candidate defect positions are determined according to the abnormal point positions, and then the surface image of the original article to be detected is cut according to the candidate defect positions to obtain the surface image of the article to be detected. The problem that the recognition efficiency is low due to the fact that defects are directly recognized in the surface image of the object to be detected in the traditional mode can be solved, and the recognition speed of the defects of the surface image of the object to be detected is effectively increased.
S240, inputting the surface image of the object to be detected into a defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks.
And S250, extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model.
And S260, matching the intermediate image features with the intermediate image features in a preset intermediate image feature library.
S270, correcting the defect type determined by the defect detection model according to the matching result; the medium-level image features in the preset medium-level image feature library are features extracted from at least one layer of feature extraction network after inputting a defect detection model based on the surface image of the article marked with the defect types.
Optionally, the defect detection model is obtained by training based on the surface images of the article corresponding to the at least two defect types, and the labeled defect type is the same as or different from the defect type subjected to the training. The different defect types are newly added defect types.
In the embodiment of the application, the surface images of the article corresponding to at least two defect types are used as training samples, and the defect detection model is trained to obtain the defect detection model; wherein, the defect type of the surface image of the article is obtained based on manual marking; according to the method, the defect detection model is trained according to the surface images of the articles with known defect types, so that the universality of the trained defect detection model can be effectively improved. Optionally, the defect detection model at least includes a convolutional neural network model, and when the defect identification is performed on the clipped image of the new defect type, the feature of the intermediate-level image can be directly determined according to the input clipped image, and the feature of the intermediate-level image is matched with the defect type in the preset intermediate-level image feature library, so that the defect type of the clipped image of the new defect type can be quickly determined.
Optionally, after S240, the method further includes:
extracting primary image characteristics from the surface image of the object to be detected; and matching the primary image features with the primary image features in a preset primary image feature library.
Accordingly, S270 includes: and correcting the defect type determined by the defect detection model according to the intermediate matching result and the primary matching result.
In the embodiment of the application, the primary image feature is a basic feature of the surface image of the object to be detected; optionally, the primary image features include gray scale distribution features and/or moment features of the surface image of the object to be detected, and effective features of the surface image of the object to be detected can be simply and integrally expressed through the primary image features, so that matching efficiency of defect types in a preset primary image feature library is improved. The gray distribution feature is, for example, a statistical histogram of the gray levels of pixels of the image of the surface of the object.
Matching defect types related to primary image features extracted from the surface image of the object to be detected in a preset primary image feature library, and correcting the defect types determined by the defect detection model according to the defect types obtained from the primary matching result and the defect types obtained from the intermediate matching result; the preset primary image feature library and the preset intermediate image feature library can support flexible configuration and change of a user; the accuracy of the defect type determined by the defect detection model can be effectively detected, so that the more accurate defect type of the surface image of the object to be detected can be obtained.
Optionally, S270 includes:
if the matching result is the defect type corresponding to the matched intermediate-level image features and is consistent with the defect type determined by identification, determining that the defect type determined by identification is the final defect type;
and if the matching result is that the defect type corresponding to the matched intermediate-level image features is inconsistent with the defect type determined by identification, determining that the matched defect type is the final defect type or reporting an abnormal identification phenomenon.
In the embodiment of the application, the defect types of the surface image of the object to be detected may be various, however, the defect types related in the training sample of the defect detection model are only marked manually, so that the defect types have great limitation; when the defect type matched by the middle-level image features is inconsistent with the defect type identified by the defect detection model, the defect type of the surface image of the object to be detected may be a new defect type, and the defect type is not in the training sample of the defect detection model, the matching result of the middle-level image features is compared with the identification result of the defect detection model, so that the problem that the detection result has a large error due to the fact that the defect type determined by the defect detection model is wrong can be effectively solved, and the detection efficiency is effectively improved.
Optionally, after the surface image of the object to be detected is input into the defect detection model, the method further includes: and outputting the contour coordinates with the defects.
In the embodiment of the application, the surface image of the object to be detected is input, the contour coordinate with the defect can be output through the defect detection model, so that the contour coordinate of the defect is mapped to the surface image of the original object to be detected, and the defect coordinate in the surface image of the original object to be detected is quickly determined.
Optionally, S210 includes:
determining an abnormal detection sliding window for each defect type, wherein the abnormal detection sliding window comprises a transverse sliding window and/or a longitudinal sliding window;
and traversing the surface image of the original article to be detected by adopting the abnormal detection sliding window, and identifying abnormal points in the abnormal detection sliding window based on an abnormal point identification algorithm.
In the embodiment of the application, the abnormal defect type corresponds to the defect type so as to solve the problem of occurrence of a false window in the traditional detection method, thereby reducing the calculation amount and the position of a target window. Specifically, the size and aspect ratio of the abnormality detection window correspond to the type of defect. FIG. 3 is a schematic diagram of an anomaly detection sliding window for preliminary defect type localization according to an embodiment of the present disclosure; for example, if five defect types are known, five corresponding sizes and length-width ratios of the abnormal detection sliding window may exist, and the five sliding windows respectively traverse the surface image of the original object to be detected five times to locate abnormal points; wherein the abnormality detection sliding window may include both a lateral sliding and a vertical sliding. And identifying abnormal points in the abnormal detection sliding window based on an abnormal point identification algorithm by using the determined abnormal detection sliding window, so that the detection rate and accuracy of the abnormal points can be effectively improved.
Optionally, the outlier identification algorithm includes at least one of: an abnormal gray distribution algorithm, an abnormal edge identification algorithm and an abnormal corner identification algorithm.
In the embodiment of the application, the adopted abnormal point identification algorithm is not limited to the identification of edge key points, and can also comprise the identification based on gray distribution and the identification of abnormal corner points, so that the abnormal points can be quickly and accurately identified according to different types of original article surface images to be detected.
Optionally, in this embodiment, the article is a piece of cloth, and before S240, the method further includes:
continuously shooting and collecting images of the moving cloth through at least one shooting device to obtain images of each object to be detected;
and respectively setting electronic identification for each object image to be detected according to the shooting equipment and the shooting sequence.
Correspondingly, after S270, the method further includes: and storing the determined defect type corresponding to the electronic identification.
In the embodiment of the application, when the cloth normally works, shooting equipment (such as a camera) is adopted to shoot the cloth uninterruptedly; illustratively, the shooting scene can be about 1.5 m-2 m cloth width, and 7 industrial cameras are used for collecting the cloth together, wherein the predicted moving speed of the cloth is 0.5 m-1 m/s; each camera collected data in a format of about 512 x 512; corresponding to an area of about 20cm by 20cm of the piece; this speed of movement allows to achieve an inspection length of at least 30m per minute, twice the efficiency of manual cloth inspection. The cameras need to be numbered, for example, the numbers collected by the N cameras may be 1, 2, …, N; the shooting order number Q of the camera is 1, 2, …, Q. If one image of the object to be detected is obtained by the 3 rd camera and the 5 th shooting of the third camera, the electronic identifier of the image of the object to be detected is (3, 5); each object image to be detected corresponds to one group of defect type codes, and the defect type codes can be used as electronic identifiers of the object images to be detected, and are used for carrying out classification management on different pieces of cloth, so that the management efficiency is effectively improved.
Optionally, the continuously capturing images of the moving piece of cloth by at least one capturing device includes:
continuously shooting and collecting images of the moving cloth through at least two shooting devices; wherein, shooting equipment sets up side by side, and the direction that sets up side by side is perpendicular with the moving direction of cloth.
In this embodiment, in order to avoid shooting the problem that the equipment is difficult to effectual cloth in moving carries out accurate collection, through setting up two at least shooting equipment of placing side by side, carry out the continuous shooting collection to cloth in the moving, effectively prevent to miss and adopt, can be comparatively complete gather the article image that awaits measuring.
Optionally, after storing the determined defect type corresponding to the electronic identifier, the method further includes:
and determining the position of the corresponding defect type in the cloth according to each electronic identifier in the cloth, and performing statistical analysis according to the position and the defect type to determine the abnormity of weaving equipment of the cloth or a cutting scheme of subsequent cloth cutting equipment.
Specifically, the number (N) of the image of the cloth collected by each camera may be respectively numbered, the collected images may be numbered (Q) according to the collection time sequence, and then the number (M) of the identified defect type may be determined according to the identification result, and the number may be added to the electronic tag of the cloth. For example, the format shown in table 1 below:
TABLE 1
| Camera numbering | Collection serial number | Kind of defect |
| N | Q | M |
In the embodiment of the application, the electronic identification, the defect type and the defect quantity of a plurality of pieces of cloth are counted on different production lines, the defect rule in the production line is analyzed, and the production line defect statistical information is generated, so that the quality inspection of a woven cloth production line and the subsequent cutting of the cloth using the production line and the classified use of different production lines of the cloth are favorably guided; meanwhile, the quality inspection identifier is used without stopping the equipment, so that the production efficiency is greatly improved.
Fig. 5 is a schematic structural diagram of an apparatus for detecting surface defects of an article according to an embodiment of the present application, where the embodiment is applicable to a case of detecting surface defects of an article, and the apparatus is configured in an electronic device, and the apparatus 500 for detecting surface defects of an article according to any embodiment of the present application specifically includes the following components:
a defect type identification module 510, configured to input the surface image of the object to be detected into the defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks;
an image feature extraction module 520, configured to extract a medium-level image feature output by at least one layer of feature extraction network in the identification process of the defect detection model;
an image feature matching module 530, configured to match the intermediate-level image features with intermediate-level image features in a preset intermediate-level image feature library;
a defect type correction module 540, configured to correct the defect type determined by the defect detection model according to the matching result; and the intermediate image features in the preset intermediate image feature library are features extracted from at least one layer of feature extraction network after the defect detection model is input based on the surface image of the article marked with the defect type.
Optionally, the defect detection model is obtained by training based on surface images of the article corresponding to at least two defect types, and the labeled defect type is the same as or different from the defect type subjected to training.
Optionally, the defect detection model at least includes a convolutional neural network model.
Optionally, the method further includes:
the image feature extraction module 520 is further configured to extract primary image features from the surface image of the object to be detected;
an image feature matching module 530, configured to match the primary image features with primary image features in a preset primary image feature library;
accordingly, the defect type correction module 540 is specifically configured to:
and correcting the defect type determined by the defect detection model according to the intermediate matching result and the primary matching result.
Optionally, the primary image feature includes a gray scale distribution feature and/or a moment feature of the surface image of the object to be measured.
Optionally, the defect type correcting module 540 is further specifically configured to:
if the matching result is the defect type corresponding to the matched intermediate-level image features and is consistent with the defect type determined by identification, determining that the defect type determined by identification is the final defect type;
and if the matching result is that the defect type corresponding to the matched intermediate-level image features is inconsistent with the defect type determined by identification, determining that the matched defect type is the final defect type or reporting an abnormal identification phenomenon.
Optionally, the method further includes:
and the coordinate output module is used for outputting the contour coordinates with the defects.
Optionally, the method further includes:
and the abnormal point identification module is used for identifying abnormal points in the surface image of the original article to be detected based on an abnormal point identification algorithm.
The defect position determining module is used for determining candidate defect positions according to the identified abnormal point positions;
the image shearing module is used for shearing the surface image of the original article to be detected according to the candidate defect position so as to obtain the surface image of the article to be detected; the surface image of the object to be measured comprises at least one abnormal point.
Optionally, the abnormal point identifying module is specifically configured to:
determining an anomaly detection sliding window for each defect type, wherein the anomaly detection sliding window comprises a transverse sliding window and/or a longitudinal sliding window;
traversing in the surface image of the original article to be detected by adopting the abnormal detection sliding window, and identifying abnormal points in the abnormal detection sliding window based on an abnormal point identification algorithm.
Optionally, the outlier identification algorithm includes at least one of: an abnormal gray distribution algorithm, an abnormal edge identification algorithm and an abnormal corner identification algorithm.
Optionally, the method further includes:
the image acquisition module is used for continuously shooting and acquiring images of the moving cloth through at least one shooting device so as to acquire images of each object to be detected;
the electronic identifier setting module is used for respectively setting electronic identifiers for the images of the objects to be detected according to the shooting equipment and the shooting sequence;
and the defect type storage module is used for storing the determined defect type corresponding to the electronic identifier.
Optionally, the image obtaining module is specifically configured to:
continuously shooting and collecting images of the moving cloth through at least two shooting devices; the shooting equipment is arranged side by side, and the direction of the side by side arrangement is perpendicular to the moving direction of the cloth.
Optionally, the method further includes:
and the statistical analysis module is used for determining the position of the corresponding defect type in the cloth according to each electronic identifier in the cloth and performing statistical analysis according to the position and the defect type so as to determine the abnormity of weaving equipment of the cloth or the cutting scheme of subsequent cloth cutting equipment.
According to the technical scheme of the embodiment, the accuracy of the image defect types on the surface of the object to be detected is effectively improved; when the newly added defect type is identified, the defect detection model does not need to be updated, and the defect detection model can be directly matched through a preset intermediate image feature library, so that the detection efficiency is further improved.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 6 is a block diagram of an electronic device of a method for detecting surface defects of an article according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the method for detecting surface defects of an article provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the method for detecting surface defects of an article provided by the present application.
The memory 602, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for detecting surface defects of an article in the embodiments of the present application. The processor 601 executes various functional applications and data processing of the server by executing non-transitory software programs, instructions and modules stored in the memory 602, so as to implement the method for detecting surface defects of an article in the above method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the electronic device in accordance with detection of surface defects of the article, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory remotely located from the processor 601, and these remote memories may be connected over a network to electronics of the detection of surface defects on the article. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method for detecting surface defects of an article may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device for detection of surface defects of the article, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or like input device. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the accuracy of the image defect types on the surface of the article to be detected is effectively improved; when the newly added defect type is identified, the defect detection model does not need to be updated, and the defect detection model can be directly matched through a preset intermediate image feature library, so that the detection efficiency is further improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A method for detecting surface defects of an article, comprising:
inputting the surface image of the object to be detected into a defect detection model to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks;
extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model;
matching the intermediate image features with intermediate image features in a preset intermediate image feature library;
correcting the defect type determined by the defect detection model according to the matching result; and the intermediate image features in the preset intermediate image feature library are features extracted from at least one layer of feature extraction network after the defect detection model is input based on the surface image of the article marked with the defect type.
2. The method of claim 1, wherein the defect detection model is obtained by training based on surface images of the article corresponding to at least two defect types, and the labeled defect type is the same as or different from the defect type being trained.
3. The method of claim 2, wherein the defect detection model comprises at least a convolutional neural network model.
4. The method of claim 1, wherein after inputting the surface image of the object to be tested into a defect detection model to identify and determine the defect type, the method further comprises:
extracting primary image characteristics from the surface image of the object to be detected;
matching the primary image features with primary image features in a preset primary image feature library;
correspondingly, the step of correcting the defect type determined by the defect detection model according to the matching result comprises the following steps:
and correcting the defect type determined by the defect detection model according to the intermediate matching result and the primary matching result.
5. The method according to claim 4, wherein the primary image features comprise grey-scale distribution features and/or moment features of the surface image of the object to be tested.
6. The method of claim 1, wherein correcting the defect type determined by the defect detection model based on the matching result comprises:
if the matching result is the defect type corresponding to the matched intermediate-level image features and is consistent with the defect type determined by identification, determining that the defect type determined by identification is the final defect type;
and if the matching result is that the defect type corresponding to the matched intermediate-level image features is inconsistent with the defect type determined by identification, determining that the matched defect type is the final defect type or reporting an abnormal identification phenomenon.
7. The method of claim 1, wherein after inputting the image of the surface of the object to be tested into the defect detection model, the method further comprises:
and outputting the contour coordinates with the defects.
8. The method according to any one of claims 1-7, wherein before inputting the surface image of the object to be tested into the defect inspection model to identify and determine the defect type, the method further comprises:
identifying abnormal points in the surface image of the original article to be detected based on an abnormal point identification algorithm;
determining candidate defect positions according to the identified abnormal point positions;
according to the candidate defect position, shearing the surface image of the original article to be detected to obtain the surface image of the article to be detected; the surface image of the object to be measured comprises at least one abnormal point.
9. The method of claim 8, wherein identifying outliers in the original object surface image based on an outlier identification algorithm comprises:
determining an anomaly detection sliding window for each defect type, wherein the anomaly detection sliding window comprises a transverse sliding window and/or a longitudinal sliding window;
traversing in the surface image of the original article to be detected by adopting the abnormal detection sliding window, and identifying abnormal points in the abnormal detection sliding window based on an abnormal point identification algorithm.
10. The method of claim 8, wherein the outlier identification algorithm comprises at least one of: an abnormal gray distribution algorithm, an abnormal edge identification algorithm and an abnormal corner identification algorithm.
11. The method according to any one of claims 1 to 7, wherein the article is a piece of cloth, and before inputting the surface image of the article to be detected into the defect detection model to identify and determine the defect type, the method comprises:
continuously shooting and collecting images of the moving cloth through at least one shooting device to obtain images of each object to be detected;
respectively setting electronic identification for each article image to be detected according to the shooting equipment and the shooting sequence;
correspondingly, after the defect type determined by the defect detection model is corrected according to the matching result, the method also comprises the following steps;
and storing the determined defect type corresponding to the electronic identification.
12. The method of claim 11, wherein continuously capturing images of the moving piece of cloth with at least one camera comprises:
continuously shooting and collecting images of the moving cloth through at least two shooting devices; the shooting equipment is arranged side by side, and the direction of the side by side arrangement is perpendicular to the moving direction of the cloth.
13. The method of claim 11, wherein after storing the determined defect type corresponding to the electronic identifier, further comprising:
and determining the position of the corresponding defect type in the cloth according to each electronic identifier in the cloth, and performing statistical analysis according to the position and the defect type to determine the abnormity of weaving equipment of the cloth or a cutting scheme of subsequent cloth cutting equipment.
14. An apparatus for detecting surface defects of an article, comprising:
the defect type identification module is used for inputting the surface image of the object to be detected into the defect detection model so as to identify and determine the defect type; the defect detection model is a deep learning model and comprises at least two layers of feature extraction networks;
the image feature extraction module is used for extracting the medium-level image features output by at least one layer of feature extraction network in the identification process of the defect detection model;
the image feature matching module is used for matching the intermediate image features with intermediate image features in a preset intermediate image feature library;
the defect type correction module is used for correcting the defect type determined by the defect detection model according to the matching result; and the intermediate image features in the preset intermediate image feature library are features extracted from at least one layer of feature extraction network after the defect detection model is input based on the surface image of the article marked with the defect type.
15. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-13.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-13.
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