CN114187237A - A conveyor belt tearing and deviation detection method, device, equipment and storage medium - Google Patents

Abstract

The application relates to a method, a device, equipment and a storage medium for detecting tearing and deviation of a conveying belt, wherein the method comprises the steps of acquiring video data of the conveying belt and constructing an image database; inputting data of an image database into a deep neural network model for training, and outputting abnormal image data characteristics; training a hyperparametric combination which enables the highest classification accuracy rate based on the support vector machine model to obtain the support vector machine model under the hyperparametric combination; enabling the deep neural network model and the support vector machine model to detect video data of the conveying belt; when a preset condition is met, the deep neural network model outputs a characteristic vector z, and the current video data of the conveying belt is judged to be abnormal; the support vector machine model outputs an abnormal result of tearing or deviation of the conveying belt based on the characteristic vector z. The problem of the lower rate of accuracy of discernment of conveyer belt tear or off tracking scene is solved. The method and the device have the effect of improving the identification accuracy.

Description

A conveyor belt tearing and deviation detection method, device, equipment and storage medium

technical field

The present application relates to the technical field of industrial automation, and in particular, to a method, device, device and storage medium for detecting tearing and deviation of a conveyor belt.

Background technique

At present, the conveyor belt is an important tool for the power plant to carry thermal coal, and it is a necessary condition for the safe and reliable operation of the power plant. However, in the long-term coal transportation process, the conveyor belt will be torn, deviate and other problems due to alternating load changes and fatigue damage. In severe cases, it will affect the normal operation of the factory and even threaten workers. work safety.

Specifically, the tearing of the conveyor belt refers to the tearing of the conveyor belt caused by friction damage, metal jamming, external pressure, high-speed operation, etc. during the process of transporting coal, which will lead to thermal coal leakage and continuous expansion of cracks. In severe cases, it will cause major accidents such as machine failure, production stagnation, casualties, etc., which will pose a major threat to the safe operation of the coal mine transportation system. Existing conveyor belt tear detection methods mainly include manual detection, sensor detection and tear detection methods based on image processing, but manual detection methods cannot detect tears in time and stop transportation; sensor detection is easily damaged, resulting in detection failure, and accurate detection. The tearing detection method based on image processing can only detect tearing in a single scene, and it also lacks early prediction of tearing trend, and its versatility and reliability are low.

The deviation of the conveyor belt means that the distance between the actual center line and the theoretical center line of the conveyor belt is too large, which will lead to the accumulation of thermal coal at the end, resulting in side leakage, causing environmental pollution, increasing the cleaning workload, affecting production efficiency and conveyor belt usage. life, and it will lead to safety production accidents. Existing conveyor belt deviation detection methods mainly include manual detection, photoelectric detection and visual image detection methods, but the manual detection method is inefficient, cannot detect deviation in time and stop work for maintenance; photoelectric detection equipment is expensive and difficult to popularize; visual image detection Detection methods rely on mathematical models and are greatly affected by complex environments, which easily lead to low detection accuracy.

In view of the above-mentioned related technologies, the inventor believes that there is a defect that the recognition accuracy of the existing conveyor belt tearing or deviation scene is low.

SUMMARY OF THE INVENTION

In order to improve the recognition accuracy of a conveyor belt tearing or deviation scene, the present application provides a conveyor belt tearing and deviation detection method, device, equipment and storage medium.

In a first aspect, the present application provides a method for detecting tearing and deviation of a conveyor belt, which has the characteristics of accurately detecting the tearing and deviation of a conveyor belt.

This application is achieved through the following technical solutions:

A method for detecting tearing and deviation of a conveyor belt, comprising the following steps:

Obtain the video data of the conveyor belt and build an image database;

inputting the data of the image database into a deep neural network model for training, and outputting abnormal image data features;

Based on the combination of hyperparameters with the highest classification accuracy based on the support vector machine model, and inputting the torn image data features and deviated image data features of the marked abnormal image data features into the support vector machine model for training, the hyperparameters are obtained The support vector machine model under the combination;

Make the described deep neural network model of training and described support vector machine model under described hyperparameter combination to detect the video data of conveyor belt;

When the preset conditions are met, the deep neural network model outputs the feature vector z, and the video data of the current conveyor belt is judged to be abnormal;

The support vector machine model outputs abnormal results of conveyor belt tearing or conveyor belt deviation based on the feature vector z.

In a preferred example of the present application, it may be further configured that: the preset condition includes that the distance r between the feature vector z output by the deep neural network model and the preset center vector c is greater than the preset threshold R.

In a preferred example, the present application can be further configured as: the step of inputting the data of the image database into the deep neural network model for training includes:

A preset contrast loss function, the contrast loss function includes,

Among them, I ₁ represents the normal image data set, I ₂ represents the abnormal image data set, i represents an image in the normal image data set I ₁ or the abnormal image data set I ₂ , Z _i represents the feature vector of the image i, and I ₁ \i represents all image data sets other than a certain image i in the normal image data set I ₁ , j represents an image other than a certain image i in the normal image data set I ₁ or abnormal image data set I ₂ , Z _j Represents the feature vector of image j, I ₂ \i represents all other image data sets except a certain image i in the abnormal image data set I ₂ , t is one of the normal image data set I ₁ or the abnormal image data set I ₂ image, Z _t is the feature vector of image t; the deep neural network model is pre-trained using the contrast loss function.

In a preferred example, the present application can be further configured as: after pre-training the deep neural network model, the following steps are also included:

A preset training loss function, the training loss function includes,

Among them, _zi is the output vector of the input image i on the deep neural network model, and c is the mean value of the output vector obtained by pre-training;

The pretrained deep neural network model is optimized using the training loss function.

In a preferred example, the present application can be further configured as follows: the step of obtaining the hyperparameter combination of the support vector machine model with the highest classification accuracy includes:

Preset several groups of support vector machine models including hyperparameter combinations of kernel functions, penalty parameters, and relaxation vectors, and train the support vector machine models based on the tearing image data features and the deviating image data features, and obtain the The classification accuracy corresponding to the group hyperparameter combination;

Construct the response relationship between hyperparameter combinations and classification accuracy;

Combined with the gain expectation, the hyperparameter combination with the highest classification accuracy is obtained.

In a preferred example, the present application can be further configured as: before the data of the image database is input into the deep neural network model for training, the following steps are further included:

performing data enhancement processing on the tear image data and the deviated image data of the abnormal category image data of the divided image database;

The normal category image data of the image database and the torn image data and the deviated image data subjected to data enhancement processing are input into the deep neural network model.

In a preferred example, the present application may be further configured as: the steps of inputting the normal category image data of the image database, the tearing image data and the deviated image data after data enhancement processing into the deep neural network model Before, it also includes the following steps:

The tearing image data, the deviated image data, and the normal category image data that have undergone data enhancement processing are normalized, and then input into the deep neural network model.

In a second aspect, the present application provides a conveyor belt tearing and deviation detection device, which has the characteristics of accurately detecting the tearing and deviation of the conveyor belt.

This application is achieved through the following technical solutions:

A conveyor belt tearing and deviation detection device, comprising:

The data acquisition module is used to acquire the video data of the conveyor belt and construct an image database;

The first training module is used to input the data of the image database into a deep neural network model for training, and output abnormal image data features;

The second training module is used to combine the hyperparameters with the highest classification accuracy based on the support vector machine model, and input the torn image data features and deviated image data features of the marked abnormal image data features into the support vector machine model Perform training to obtain the support vector machine model under the hyperparameter combination;

A detection module, configured to enable the trained deep neural network model and the support vector machine model under the hyperparameter combination to detect the video data of the conveyor belt;

an abnormality perception module, used to make the deep neural network model output a feature vector z when a preset condition is met, and judge the current video data of the conveyor belt as abnormal;

The abnormal category early warning module is configured to enable the support vector machine model to output abnormal results of conveyor belt tearing or conveyor belt deviation based on the feature vector z.

In a third aspect, the present application provides a device with the characteristics of accurately detecting the tearing and deviation of the conveyor belt.

This application is achieved through the following technical solutions:

A device comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implements the above-mentioned tearing and deviation of the conveyor belt when executing the computer program The steps of the detection method.

In a fourth aspect, the present application provides a storage medium, which has the characteristics of accurately detecting tearing and deviation of a conveyor belt.

This application is achieved through the following technical solutions:

A storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the steps of the above-mentioned method for detecting the tearing and deviation of a conveyor belt.

The present application includes at least one of the following beneficial technical effects:

1. A conveyor belt tearing and deviation detection method. The visual detection method based on neural network detects the fault of the conveyor belt, trains the deep neural network model to extract the image features of the conveyor belt, and classifies the normal and abnormal conditions of the conveyor belt. , when it is judged to be abnormal, the abnormal image data features are output; then based on the abnormal image data features, the tearing image data features and the deviated image data features are marked, and the hyperparameter combination of the support vector machine model that maximizes the classification accuracy is obtained. , train the support vector machine model to further subdivide the tearing and deviation of abnormal images of the conveyor belt; improve the recognition accuracy of the existing conveyor belt tearing or deviation scenes, and improve the The recognition accuracy of the conveyor belt tearing or deviation scene overcomes the defects of timeliness, efficiency, accuracy, reliability, and versatility of traditional detection methods, and can realize efficient and accurate conveyor belt detection, and timely carry out equipment inspection. Shutdown for maintenance is of great significance to ensuring the safe and reliable operation of energy plants;

2. Use the contrast loss function to pre-train the deep neural network model to improve the ability of the deep neural network model to extract image features, effectively filter the image background, noise and other interfering features, and improve the training efficiency of the model;

3. Make the training loss function optimize the deep neural network model pre-trained by the contrast loss function, so that the output features of the normal image of the conveyor belt are close to the center c, and the output features of the abnormal images of the conveyor belt are far away from the center c, so as to improve the accuracy of the conveyor belt. Accurate classification of normal and abnormal images;

4. Obtain the hyperparameter combination of the support vector machine model to train the SVM model with the highest classification accuracy, and use the SVM model under the optimal hyperparameter combination to further classify the abnormal image data feature categories of the conveyor belt, effectively avoiding the need for Due to the serious over-fitting problem caused by less abnormal data, the generalization performance of the model is improved, that is, the bias problem caused by using the deep neural network model for classification is avoided, and the judgment accuracy of abnormal image detection of the conveyor belt is improved. The dimension of the input data can be improved, the detection efficiency of the category image with a large proportion of the normal image data of the conveyor belt can be improved, and the response speed of the SVM model can be improved;

5. Perform data enhancement processing on the divided tear image data and deviation image data and then input them into the deep neural network model to increase the abnormal image samples of the conveyor belt in the deviation and tear state, and reduce the image of the conveyor belt. Unbalanced sample data is beneficial to improve the recognition accuracy of conveyor belt tearing and deviation scenarios;

6. Normalize the tearing image data, deviated image data, and normal category image data after data enhancement processing, and then input it into the deep neural network model to reduce the amount of data calculation in the training of the deep neural network model. , which is convenient for subsequent deep neural network model training.

Description of drawings

FIG. 1 is a model training flow chart of a method for detecting tearing and deviation of a conveyor belt according to an embodiment of the present application.

Figure 2 is a schematic diagram of the training of the deep neural network model and the support vector machine model.

FIG. 3 is a flowchart of a method for detecting tearing and deviation of a conveyor belt according to an embodiment of the present application.

FIG. 4 is a structural block diagram of a conveyor belt tearing and deviation detection device according to an embodiment of the present application.

Detailed ways

This specific embodiment is only an explanation of the application, and it does not limit the application. Those skilled in the art can make modifications to the embodiment without creative contribution as needed after reading this specification, but as long as the rights of the application are All claims are protected by patent law.

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

In addition, the term "and/or" in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, There are three cases of B alone. In addition, the character "/" in this text, unless otherwise specified, generally indicates that the related objects before and after are an "or" relationship.

The embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Referring to FIG. 1 , an embodiment of the present application provides a method for detecting tearing and deviation of a conveyor belt. The main steps of the method are described as follows.

Obtain the video data of the conveyor belt and build an image database;

The data of the image database is input into the deep neural network model for training, and the abnormal image data features are output;

Based on the support vector machine model, the hyperparameter combination with the highest classification accuracy, and the tearing image data features and deviated image data features of the marked abnormal image data features are input into the support vector machine model for training, and the hyperparameter combination is obtained. The support vector machine model of ;

Use the trained deep neural network model and the support vector machine model under the combination of hyperparameters to detect the video data of the conveyor belt;

When the preset conditions are met, the deep neural network model outputs the feature vector z, and judges the current video data of the conveyor belt as abnormal;

Based on the feature vector z, the SVM model outputs abnormal results of belt tearing or belt misalignment.

Specifically, the video data of the thermal coal conveyor belt of the power plant is collected, and an image is taken every several frames to construct an image database. In this embodiment, an image is taken every 5 frames in the conveyor belt video, and is aggregated to form an image data set.

Then, the images in the image data set are marked as normal and abnormal categories, and normal category image data and abnormal category image data are obtained; among them, the category image data is further subdivided and marked as two categories: tearing image data and deviating image data. , build the image database.

Further, by analyzing the image information in the image database, it is found that there are many image samples in the normal state of the conveyor belt, but very few abnormal samples in the deviation and tear state, and the problem of image sample data imbalance occurs. It affects the recognition accuracy of conveyor belt tearing and deviation scenarios.

Therefore, data enhancement processing is performed on the tearing image data and the deviating image data to expand the training data set of abnormal images and improve the accuracy of the model. In this embodiment, enhancement processing methods such as rotation, scaling, color space adjustment, mosaic enhancement, etc. can be used to enhance the tearing image data and the deviation image data, so as to increase the power plant thermal coal conveyor belt in the deviation and tearing state. Abnormal samples to reduce the imbalance of the image sample data of the conveyor belt, which is beneficial to improve the recognition accuracy of the tearing and deviation of the conveyor belt.

Further, after normalizing the tearing image data and deviating image data after data enhancement processing, as well as the normal category image data of the image database, they are input into the deep neural network model for training. In this embodiment, the maximum and minimum normalization method can be adopted, and the specific formula is as follows:

Wherein, x _i represents the original pixel point of the image to be processed, min(x) represents the minimum value of the image pixel, and max(x) represents the maximum value of the image pixel.

The normalization process will not change the information storage of the image itself, but the value range of the normalized image pixel value is converted from 0 to 255 to 0 to 1, so as to reduce the amount of data calculation during the training of the deep neural network model. It is convenient for subsequent deep neural network model training.

In this embodiment, the deep neural network model adopts the ResNet18-SVDD deep neural network model to extract the image data features of the conveyor belt.

Specifically, referring to Fig. 2, a ResNet18 deep neural network model is constructed. The main part of the model adopts the ResNet18 structure, followed by several fully connected layers FC. In this embodiment, there may be three fully connected layers FC.

Then, a preset contrastive loss function is used to pre-train the deep neural network model using the contrastive learning method.

Among them, the contrastive loss function can include,

I₁表示正常图像数据集合，

I₂表示异常图像数据集合，i表示正常图像数据集合I₁或异常图像数据集合I₂中的某一个图像，Z_i表示图像i的特征向量，I₁\i表示正常图像数据集合I₁中的某一图像i以外的其他所有图像数据集合，j表示正常图像数据集合I₁或异常图像数据集合I₂中某一图像i以外的一个图像，Z_j表示图像j的特征向量，I₂\i表示异常图像数据集合I₂中的某一图像i以外的其他所有图像数据集合，t为正常图像数据集合I₁或异常图像数据集合I₂中的一个图像，Z_t为图像t的特征向量。in,

I ₁ represents the normal image data set,

I ₂ represents the abnormal image data set, i represents a certain image in the normal image data set I ₁ or the abnormal image data set I ₂ , Z _i represents the feature vector of the image i, I ₁ \i represents the normal image data set I ₁ All other image data sets except a certain image i, j represents an image other than a certain image i in the normal image data set I ₁ or abnormal image data set I ₂ , Z _j represents the feature vector of image j, I ₂ \ i represents all image data sets other than a certain image i in the abnormal image data set I ₂ , t is an image in the normal image data set I ₁ or the abnormal image data set I ₂ , Z _t is the feature vector of the image t .

Based on the normalized image data, the contrast loss function is used to pre-train the deep neural network model to improve the ability of the deep neural network model to extract image features, effectively filter the image background, noise and other interfering features, and can Improve the training efficiency of the model. At this point, the output vector _zi is obtained from the output of the deep neural network model.

Further, the preset training loss function, the training loss function may include,

Among them, I ₁ represents the normal image data set, I ₂ represents the abnormal image data set, z _i is the output vector of the input image i on the deep neural network model, c is the mean value of the output vector obtained through comparative learning, and R is the preset training set.

Based on the normal image data set I ₁ , the abnormal image data set I ₂ and the output vector zi of the input image _i on the deep neural network model, the training loss function is optimized to the deep neural network model pre-trained by the contrast loss function, and the obtained The optimized ResNet18-SVDD deep neural network model.

The optimized ResNet18-SVDD deep neural network model is used because of its Support Vector Data Description (SVDD), that is, through the training loss function, a hypersphere with a minimum radius is found so that the interior is a positive sample and the exterior is a negative sample , then the sample just located on the hypersphere is to construct the support vector of the hypersphere, so that the output feature of the normal image of the conveyor belt is close to the center c, and the output feature of the abnormal image of the conveyor belt is far away from the center c, so as to make the normal image of the conveyor belt close to the center c. Images and abnormal images are accurately classified.

In addition, due to the small amount of data of abnormal images of the conveyor belt, even if the optimized ResNet18-SVDD deep neural network model is used to classify the video data of the conveyor belt, it is still prone to overfitting, which affects the tearing or deviation of the conveyor belt. The recognition accuracy of the scene, and the support vector machine model (support vector machines, SVM) is more excellent in the classification of a small amount of data. The support vector machine is a two-class model, and its basic model is defined in the feature space. A linear classifier with the largest margin. The learning strategy of SVM is interval maximization, which can be formalized as a problem of solving convex quadratic programming, which is also equivalent to the minimization problem of regularized hinge loss function. The learning algorithm of SVM is the optimization algorithm for solving convex quadratic programming.

Therefore, in this scheme, the abnormal image data features output by the deep neural network model are input into the support vector machine model for training, so as to further subdivide the categories of abnormal images of the conveyor belt.

Referring to FIG. 1 and FIG. 2, specifically, obtain all abnormal image output vectors z ₂ in the output vector _zi output by the optimized ResNet18-SVDD deep neural network model, as the abnormal image output feature data set, and the abnormal image The output feature datasets are labeled as tearing image data features and deviating image data features.

Normalize the torn image data features and the deviated image data features, and divide 80% of the data set into training sets and 20% into test sets to preset training sets and test sets. Among them, the training set is used to train the model; the test set is used to test the model trained on the training set to find the model with the best effect.

The SVM model is trained using the normalized data sets of tearing image data features and deviating image data features, and the optimal hyperparameter combination of SVM is obtained through the Kiring response surface method, so that the classification accuracy is the highest, and then the The SVM model under this set of optimal hyperparameter combinations.

Wherein, the step of obtaining the hyperparameter combination of the SVM model with the highest classification accuracy includes: presetting several sets of hyperparameter combinations of the support vector machine model including the kernel function, the penalty parameter, and the relaxation vector, and based on the tear image data features and Deviate the image data features, train the support vector machine model, and obtain the classification accuracy rate corresponding to each set of hyperparameter combinations; among them, the classification accuracy rate is an indicator for evaluating the performance of the classifier, and the classification accuracy rate refers to the given test data set, The ratio of the number of samples correctly classified by the classifier to the total number of samples, that is, the formula of the classification accuracy P is:

Among them, TP refers to predicting positive class samples as positive class numbers, and FP refers to predicting negative class samples as positive class numbers;

Construct the response relationship between hyperparameter combination and classification accuracy, that is, Kriging response surface method, obtain the function expression of hyperparameter combination and classification accuracy through Kriging interpolation method to construct SVM model;

Combined with the expected improvement (EI), the hyperparameter combination with the highest classification accuracy is searched through the genetic algorithm to obtain the hyperparameter combination with the highest classification accuracy as the optimal hyperparameter combination of the SVM, and the hyperparameter combination with the highest classification accuracy is obtained by training. SVM model under parameter combination.

Furthermore, the SVM model under the optimal hyperparameter combination can further classify the abnormal image data feature categories of the conveyor belt, which effectively avoids the serious over-fitting problem caused by the lack of abnormal data, and improves the generalization performance of the model. The bias problem caused by the use of the deep neural network model for classification is improved, and the judgment accuracy of abnormal image detection of the conveyor belt is improved; at the same time, the dimension of the input data is reduced, which can improve the accuracy of the category images with a large proportion of the normal image data of the conveyor belt. The detection efficiency improves the response speed of the SVM model.

In this embodiment, the hyperparameter combination can be set randomly.

Referring to FIG. 3 , the video data of the conveyor belt is acquired in real time, an image is extracted every several frames, uploaded to the server, and the image data is normalized and input into the trained deep neural network model.

Make the trained deep neural network model perform real-time detection on the input image data.

When the preset conditions are met, the deep neural network model outputs the feature vector z, and judges the current video data of the conveyor belt as abnormal. In this embodiment, the preset condition includes that the distance r between the feature vector z output by the deep neural network model and the preset center vector c is greater than the preset threshold R, where r=||z-c||^2.

In this embodiment, the preset threshold R may be the radius value of the hypersphere obtained by training the ResNet18-SVDD deep neural network model.

When the distance r is greater than the preset threshold R, the deep neural network model outputs the feature vector z, and judges that the video data of the current conveyor belt is abnormal; when the distance r is less than or equal to the preset threshold R, the deep neural network model judges the current The video data of the conveyor belt is normal, and the detection of the next image is continued.

At the same time, the output feature vector z is input into the SVM model under the optimal hyperparameter combination. Based on the feature vector z, the SVM model outputs the abnormal results of conveyor belt tearing or conveyor belt deviation, so as to obtain the type of conveyor belt abnormality and send out Exception type alert.

Further, a conveyor belt tearing and deviation detection method is based on a neural network visual detection method to detect the fault of the conveyor belt, train a deep neural network model to extract the image features of the conveyor belt, and classify the normal and abnormal conditions of the conveyor belt. When it is judged to be abnormal, the abnormal image data features are output; then based on the abnormal image data features, the tearing image data features and the deviation image data features are marked, and the hyperparameter combination of the support vector machine model with the highest classification accuracy is obtained. The support vector machine model is trained to further subdivide the tearing and deviation of abnormal images of the conveyor belt; the recognition accuracy of the existing conveyor belt tearing or deviation scene is improved, and the conveyor belt is improved. The recognition accuracy rate of belt tearing or deviation scene overcomes the defects of timeliness, efficiency, accuracy, reliability and versatility of traditional detection methods; it can realize efficient and accurate conveyor belt detection and stop equipment in time Maintenance is of great significance to ensuring the safe and reliable operation of energy plants.

It should be understood that, the execution order of each process in the foregoing embodiments should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Referring to FIG. 4 , an embodiment of the present application also provides a conveyor belt tear and deviation detection device, which is the same as the conveyor belt tear and deviation detection method in the above-mentioned embodiment. One-to-one correspondence with the system. The conveyor belt tearing and deviation detection device includes:

The data acquisition module is used to acquire the video data of the conveyor belt and construct an image database;

The first training module is used to input the data of the image database into the deep neural network model for training, and output abnormal image data features;

The second training module is used to combine the hyperparameters with the highest classification accuracy based on the support vector machine model, and input the torn image data features and deviated image data features of the marked abnormal image data features into the support vector machine model for training , obtain the support vector machine model under the hyperparameter combination;

The detection module is used to detect the video data of the conveyor belt with the support vector machine model under the combination of the trained deep neural network model and the optimal hyperparameter;

The abnormality perception module is used to make the deep neural network model output the feature vector z when the preset conditions are met, and judge the current video data of the conveyor belt as abnormal;

The abnormal type early warning module is used to make the support vector machine model output abnormal results of conveyor belt tearing or conveyor belt deviation based on the feature vector z, and send out abnormal type reminders.

Further, a conveyor belt tearing and deviation detection device also includes:

The data enhancement processing module is used to perform data enhancement processing on the tear image data and the deviation image data of the abnormal category image data of the divided image database;

The data normalization processing module is used to normalize the torn image data and deviated image data after data enhancement processing, as well as the normal category image data of the image database.

For the specific definition of a conveyor belt tear and deviation detection device, please refer to the above definition of a conveyor belt tear and deviation detection method, which will not be repeated here. Each module in the above-mentioned conveyor belt tearing and deviation detection device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, an apparatus is provided that includes a computer device, which may be a server. The computer device includes a processor, memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The nonvolatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for detecting a conveyor belt tear and deviation.

In one embodiment, there is provided a storage medium comprising a computer-readable storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, The processor performs the following steps when executing a computer program:

Obtain the video data of the conveyor belt and build an image database;

The data of the image database is input into the deep neural network model for training, and the abnormal image data features are output;

Based on the support vector machine model, the hyperparameter combination with the highest classification accuracy, and the tearing image data features and deviated image data features of the marked abnormal image data features are input into the support vector machine model for training, and the hyperparameter combination is obtained. The support vector machine model of ;

Use the trained deep neural network model and the support vector machine model under the combination of hyperparameters to detect the video data of the conveyor belt;

When the preset conditions are met, the deep neural network model outputs the feature vector z, and judges the current video data of the conveyor belt as abnormal;

Based on the feature vector z, the SVM model outputs abnormal results of belt tearing or belt misalignment.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the system into different functional units or modules to complete all or part of the functions described above.

Claims (10)

1. A method for detecting tearing and deviation of a conveying belt is characterized by comprising the following steps:

acquiring video data of a conveyer belt, and constructing an image database;

inputting data of the image database into a deep neural network model for training, and outputting abnormal image data characteristics;

inputting a hyper-parameter combination which enables the classification accuracy to be highest based on a support vector machine model, and enabling the marked tearing image data characteristics and the marked deviation image data characteristics of the abnormal image data characteristics to the support vector machine model for training to obtain the support vector machine model under the hyper-parameter combination;

enabling the trained deep neural network model and the support vector machine model under the hyper-parameter combination to detect video data of a conveying belt;

when a preset condition is met, the deep neural network model outputs a characteristic vector z, and the current video data of the conveying belt is judged to be abnormal;

and outputting an abnormal result of tearing or deviation of the conveying belt by the support vector machine model based on the characteristic vector z.

2. The conveyor belt tearing and deviation detecting method according to claim 1, wherein the preset condition includes that a distance R between a feature vector z output by the deep neural network model and a preset central vector c is greater than a preset threshold R.

3. The conveyor belt tearing and deviation detecting method as claimed in claim 1, wherein: the step of inputting the data of the image database into a deep neural network model for training comprises the following steps:

presetting a contrast loss function, wherein the contrast loss function comprises,

wherein, I₁Representing a normal image data set, I₂Representing an abnormal image data set, I representing a normal image data set I₁Or abnormal image data set I₂One image of Z_iFeature vector representing image I, I₁\ I denotes a normal image data set I₁J represents the normal image data set I₁Or abnormal image data set I₂One image other than the certain image i, Z_jFeature vector, I, representing image j₂\ I represents an abnormal image data set I₂T is a normal image data set I₁Or abnormal image data set I₂An image of Z_tIs the feature vector of the image t;

and pre-training a deep neural network model by using the contrast loss function.

4. The conveyor belt tearing and deviation detecting method as claimed in claim 3, wherein: after the deep neural network model is pre-trained, the method further comprises the following steps:

presetting a training loss function, the training loss function comprising,

wherein z is_iAn output vector of an input image i on the deep neural network model is obtained, and c is an output vector mean value obtained by pre-training;

and optimizing the pre-trained deep neural network model by using the training loss function.

5. The conveyor belt tearing and deviation detecting method according to claim 1, wherein the step of obtaining the hyper-parameter combination of the support vector machine model which enables the highest classification accuracy rate comprises:

presetting hyper-parameter combinations including kernel functions, punishment parameters and relaxation vectors of a plurality of groups of support vector machine models, training the support vector machine models based on the tearing image data characteristics and the deviation image data characteristics, and obtaining classification accuracy corresponding to each group of hyper-parameter combinations;

constructing a response relation between the hyper-parameter combination and the classification accuracy;

and acquiring the hyper-parameter combination with the highest classification accuracy by combining the gain expectation.

6. The conveyor belt tearing and deviation detecting method according to any one of claims 1-5, wherein before inputting the data of the image database into a deep neural network model for training, the method further comprises the following steps:

performing data enhancement processing on torn image data and off-tracking image data of the abnormal category image data of the divided image database;

and inputting the normal category image data of the image database, the tearing image data and the deviation image data which are subjected to data enhancement processing into a depth neural network model.

7. The method for detecting the tearing and the deviation of the conveying belt according to claim 6, wherein before the step of inputting the normal category image data of the image database and the torn image data and the deviation image data which are processed by data enhancement into the deep neural network model, the method further comprises the following steps:

and normalizing the torn image data and the off-tracking image data subjected to data enhancement processing and the normal type image data, and inputting the normalized data into a deep neural network model.

8. The utility model provides a conveyer belt is torn and off tracking detection device which characterized in that includes:

the data acquisition module is used for acquiring video data of the conveyer belt and constructing an image database;

the first training module is used for inputting the data of the image database into a deep neural network model for training and outputting abnormal image data characteristics;

the second training module is used for enabling a hyper-parameter combination with the highest classification accuracy to be achieved based on a support vector machine model, enabling the marked tearing image data features and the marked deviation image data features of the abnormal image data features to be input into the support vector machine model for training, and obtaining the support vector machine model under the hyper-parameter combination;

the detection module is used for enabling the trained deep neural network model and the support vector machine model under the hyper-parameter combination to detect video data of a conveying belt;

the anomaly sensing module is used for enabling the deep neural network model to output a characteristic vector z when a preset condition is met, and judging the current video data of the conveying belt to be abnormal;

and the abnormal category early warning module is used for enabling the support vector machine model to output an abnormal result of tearing or deviation of the conveying belt based on the characteristic vector z.

9. An apparatus comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to perform the steps of the method of any one of claims 1 to 7.

10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

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