CN113029046A - Method and device for detecting straightness of scraper conveyor based on video identification - Google Patents
Method and device for detecting straightness of scraper conveyor based on video identification Download PDFInfo
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Abstract
The disclosure relates to a method for detecting straightness of a scraper conveyor based on video identification. The method comprises the following steps: acquiring an image of an area where a scraper conveyor is located; identifying a scraper conveyor and an instance segmentation thereof in the image; extracting the coordinate value of the outer contour of the scraper conveyor in the image; calculating a linear correlation coefficient according to the coordinate value of the outer contour; and if the calculated linear correlation coefficient is smaller than a preset threshold value, judging that the straightness of the scraper conveyor does not meet the requirement. According to the scheme provided by the disclosure, the image data of the scraper conveyor can be utilized, the straightness condition of the scraper conveyor is obtained through image recognition, the problem of detection failure caused by damage in the use process of the sensor is avoided, and meanwhile, the method is simple in required hardware structure and more suitable in price cost.
Description
Technical Field
The disclosure relates to the field of image recognition and coal conveying machinery, in particular to a method and a device for detecting straightness of a scraper conveyor based on video recognition.
Background
According to the regulations of coal mine safety regulations, the coal wall of the coal face, the scraper conveyor and the hydraulic support are required to be kept in a straight line. The scraper conveyor is used as a track of a coal mining machine, and the straight line condition of the scraper conveyor directly influences the straight line condition of a coal wall of a coal mining working face, the service life of the coal mining machine, the coal mining efficiency and the coal cutting quality. Therefore, it is very important to ensure the straight line of the scraper conveyor, and the research on the technology for detecting the straight line of the scraper conveyor is an important measuring tool for ensuring the straight line of the scraper conveyor.
The existing method for detecting the straightness of the scraper conveyor comprises the following steps: one method for determining the straightness of the scraper by using an ultrasonic sensor or a sensing optical fiber has the following disadvantages: the method depends on the laying of a large number of sensors, the environment under the coal mine is severe, the sensors are easy to lose efficacy, and the straightness cannot be measured normally after the sensors lose efficacy. And the other is that the operation track of the coal mining machine is positioned and detected through a positioning sensor, and the straightness of the scraper conveyor is detected according to the relation between the coal mining machine and the scraper conveyor, so that the method is high in cost.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method and an apparatus for detecting the straightness of a scraper conveyor based on video recognition, which can ensure that the scraper conveyor keeps straight, and improve the service life of a coal mining machine, the coal mining efficiency and the coal cutting quality.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for detecting straightness of a scraper conveyor based on video recognition, including:
acquiring an image of an area where a scraper conveyor is located;
identifying a scraper conveyor and an instance segmentation thereof in the image;
extracting the coordinate value of the outer contour of the scraper conveyor in the image;
calculating a linear correlation coefficient according to the coordinate value of the outer contour;
and if the calculated linear correlation coefficient is smaller than a preset threshold value, judging that the straightness of the scraper conveyor does not meet the requirement.
Further, the method further comprises:
fitting the coordinates of each position of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
and if the calculated linear correlation coefficient is larger than or equal to the preset threshold, judging whether the straightness of the scraper conveyor meets the requirement according to the change trend of the distance.
Further, the judging whether the straightness of the scraper conveyor meets the requirement according to the variation trend of the distance specifically comprises:
averaging the distances from each preset number of pixel points on the lower edge to the fitting straight line, and obtaining the difference between the average value and the previous average value, thereby obtaining the distance change delta d between the upper edge and the lower edge of the outer contouriAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
Further, the method further comprises:
if the straightness of the scraper conveyor is judged not to meet the requirement, determining the position of the scraper conveyor with the straightness not meeting the requirement according to the position of the image acquisition device corresponding to the image;
generating adjustment instructions and/or pre-warning information for the position of the face conveyor.
According to a second aspect of the embodiments of the present disclosure, there is provided a scraper conveyor straightness detection device based on video recognition, including:
the acquisition module is used for acquiring an image of an area where the scraper conveyor is located;
the identification module is used for identifying the scraper conveyor and the example segmentation thereof in the image;
the extraction module is used for extracting the coordinate value of the outer contour of the scraper conveyor in the image;
the calculation module is used for calculating a linear correlation coefficient according to the outer contour coordinate value;
and the judging module is used for judging that the straightness of the scraper conveyor does not meet the requirement if the linear correlation coefficient calculated by the calculating module is smaller than a preset threshold value.
Further, the apparatus further comprises:
the fitting module is used for fitting the position coordinates of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
the calculation module is also used for respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
and the judging module is also used for judging whether the straightness of the scraper conveyor meets the requirement or not according to the change trend of the distance if the linear correlation coefficient calculated by the calculating module is greater than or equal to the preset threshold.
Further, the determining module is specifically configured to:
averaging the distances from each preset number of pixel points on the lower edge to the fitting straight line, and obtaining the difference between the average value and the previous average value, thereby obtaining the distance change delta d between the upper edge and the lower edge of the outer contouriAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
Further, the apparatus further comprises:
the positioning module is used for determining the position of the scraper conveyor with the linearity not meeting the requirement according to the position of the image acquisition device corresponding to the image if the judgment module judges that the linearity of the scraper conveyor does not meet the requirement;
and the generating module is used for generating a regulating instruction and/or early warning information aiming at the position of the scraper conveyor.
According to a third aspect of the embodiments of the present disclosure, there is provided a terminal device, including:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the method as described above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the image data of the scraper conveyor is utilized, the straightness condition of the scraper conveyor is obtained through image recognition, the problem of detection failure caused by damage in the use process of the sensor is avoided, meanwhile, the method is simple in required hardware structure, and the price cost is more suitable.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 is a schematic flow diagram illustrating a method for detecting straightness of a scraper conveyor based on video recognition according to an exemplary embodiment of the present disclosure;
FIG. 2 is a marking box and example segmentation results for a flight conveyor;
FIG. 3 is a schematic view of the coordinates of the various positions of the upper edge fitted into a straight line;
fig. 4 is a block diagram illustrating a structure of a scraper conveyor straightness detection device based on video recognition according to an exemplary embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating a computing device, according to an example embodiment of the present disclosure.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred 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.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means two or more unless specifically limited otherwise.
Technical solutions of embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart diagram illustrating a method for detecting straightness of a scraper conveyor based on video recognition according to an exemplary embodiment of the present disclosure.
With reference to figure 1 of the drawings,
the method comprises the following steps:
101. acquiring an image of an area where a scraper conveyor is located;
specifically, in the embodiment of the invention, a high-definition camera can be arranged above the fully mechanized coal mining face channel, and the high-definition camera is used for acquiring the image of the area where the scraper conveyor is located
102. Identifying a scraper conveyor and an instance segmentation thereof in the image;
specifically, the specific implementation process of step 102 is as follows:
1) preparing a data set to construct a scraper conveyor segmentation database with labels and labels, and training an example segmentation model: and acquiring images of a camera, labeling the scraper conveyor in the images, and preparing picture data and label data required by model training. When other equipment or people shield the scraper conveyor to different degrees, the marks need to bypass the shields, and only the parts which are not shielded in the visual field range are marked, so that irrelevant information features are obtained in the feature extraction process, and the detection effect is not influenced.
2) And training an instance segmentation model by using the label data, so that the video intelligent model has the capability of identifying the plate conveyor.
3) The camera collects field condition data, the data are transmitted to the intelligent identification system through a network, the model identifies the scraper conveyor and the example segmentation of the scraper conveyor, and the marking frame and the example segmentation result of the scraper conveyor are shown in figure 2.
103. Extracting the coordinate value of the outer contour of the scraper conveyor in the image;
in step 103, the output example segmentation of the video intelligent model in step 102 is decoded, the profile information of the scraper conveyor is extracted, and the coordinates of each position of the edge of the coal producing plate in the scraper conveyor are extracted through the coordinate relation. The method specifically comprises the following steps:
1) segmenting according to the example to obtain a mask value, carrying out binarization processing on the image, and extracting physical characteristics;
2) coordinate information (x, y) of all the outermost layer contours in the figure is found by using the contours, and therefore the edge position information (x, y) of the coal producing plate in the scraper conveyor is obtained.
104. Calculating a linear correlation coefficient according to the coordinate value of the outer contour;
specifically, the edge position information (x, y) of the coal producing plate in the scraper conveyor is obtained according to step 103, and the linear correlation coefficient r of x and y is obtained by solving the variable values of x and y through a Pearson correlation coefficient (but not limited to the method for solving the Pearson correlation coefficient, and other methods such as cosine correlation, Spearman, Kendal correlation and the like can also be used, and the patent only uses the solution of the Pearson correlation coefficient for explanation).
Wherein the Pearson correlation coefficient solving formula is as follows:
105. and if the calculated linear correlation coefficient is smaller than a preset threshold value, judging that the straightness of the scraper conveyor does not meet the requirement.
Specifically, a linear correlation coefficient r of a contour coordinate on the scraper conveyor is compared with a threshold value alpha of linearity detection set manually, when r is smaller than alpha, the linearity of the scraper conveyor is considered to be not satisfactory, the system can acquire the position of the scraper with the linearity not satisfactory and a difference value between the position of the scraper and the threshold value according to the serial number of the camera, and a corresponding adjusting and early warning mode is made. When r is larger than or equal to alpha, the straightness of the scraper conveyor is considered to meet the requirement, the system can acquire the position of the scraper with the straightness meeting the requirement according to the serial number of the camera, and the meeting information is pushed out.
The invention adopts an example segmentation technology, and calculates the edge straightness situation by extracting the outer contour shape of the scraper conveyor, thereby calculating the straight line situation of the scraper conveyor. The method solves the problems of high cost and construction difficulty caused by the dependence on a large number of sensors in the conventional scraper conveyor measuring method. Meanwhile, the camera is independent of the coal mining equipment on the fully mechanized mining surface, and the operation of the equipment cannot be interfered. Another advantage of the camera is that the camera is more environmentally friendly than the sensing component and is less prone to problems of poor measurement due to sensor failure.
Optionally, in this embodiment, before performing step 103, the method further includes:
and (4) processing the frame-by-frame identification result of the image obtained in the step (102), and when the position of the scraper conveyor is identified to be changed in continuous frames, the process stage of pushing is in progress, and edge extraction is not performed at the moment. When it is found that the position of the identified blade conveyor does not change in successive frames, contour information decoding processing of the blade conveyor is performed. Therefore, the calculation amount is reduced, and the processing efficiency of the intelligent recognition system is improved.
Optionally, in this embodiment, the method further includes:
106. fitting the coordinates of each position of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
specifically, the expression form of the fitted straight line is:
Ax+By+C＝0
the fitting of the respective position coordinates of the upper edge into a straight line is shown in fig. 3.
107. Respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
specifically, position coordinate information (x) of the lower edge profile of the scraper conveyor is acquired0,y0) And calculating the distance between the coordinate of the lower edge and the contour coordinate line on the scraper conveyor obtained by a linear fitting algorithm by using the following formula of the distance from the point to the line:
108. and if the calculated linear correlation coefficient is larger than or equal to the preset threshold, judging whether the straightness of the scraper conveyor meets the requirement according to the change trend of the distance.
Specifically, according to the position between the lower edge profile obtained by calculation under each coordinate and the fitted straight line, the change trend of the distance between the two edges can be obtained.
Averaging the distances between every ten pixel points on the lower edge and the fitting straight line, and obtaining the difference between the average value and the previous average value, thereby obtaining the distance change between the upper edge and the lower edge of the outer contour asΔdiAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
Optionally, in this embodiment, the method further includes:
109. if the straightness of the scraper conveyor is judged not to meet the requirement, determining the position of the scraper conveyor with the straightness not meeting the requirement according to the position of the image acquisition device corresponding to the image;
110. generating adjustment instructions and/or pre-warning information for the position of the face conveyor.
Specifically, the comparison result of the linear correlation coefficient r of the contour coordinate on the scraper conveyor and the artificially set threshold value alpha of the linearity detection and the trend of the distance change delta di between the fitting line of the lower edge and the upper edge of the scraper conveyor are combined, the linear condition of the plate conveyor is comprehensively judged according to the rules defined in the content of the embodiment, information whether the linearity of the scraper conveyor meets the process requirements is output, and corresponding processing rules are given according to different output information.
1) When r is less than alpha, the straightness of the scraper conveyor output by the system does not meet the requirement, the system gives an alarm to the camera section which does not meet the straight line, gives an instruction to pushing equipment and related personnel, or enters a preset processing mechanism;
2) when r is larger than or equal to alpha, but the change of the distance between the lower edge and the upper edge fitting line of the scraper conveyor in each pixel is larger than a set threshold value delta d, giving an early warning prompt by the system, and manually checking or entering another preset processing mechanism;
3) when r is larger than or equal to alpha and the change of the distance delta di is smaller than or equal to a set threshold delta d, the system output push-slip is successful, and the straightness meets the process requirements of the system.
Fig. 4 is a block diagram illustrating a structure of a scraper conveyor straightness detection device based on video recognition according to an exemplary embodiment of the present disclosure.
Referring to fig. 4, the apparatus includes:
the acquisition module is used for acquiring an image of an area where the scraper conveyor is located;
the identification module is used for identifying the scraper conveyor and the example segmentation thereof in the image;
the extraction module is used for extracting the coordinate value of the outer contour of the scraper conveyor in the image;
the calculation module is used for calculating a linear correlation coefficient according to the outer contour coordinate value;
and the judging module is used for judging that the straightness of the scraper conveyor does not meet the requirement if the linear correlation coefficient calculated by the calculating module is smaller than a preset threshold value.
Optionally, in this embodiment, the apparatus further includes:
the fitting module is used for fitting the position coordinates of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
the calculation module is also used for respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
and the judging module is also used for judging whether the straightness of the scraper conveyor meets the requirement or not according to the change trend of the distance if the linear correlation coefficient calculated by the calculating module is greater than or equal to the preset threshold.
Optionally, in this embodiment, the determining module is specifically configured to:
averaging the distances from each preset number of pixel points on the lower edge to the fitting straight line, and obtaining the difference between the average value and the previous average value, thereby obtaining the distance change delta d between the upper edge and the lower edge of the outer contouriAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
Optionally, in this embodiment, the apparatus further includes:
the positioning module is used for determining the position of the scraper conveyor with the linearity not meeting the requirement according to the position of the image acquisition device corresponding to the image if the judgment module judges that the linearity of the scraper conveyor does not meet the requirement;
and the generating module is used for generating a regulating instruction and/or early warning information aiming at the position of the scraper conveyor.
FIG. 5 is a schematic diagram illustrating a computing device, according to an example embodiment of the present disclosure.
Referring to fig. 5, computing device 500 includes memory 510 and processor 520.
The Processor 520 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 510 may include various types of storage units, such as system memory, Read Only Memory (ROM), and permanent storage. Wherein the ROM may store static data or instructions for the processor 520 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. Further, the memory 510 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, may also be employed. In some embodiments, memory 510 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a digital versatile disc read only (e.g., DVD-ROM, dual layer DVD-ROM), a Blu-ray disc read only, an ultra-dense disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disk, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 510 has stored thereon executable code that, when processed by the processor 520, may cause the processor 520 to perform some or all of the methods described above.
The aspects of the present disclosure have been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that the acts and modules referred to in the specification are not necessarily required by the invention. In addition, it can be understood that steps in the method of the embodiment of the present disclosure may be sequentially adjusted, combined, and deleted according to actual needs, and modules in the device of the embodiment of the present disclosure may be combined, divided, and deleted according to actual needs.
Furthermore, the method according to the present disclosure may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present disclosure.
Alternatively, the present disclosure may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) that, when executed by a processor of an electronic device (or computing device, server, or the like), causes the processor to perform some or all of the various steps of the above-described method according to the present disclosure.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method for detecting straightness of a scraper conveyor based on video identification is characterized by comprising the following steps:
acquiring an image of an area where a scraper conveyor is located;
identifying a scraper conveyor and an instance segmentation thereof in the image;
extracting the coordinate value of the outer contour of the scraper conveyor in the image;
calculating a linear correlation coefficient according to the coordinate value of the outer contour;
and if the calculated linear correlation coefficient is smaller than a preset threshold value, judging that the straightness of the scraper conveyor does not meet the requirement.
2. The method of claim 1, further comprising:
fitting the coordinates of each position of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
and if the calculated linear correlation coefficient is larger than or equal to the preset threshold, judging whether the straightness of the scraper conveyor meets the requirement according to the change trend of the distance.
3. The method according to claim 2, wherein the judging whether the straightness of the scraper conveyor meets the requirement according to the change trend of the distance specifically comprises the following steps:
averaging the distances from each preset number of pixel points on the lower edge to the fitting straight line, and obtaining the difference between the average value and the previous average value, thereby obtaining the distance change delta d between the upper edge and the lower edge of the outer contouriAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
4. The method according to any one of claims 1-3, further comprising:
if the straightness of the scraper conveyor is judged not to meet the requirement, determining the position of the scraper conveyor with the straightness not meeting the requirement according to the position of the image acquisition device corresponding to the image;
generating adjustment instructions and/or pre-warning information for the position of the face conveyor.
5. The utility model provides a scraper conveyor straightness accuracy detection device based on video identification which characterized in that includes:
the acquisition module is used for acquiring an image of an area where the scraper conveyor is located;
the identification module is used for identifying the scraper conveyor and the example segmentation thereof in the image;
the extraction module is used for extracting the coordinate value of the outer contour of the scraper conveyor in the image;
the calculation module is used for calculating a linear correlation coefficient according to the outer contour coordinate value;
and the judging module is used for judging that the straightness of the scraper conveyor does not meet the requirement if the linear correlation coefficient calculated by the calculating module is smaller than a preset threshold value.
6. The apparatus of claim 5, further comprising:
the fitting module is used for fitting the position coordinates of the upper edge of the outer contour of the scraper conveyor into a straight line by adopting a linear fitting algorithm;
the calculation module is also used for respectively calculating the distance between each position coordinate of the lower edge of the outer contour of the scraper conveyor and the fitted straight line;
and the judging module is also used for judging whether the straightness of the scraper conveyor meets the requirement or not according to the change trend of the distance if the linear correlation coefficient calculated by the calculating module is greater than or equal to the preset threshold.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
for each preset number of pixel points on the lower edge to the fitting straight lineThe distance between the two is averaged, and the difference between the average value of the distance and the previous average value of the distance is obtained, so that the change of the distance between the upper edge and the lower edge of the outer contour is obtained as delta diAccording to Δ diIf Δ di is larger than a set threshold Δ d, the linearity is considered to be unsatisfactory, e.g., Δ diAnd if the value is less than or equal to the set threshold value delta d, the straightness meets the requirement.
8. The apparatus of any one of claims 5-7, further comprising:
the positioning module is used for determining the position of the scraper conveyor with the linearity not meeting the requirement according to the position of the image acquisition device corresponding to the image if the judgment module judges that the linearity of the scraper conveyor does not meet the requirement;
and the generating module is used for generating a regulating instruction and/or early warning information aiming at the position of the scraper conveyor.
9. A terminal device, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-4.
10. A non-transitory machine-readable storage medium having executable code stored thereon, wherein the executable code, when executed by a processor of an electronic device, causes the processor to perform the method of any of claims 1-4.
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