CN113989546A - Material yard belt transportation monitoring method based on neural network - Google Patents
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Abstract
A stock ground belt transportation monitoring method based on a neural network comprises the steps of obtaining a video picture of material flow on a belt conveyor in a carrying process; collecting historical data of the material flow video images, and then classifying and labeling to form an image data set of the material flow transportation state; highly summarizing and extracting features of the image data set by using a neural network algorithm to obtain a convolutional neural network model of the belt material flow transportation state; and classifying and identifying the current frame image in the belt logistics video acquired in real time by using the obtained convolutional neural network model to achieve the purpose of monitoring the logistics transportation state. Compared with the prior art, the invention does not need to manually monitor the video in real time, thereby reducing the working time and labor cost of manpower; compared with manual monitoring, the belt logistics transportation state monitoring system has higher monitoring accuracy rate for the transportation state of the belt logistics, and realizes the automatic and unmanned functions of real-time detection of the belt transportation state.
Description
Technical Field
The invention relates to the field of stock ground belt transportation, in particular to a stock ground belt transportation monitoring method based on a neural network.
Background
The belt conveyor is the most economical, most efficient and most convenient method for industrial and mining enterprises to convey raw materials, and the belt conveyor is also a commonly used power conveyor, is widely applied, and is high in quality and low in price. The belt conveyer can convey various material flows with different shapes, different granularity and light weight and the like. The belt conveyor mainly aims to smoothly convey bulk material flow to a destination through a belt conveyor conveying system, and if the belt conveyor system is blocked and has no material, the production process flow is interrupted, the flow directions above and below the belt conveyor system are not clear, the production and logistics scheduling is influenced, and the double losses of production and economy are caused.
The phenomenon can be reduced by installing related monitoring equipment near a production line, but a video monitoring system can generate a large number of video images every day, if the transportation state is completely identified by manpower, the workload is very huge, the efficiency is very low, on the other hand, long-time operation can generate fatigue, and further the phenomena of missed judgment and wrong judgment are caused, the operation quality is difficult to guarantee, and potential safety hazards exist. Therefore, the deep learning technology with high recognition rate is combined in the work task of classifying the belt transportation state of the stock ground, self-learning is carried out through a computer, the material flow state is automatically recognized and classified, most of manual workload is saved and replaced, and the work quality and the inspection efficiency are improved.
Disclosure of Invention
In view of the above, the present invention has been made to provide a neural network-based stock ground belt transport monitoring method that overcomes or at least partially solves the above-mentioned problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a stock ground belt transportation monitoring method based on a neural network comprises the following steps:
s100, collecting historical data of a belt material flow monitoring video image, classifying the image according to the existence state of the material flow, marking the type of the image, and making a material flow state image data set from the marked image;
s200, constructing a convolutional neural network model as an image classifier of a material flow state, and training the convolutional neural network model by using a material flow state image data set to obtain the convolutional neural network model based on the material flow state in belt transportation;
s300, extracting the current frame image in the belt material flow video collected in real time, classifying the current frame image by using the trained convolutional neural network model of the material flow state, and achieving the purpose of monitoring the belt material flow transportation state according to the classification result.
Further, the specific method of S100 includes: classifying and marking the belt material flow state, and classifying images, including a material existence condition and a material nonexistence condition; labeling each frame of image, labeling the image with a class label, and dividing the labeled data into two parts, wherein one part is a training set and is used for training a convolutional neural network model; the other part is a test set used for testing the classification effect of the convolutional neural network model.
Further, a stock ground belt transportation monitoring method based on a neural network as claimed in claim 2, wherein S200 comprises:
s201, taking a training set in an image data set as an input of a convolutional neural network model, setting training parameters of the convolutional neural network, and obtaining an initial convolutional neural network based on material flow state monitoring;
s202, inputting a test set in the image data set into a trained initial convolutional neural network model, identifying images in the test set through the initial neural network model to obtain image classifications corresponding to the current images, matching the image classifications with actual image classifications of the current images in the test set, and determining that the current convolutional neural network model is a convolutional neural network model for material flow state monitoring when matching accuracy is higher than a preset threshold value.
Further, S202 further includes: and when the matching accuracy is smaller than the preset threshold, if the current initial neural network model cannot meet the requirement, re-executing the steps S201-S202 until the matching accuracy of the test set reaches the preset threshold.
Further, the convolutional neural network model is composed of 5 modules, each module is formed by connecting a plurality of convolutional networks of 3 × 3 in series, the convolutional neural network comprises 13 convolutional layers, and 64, 128, 256, 512 and 512 convolutional kernels of 3 × 3 size are respectively used.
Further, in the convolutional neural network, the activation function of the convolutional layer adopts a modified linear unit, normalization is performed after the activation function of the modified linear unit, and the output of the normalization is normalized to a standard normal distribution with a mean value of 0 and a variance of 1.
Furthermore, the convolutional neural network is followed by a pooling layer after each convolutional layer, dimension reduction is carried out through the pooling layer, and image features are expressed by higher-layer abstractions.
Further, the convolutional neural network is followed by 3 fully-connected layers after the convolutional layer pooling layer. The dimension of the output vector of the last full-connection layer is 2 dimensions, and the dimension refers to the number of two categories of material existence and material nonexistence.
Furthermore, according to the convolutional neural network, a softmax layer is added behind the full connection layer, and the softmax layer is used for calculating confidence coefficient of an output result of the full connection layer by using a softmax function and representing the probability of an output category.
Further, S300 further includes:
classifying the current frame image by using the trained convolutional neural network model of the material flow state, and if the frame image is classified as a material frame by the convolutional neural network model based on the belt material flow state, indicating that the current belt material flow state is material; if the frame belongs to a material-free frame, the current belt is indicated to be free of material, and the material is notified to relevant responsible persons in real time in an alarm mode, so that the timely processing of material charging is ensured.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention discloses a stock ground belt transportation monitoring method based on a neural network, which is used for video monitoring of the running state of material flow of a belt conveyor of a raw material factory, and comprises the steps of obtaining a video picture of the material flow on the belt conveyor in the carrying process; collecting historical data of the material flow video images, and then classifying and labeling to form an image data set of the material flow transportation state; highly summarizing and extracting features of the image data set by using a neural network algorithm to obtain a convolutional neural network model of the belt material flow transportation state; and classifying and identifying the current frame image in the belt logistics video acquired in real time by using the obtained convolutional neural network model to achieve the purpose of monitoring the logistics transportation state. Compared with the prior art, the invention does not need to manually monitor the video in real time, thereby reducing the working time and labor cost of manpower; compared with manual monitoring, the belt logistics transportation state monitoring system has higher monitoring accuracy rate for the transportation state of the belt logistics, and realizes the automatic and unmanned functions of real-time detection of the belt transportation state.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a stock yard belt transportation monitoring method based on a neural network in embodiment 1 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems in the prior art, the embodiment of the invention provides a stock ground belt transportation monitoring method based on a neural network.
Example 1
A stock ground belt transportation monitoring method based on a neural network, as shown in fig. 1, comprising:
s100, collecting historical data of a belt material flow monitoring video image, classifying the image according to the existence state of the material flow, marking the type of the image, and making a material flow state image data set from the marked image;
in this embodiment, the specific method of S100 includes: classifying and marking the belt material flow state, and classifying images, including a material existence condition and a material nonexistence condition; labeling each frame of image, labeling the image with a class label, and dividing the labeled data into two parts, wherein one part is a training set and is used for training a convolutional neural network model; the other part is a test set used for testing the classification effect of the convolutional neural network model.
Specifically, the video image acquisition can be performed on the belt material flow by adopting the material flow video of a specific raw material factory or a belt production line. The material condition at least comprises: trace material on the belt, full material on the belt, stone material flow, powder material flow, tree-shaped material flow and mixed material flow; the material-free condition is a smooth and material-free state in the observation range of human eyes on the belt.
S200, constructing a convolutional neural network model as an image classifier of a material flow state, and training the convolutional neural network model by using a material flow state image data set to obtain the convolutional neural network model based on the material flow state in belt transportation;
in this embodiment, S200 specifically includes:
s201, taking a training set in an image data set as an input of a convolutional neural network model, setting training parameters of the convolutional neural network, and obtaining an initial convolutional neural network based on material flow state monitoring;
s202, inputting a test set in the image data set into a trained initial convolutional neural network model, identifying images in the test set through the initial neural network model to obtain image classifications corresponding to the current images, matching the image classifications with actual image classifications of the current images in the test set, and determining that the current convolutional neural network model is a convolutional neural network model for material flow state monitoring when matching accuracy is higher than a preset threshold value.
In some preferred embodiments, S202 further comprises: and when the matching accuracy is smaller than the preset threshold, if the current initial neural network model cannot meet the requirement, re-executing the steps S201-S202 until the matching accuracy of the test set reaches the preset threshold.
In this embodiment, the convolutional neural network model is composed of 5 modules, each module is formed by connecting a plurality of convolutional networks of 3 × 3 in series, the convolutional neural network includes 13 convolutional layers, and 64, 128, 256, 512 and 512 convolutional kernels of 3 × 3 size are used respectively.
In this embodiment, in the convolutional neural network, the activation function of the convolutional layer adopts a modified linear unit, normalization is performed after the linear unit activation function is modified, and the output of the normalization is normalized to a standard normal distribution with a mean value of 0 and a variance of 1.
In this embodiment, the convolutional neural network is followed by a pooling layer after each convolutional layer, and dimension reduction is performed through the pooling layer, so that image features are represented by higher-level abstractions.
In this embodiment, the convolutional neural network is followed by 3 fully-connected layers after the convolutional layer pooling layer. The dimension of the output vector of the last full-connection layer is 2 dimensions, and the dimension refers to the number of two categories of material existence and material nonexistence.
In this embodiment, in the convolutional neural network, a softmax layer is added after the fully-connected layer, and the softmax layer is to calculate a confidence level for an output result of the fully-connected layer by using a softmax function, and represents a probability of an output category.
S300, extracting the current frame image in the belt material flow video collected in real time, classifying the current frame image by using the trained convolutional neural network model of the material flow state, and achieving the purpose of monitoring the belt material flow transportation state according to the classification result.
In this embodiment, S300 further includes: classifying the current frame image by using the trained convolutional neural network model of the material flow state, and if the frame image is classified as a material frame by the convolutional neural network model based on the belt material flow state, indicating that the current belt material flow state is material; if the frame belongs to a material-free frame, the current belt is indicated to be free of material, and the material is notified to relevant responsible persons in real time in an alarm mode, so that the timely processing of material charging is ensured.
The embodiment discloses a stock ground belt transportation monitoring method based on a neural network, which is used for video monitoring of the running state of material flow of a belt conveyor of a raw material factory, and comprises the steps of obtaining a video picture of the material flow on the belt conveyor in the carrying process; collecting historical data of the material flow video images, and then classifying and labeling to form an image data set of the material flow transportation state; highly summarizing and extracting features of the image data set by using a neural network algorithm to obtain a convolutional neural network model of the belt material flow transportation state; and classifying and identifying the current frame image in the belt logistics video acquired in real time by using the obtained convolutional neural network model to achieve the purpose of monitoring the logistics transportation state. Compared with the prior art, the invention does not need to manually monitor the video in real time, thereby reducing the working time and labor cost of manpower; compared with manual monitoring, the belt logistics transportation state monitoring system has higher monitoring accuracy rate for the transportation state of the belt logistics, and realizes the automatic and unmanned functions of real-time detection of the belt transportation state.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. A stock ground belt transportation monitoring method based on a neural network is characterized by comprising the following steps:
s100, collecting historical data of a belt material flow monitoring video image, classifying the image according to the existence state of the material flow, marking the type of the image, and making a material flow state image data set from the marked image;
s200, constructing a convolutional neural network model as an image classifier of a material flow state, and training the convolutional neural network model by using a material flow state image data set to obtain the convolutional neural network model based on the material flow state in belt transportation;
s300, extracting the current frame image in the belt material flow video collected in real time, classifying the current frame image by using the trained convolutional neural network model of the material flow state, and achieving the purpose of monitoring the belt material flow transportation state according to the classification result.
2. The neural network-based stock ground belt transportation monitoring method according to claim 1, wherein the S100 specific method comprises the following steps: classifying and marking the belt material flow state, and classifying images, including a material existence condition and a material nonexistence condition; labeling each frame of image, labeling the image with a class label, and dividing the labeled data into two parts, wherein one part is a training set and is used for training a convolutional neural network model; the other part is a test set used for testing the classification effect of the convolutional neural network model.
3. The neural network-based stock ground belt transportation monitoring method according to claim 1, wherein S200 comprises:
s201, taking a training set in an image data set as an input of a convolutional neural network model, setting training parameters of the convolutional neural network, and obtaining an initial convolutional neural network based on material flow state monitoring;
s202, inputting a test set in the image data set into a trained initial convolutional neural network model, identifying images in the test set through the initial neural network model to obtain image classifications corresponding to the current images, matching the image classifications with actual image classifications of the current images in the test set, and determining that the current convolutional neural network model is a convolutional neural network model for material flow state monitoring when matching accuracy is higher than a preset threshold value.
4. The neural network-based stock ground belt transportation monitoring method according to claim 3, wherein S202 further comprises: and when the matching accuracy is smaller than the preset threshold, if the current initial neural network model cannot meet the requirement, re-executing the steps S201-S202 until the matching accuracy of the test set reaches the preset threshold.
5. A stock ground belt transport monitoring method based on a neural network as claimed in claim 3, characterized in that the convolutional neural network model is composed of 5 modules, each module is composed of a plurality of convolution networks of 3 x 3 connected in series, the convolutional neural network comprises 13 convolution layers, and 64, 128, 256, 512 and 512 convolution kernels of 3 size are respectively used.
6. The method as claimed in claim 3, wherein the convolutional neural network, activation function of convolutional layer adopts modified linear unit, normalization is performed after the linear unit activation function is modified, and output is normalized to standard normal distribution with mean 0 and variance 1.
7. The method as claimed in claim 3, wherein the convolutional neural network is followed by a pooling layer after each convolutional layer, and the pooling layer is used for dimensionality reduction, and the image features are represented by higher-level abstractions.
8. The stock ground belt transportation monitoring method based on the neural network as claimed in claim 3, wherein the convolutional neural network is connected with 3 fully-connected layers after the convolutional layer pooling layer, wherein the dimension of the output vector of the last fully-connected layer is 2-dimensional, which refers to the number of two categories of material existence and material nonexistence.
9. The method as claimed in claim 3, wherein the convolutional neural network is formed by adding a softmax layer after the full link layer, and the softmax layer is used for calculating confidence of the output result of the full link layer by using a softmax function and representing the probability of the output class.
10. The neural network-based stock ground belt transportation monitoring method according to claim 1, wherein S300 further comprises:
classifying the current frame image by using the trained convolutional neural network model of the material flow state, and if the frame image is classified as a material frame by the convolutional neural network model based on the belt material flow state, indicating that the current belt material flow state is material; if the frame belongs to a material-free frame, the current belt is indicated to be free of material, and the material is notified to relevant responsible persons in real time in an alarm mode, so that the timely processing of material charging is ensured.
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| CN113291703A (en) * | 2020-08-31 | 2021-08-24 | 湖南长天自控工程有限公司 | Discharge opening blockage detection method and device |
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