CN114049301A - Material yard belt blocking detection system based on target detection - Google Patents
Material yard belt blocking detection system based on target detection Download PDFInfo
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Abstract
A stock ground belt blockage detection system based on target detection comprises: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device; the belt blockage image acquisition device is used for collecting belt blockage video images to form historical data of the belt blockage video images; the system is also used for acquiring a current frame image in a belt running video in real time and inputting the current frame image into a target detection network model; the target detection network model is used for inputting an image data set of belt blockage into a target detection network for training to obtain a target detection network based on a neural network as a detector of the belt blockage, and detecting a current frame image in a real-time collected belt running video; and the alarm device acquires the detection result of the target detection network model on the current frame image in the real-time acquisition belt running video and informs the related responsible person in an alarm mode. Compared with the prior art, the invention realizes the automatic and unmanned functions of the real-time detection of the belt transportation state.
Description
Technical Field
The invention relates to the field of target detection, in particular to a system for detecting belt blockage of a stock yard based on target detection.
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 conveyor has the main task of smoothly conveying bulk materials to a destination through a belt conveyor conveying system, and if the belt conveyor system is blocked, the production process is interrupted due to shutdown and production stoppage. If the transported materials encounter obstacles or contact with the surface of a belt which is dimpled due to erosion, the material flow changes the normal transportation direction, so that the transported materials are accumulated, and the material flow is blocked. Therefore, once the material blockage occurs, the maintenance recovery time needs several hours, and the normal operation and production cannot be realized, so that the production and economic losses are caused.
The phenomenon can be reduced by installing related detection equipment near a production line, but a video detection system can generate a large number of video images every day, if the blocking 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.
Disclosure of Invention
In view of the above, the present invention has been made in order to provide a stock yard belt blockage detection system based on object detection 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 blockage detection system based on target detection comprises: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device; wherein:
the belt blockage image acquisition device is used for collecting belt blockage video images to form historical data of the belt blockage video images; the system is also used for acquiring a current frame image in the belt running video in real time and inputting the current frame image in the belt running video acquired in real time into the target detection network model;
the target detection network model is used for inputting an image data set of belt blockage into a target detection network for training to obtain a target detection network based on a neural network as a detector of the belt blockage, detecting a current frame image in a real-time collected belt running video, and achieving the purpose of detecting the belt material flow transportation state according to a detection result;
and the alarm device acquires the detection result of the target detection network model on the current frame image in the real-time acquisition belt running video, and if the frame image is detected as a material blocking frame by the target detection model based on the belt material blocking state, the alarm device informs related responsible persons in real time in an alarm mode, so that the timely processing of material blocking is ensured.
Further, the belt blockage image acquisition device can be a camera, and the belt blockage image acquisition device collects belt blockage video images, and the specific method for forming the historical data of the belt blockage video images is as follows: the belt material blockage image acquisition device is used for positioning and calibrating the belt material blockage image and the material blockage area; positioning and calibrating each frame of image meeting the material blockage, attaching a label containing coordinate position information of the material blockage area to the image, and dividing the marked data into two parts, wherein one part is a training set and is used for training a target detection network model; and the other part is a test set used for testing the detection effect of the target detection network model.
Further, the training method of the target detection network model comprises the following steps: s101, taking a training set in an image data set as input of a target detection network model, setting target detection training parameters, and obtaining initial target detection based on material blockage state detection; s102, inputting a test set in the image data set into a trained initial target detection network model, identifying images in the test set through the initial target detection network model to obtain whether a label of a material blockage exists in a current image and a corresponding material blockage area, counting and matching the current image in the test set with the real state of the current image, and determining that the current target detection network model is the target detection network model for belt material blockage detection when the matching accuracy is greater than a preset threshold value.
Further, the training method of the target detection network model further comprises the following steps: and when the matching accuracy is smaller than the preset threshold, the current initial target detection network model cannot meet the requirement, and the steps S101-S102 are executed again until the matching accuracy of the test set reaches the preset threshold.
Furthermore, the target detection network model adopts a plurality of convolutional neural networks to extract basic image features, and shortcut links are arranged between preset layers of the convolutional neural networks.
Further, the target detection network model takes 256 × 3 as input, and takes 256 residual components, each of which has two convolutional layers and one shortcut link.
Further, the target detection network model sets 3 prior frames for each downsampling scale, and clusters 9 prior frames in total.
Further, the target detection network model predicts the object class by using the output of the logistic.
Further, the alarm device can be a buzzer, when the target detection network model detects that the current frame image in the real-time collection belt running video is a material blocking frame, the relevant responsible person is informed in real time through an alarm mode through the buzzer, and the timely processing of material blocking is guaranteed.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention discloses a stock ground belt blocking detection system based on target detection, which comprises: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device. The target detection network model obtains a video picture of belt running in the carrying process; collecting historical data of belt blockage video images, and then positioning and marking belt blockage areas to form an image data set of belt blockage; highly summarizing and extracting characteristics of the image data set by using an algorithm of target detection to obtain a model of target detection containing a belt material blockage area; identifying a current frame image in a belt running video acquired in real time by using the obtained target detection model, and finding out whether the current image has a material blockage phenomenon or not and a material blockage area under the condition of material blockage, so as to achieve the purpose of detecting the material blockage of the belt in real time; the device is used for notifying related responsible persons in real time in an alarm mode through the alarm device, and timely processing of the blocking materials is guaranteed. Compared with the prior art, the method does not need to manually monitor and judge the video in real time, and reduces the manual working time and labor cost; compared with manual detection and judgment, the method has higher judgment accuracy on the belt blockage, 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 structural diagram of a system for detecting a material yard belt blockage based on target detection 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 stockyard belt blockage detection system based on target detection.
Example 1
The embodiment discloses a stock ground belt putty's detecting system based on target detection, as fig. 1, includes: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device; wherein:
the belt blockage image acquisition device is used for collecting belt blockage video images to form historical data of the belt blockage video images; the system is also used for acquiring a current frame image in the belt running video in real time and inputting the current frame image in the belt running video acquired in real time into the target detection network model;
in this embodiment, the belt blockage image acquisition device can acquire belt blockage video image devices for cameras and the like, and the belt blockage image acquisition device collects belt blockage video images, and the specific method for forming the historical data of the belt blockage video images is as follows: the belt material blockage image acquisition device is used for positioning and calibrating the belt material blockage image and the material blockage area; positioning and calibrating each frame of image meeting the material blockage, attaching a label containing coordinate position information of the material blockage area to the image, and dividing the marked data into two parts, wherein one part is a training set and is used for training a target detection network model; and the other part is a test set used for testing the detection effect of the target detection network model.
And the target detection network model is used for inputting the image data set of the belt blockage into a target detection network for training to obtain a target detection network based on a neural network as a detector of the belt blockage, detecting the current frame image in the real-time collected belt running video, and achieving the purpose of detecting the belt material flow transportation state according to the detection result.
In this embodiment, the method for training the target detection network model includes: s101, taking a training set in an image data set as input of a target detection network model, setting target detection training parameters, and obtaining initial target detection based on material blockage state detection; s102, inputting a test set in the image data set into a trained initial target detection network model, identifying images in the test set through the initial target detection network model to obtain whether a label of a material blockage exists in a current image and a corresponding material blockage area, counting and matching the current image in the test set with the real state of the current image, and determining that the current target detection network model is the target detection network model for belt material blockage detection when the matching accuracy is greater than a preset threshold value. Preferably, the training method of the target detection network model further includes: and when the matching accuracy is smaller than the preset threshold, the current initial target detection network model cannot meet the requirement, and the steps S101-S102 are executed again until the matching accuracy of the test set reaches the preset threshold.
In this embodiment, the target detection network model adopts a plurality of convolutional neural networks to extract basic image features, and shortcut links are set between preset layers of the convolutional neural networks. Specifically, in the aspect of basic image feature extraction, 53 convolutional neural networks are adopted, which use the method of residual network for reference, and shortcut links (shortcut connections) are set between some layers.
In this embodiment, the target detection network model takes 256 × 3 as input, and takes 256 residual components, each of which has two convolutional layers and one shortcut link.
In this embodiment, the target detection network model sets 3 prior frames for each downsampling scale, and clusters 9 prior frames in total.
In the present embodiment, the target detection network model performs prediction using the output of logistic when predicting the object class. Prediction is performed through the output of logistic. This enables multi-tagged objects to be supported.
And the alarm device acquires the detection result of the target detection network model on the current frame image in the real-time acquisition belt running video, and if the frame image is detected as a material blocking frame by the target detection model based on the belt material blocking state, the alarm device informs related responsible persons in real time in an alarm mode, so that the timely processing of material blocking is ensured.
In this embodiment, alarm device can be for alarm device that the staff can be informed such as bee calling organ, when target detection network model detected that the current frame image in the real-time collection belt operation video is the putty frame, inform relevant person of responsible through the alarm mode in real time through bee calling organ, guarantee the timely processing of putty.
The embodiment discloses a stock ground belt putty's detecting system based on target detection includes: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device. The target detection network model obtains a video picture of belt running in the carrying process; collecting historical data of belt blockage video images, and then positioning and marking belt blockage areas to form an image data set of belt blockage; highly summarizing and extracting characteristics of the image data set by using an algorithm of target detection to obtain a model of target detection containing a belt material blockage area; identifying a current frame image in a belt running video acquired in real time by using the obtained target detection model, and finding out whether the current image has a material blockage phenomenon or not and a material blockage area under the condition of material blockage, so as to achieve the purpose of detecting the material blockage of the belt in real time; the device is used for notifying related responsible persons in real time in an alarm mode through the alarm device, and timely processing of the blocking materials is guaranteed. Compared with the prior art, the method does not need to manually monitor and judge the video in real time, and reduces the manual working time and labor cost; compared with manual detection and judgment, the method has higher judgment accuracy on the belt blockage, 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 (9)
1. The utility model provides a detecting system of stock ground belt putty based on target detection which characterized in that includes: the device comprises a belt blockage image acquisition device, a target detection network model and an alarm device; wherein:
the belt blockage image acquisition device is used for collecting belt blockage video images to form historical data of the belt blockage video images; the system is also used for acquiring a current frame image in the belt running video in real time and inputting the current frame image in the belt running video acquired in real time into the target detection network model;
the target detection network model is used for inputting an image data set of belt blockage into a target detection network for training to obtain a target detection network based on a convolutional neural network as a detector of the belt blockage, detecting a current frame image in a real-time collected belt running video, and achieving the purpose of detecting the belt material flow transportation state according to a detection result;
and the alarm device acquires the detection result of the target detection network model on the current frame image in the real-time acquisition belt running video, and if the frame image is detected as a material blocking frame by the target detection model based on the belt material blocking state, the alarm device informs related responsible persons in real time in an alarm mode, so that the timely processing of material blocking is ensured.
2. The system for detecting the belt blockage of the stock yard based on the target detection as claimed in claim 1, wherein the belt blockage image acquiring device can be a camera, the belt blockage video image acquiring device collects the belt blockage video image, and the specific method for forming the historical data of the belt blockage video image is as follows: the belt material blockage image acquisition device is used for positioning and calibrating the belt material blockage image and the material blockage area; positioning and calibrating each frame of image meeting the material blockage, attaching a label containing coordinate position information of the material blockage area to the image, and dividing the marked data into two parts, wherein one part is a training set and is used for training a target detection network model; and the other part is a test set used for testing the detection effect of the target detection network model.
3. The system for detecting stockyard belt blockage based on target detection as claimed in claim 2, wherein the training method of the target detection network model comprises the following steps: s101, taking a training set in an image data set as input of a target detection network model, setting target detection training parameters, and obtaining initial target detection based on material blockage state detection; s102, inputting a test set in the image data set into a trained initial target detection network model, identifying images in the test set through the initial target detection network model to obtain whether a label of a material blockage exists in a current image and a corresponding material blockage area, counting and matching the current image in the test set with the real state of the current image, and determining that the current target detection network model is the target detection network model for belt material blockage detection when the matching accuracy is greater than a preset threshold value.
4. The system for detecting stockyard belt blockage based on target detection as claimed in claim 2, wherein the training method of the target detection network model further comprises: and when the matching accuracy is smaller than the preset threshold, the current initial target detection network model cannot meet the requirement, and the steps S101-S102 are executed again until the matching accuracy of the test set reaches the preset threshold.
5. The method for detecting the stockyard belt blockage based on the target detection as claimed in claim 1, wherein the target detection network model adopts a plurality of convolutional neural networks to extract basic image characteristics, and shortcut links are arranged between preset layers of the convolutional neural networks.
6. The method as claimed in claim 1, wherein the target detection network model uses 256 × 3 as input, and 256 residual components, each having two convolutional layers and one shortcut link.
7. The method as claimed in claim 1, wherein the target detection network model sets 3 prior frames for each downsampling scale, and clusters 9 prior frames in total.
8. The method for detecting the stockyard belt blockage based on the target detection as claimed in claim 1, wherein the target detection network model predicts the object class by using the output of logistic.
9. The method as claimed in claim 1, wherein the alarm device is a buzzer, and when the target detection network model detects that the current frame image in the real-time collection belt operation video is a material blockage frame, the buzzer notifies the relevant responsible person in real time in an alarm manner, so as to ensure timely processing of material blockage.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116620877A (en) * | 2023-07-21 | 2023-08-22 | 成都考拉悠然科技有限公司 | Method and system for detecting and processing blanking cabinet blocking materials based on deep learning |
| CN117186956A (en) * | 2023-10-16 | 2023-12-08 | 国能长源荆门发电有限公司 | Visual automatic anti-blocking method and system for biological gasification furnace |
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2021
- 2021-10-11 CN CN202111182066.3A patent/CN114049301A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116620877A (en) * | 2023-07-21 | 2023-08-22 | 成都考拉悠然科技有限公司 | Method and system for detecting and processing blanking cabinet blocking materials based on deep learning |
| CN117186956A (en) * | 2023-10-16 | 2023-12-08 | 国能长源荆门发电有限公司 | Visual automatic anti-blocking method and system for biological gasification furnace |
| CN117186956B (en) * | 2023-10-16 | 2024-04-05 | 国能长源荆门发电有限公司 | Visual automatic anti-blocking method and system for biological gasification furnace |
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