CN113781511B - Conveyor belt edge wear detection method, conveyor belt edge wear detection device, computer equipment and storage medium - Google Patents
Conveyor belt edge wear detection method, conveyor belt edge wear detection device, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a conveyor belt edge abrasion detection method, a conveyor belt edge abrasion detection device, computer equipment and a storage medium. The method comprises the following steps: acquiring a picture of the conveyor belt to obtain a picture to be detected; performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background; edge key point detection is carried out on the picture to be detected; determining a parameter equation of a straight line; determining a conveyor belt edge reference plane; carrying out roller detection on the picture to be detected; determining a roller datum plane; determining the abrasion degree of the edge of the conveyor belt according to the roller reference surface, the edge reference surface of the conveyor belt and the area of the conveyor belt; and carrying out alarm processing according to the abrasion degree of the edge of the conveyor belt. By implementing the method provided by the embodiment of the invention, the problem of inaccurate edge linear equation caused by the edge abrasion of the conveyor belt can be avoided, the edge abrasion detection can be identified, and the abrasion degree can be given.
Description
Technical Field
The present invention relates to conveyor belts, and more particularly to a conveyor belt edge wear detection method, apparatus, computer device, and storage medium.
Background
The detection of the edge wear of the cargo transportation conveyor belt is a very important part in the port cargo transportation detection link, but most of the detection at present is excessively dependent on hardware equipment such as sensors, and the equipment is often too expensive, and the deployment process is relatively complex.
The existing detection method uses a photoelectric sensor module, a special sensor module, an RFID module and an upper computer. The damage is detected by the photoelectric sensor, and then the information is transmitted to the upper computer. The method has the advantages of high accuracy and timeliness, but the cost is greatly increased due to the addition of hardware, and once a certain block in the hardware is in question, the whole system is in question. Still another detection method is to use a method for detecting the abrasion of the conveyor belt based on a detection system, wherein the detection system comprises a plurality of hardware installations of the conveyor belt structure, a signal collector and an alarm system, and the rotation speed value of the roller is analyzed by using voltage. This approach is highly demanding for the rollers, and once a problem occurs with the rollers, the system may generate false alarms. The conventional belt edge datum line adopts Hough transformation to perform linear detection, but the method often causes uneven caused by abrasion of the belt edge, so that the situation that a plurality of edges are detected or some edges are not detected in the belt edge detection is generated.
Therefore, it is necessary to design a new method to avoid the problem of inaccurate edge linear equation caused by the edge abrasion of the conveyor belt, and to identify the edge abrasion detection and to give the abrasion degree.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a conveyor belt edge abrasion detection method, a conveyor belt edge abrasion detection device, computer equipment and a storage medium.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the method for detecting the edge wear of the conveyor belt comprises the following steps:
acquiring a picture of the conveyor belt to obtain a picture to be detected;
performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background;
detecting edge key points of the picture to be detected to obtain the edge key points;
determining a parameter equation of a straight line according to the edge key points, and determining a conveyor belt edge reference plane according to the parameter equation of the straight line;
detecting the picture to be detected by a roller to obtain an upper edge roller and a lower edge roller;
determining a roller reference surface according to the upper edge roller and the lower edge roller;
determining the abrasion degree of the edge of the conveyor belt according to the roller datum plane, the conveyor belt edge datum plane and the conveyor belt area;
And carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
The further technical scheme is as follows: the semantic segmentation is carried out on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background, and the semantic segmentation picture comprises the following steps:
carrying out semantic segmentation on the picture to be detected by adopting a semantic segmentation network so as to obtain a segmented picture;
and performing black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture containing a conveyor belt region and a background.
The further technical scheme is as follows: the semantic segmentation network is formed by adopting a resnet50 model as a skeleton network, combining a context path and a space path mechanism to perform feature fusion through a feature fusion module, changing a loss function of the semantic segmentation network into a Dice loss function, selecting an attention refinement module before the feature fusion module to output and add two features as auxiliary loss functions, and adding the Dice loss function and the auxiliary loss function according to a proportion to form a final loss function of the semantic segmentation network.
The further technical scheme is as follows: the step of detecting the edge key points of the picture to be detected to obtain the edge key points comprises the following steps:
And identifying coordinates of the four key points of the picture to be detected by adopting a CPN network so as to obtain edge key points.
The further technical scheme is as follows: and the confidence coefficient of the key points of the CPN is 0.5-0.7, k-means clustering is carried out on coordinates of the four key points in space, and the central point of the cluster is selected as an edge key point.
The further technical scheme is as follows: the roller detection is performed on the picture to be detected to obtain an upper edge roller and a lower edge roller, and the roller detection comprises:
carrying out target positioning on the rolling shafts in the picture to be detected by adopting a yolov5 target detection model to obtain the image coordinate position of each rolling shaft;
calculating the center point coordinates of each roller according to the image coordinate position of each roller;
the distance of the center point coordinates to the upper edge conveyor belt and the distance of the center point coordinates to the lower edge conveyor belt are calculated to determine the upper edge rollers and the lower edge rollers.
The further technical scheme is as follows: the roller datum plane is determined according to the upper edge roller and the lower edge roller, and the roller datum plane comprises:
calculating the endpoint coordinates of the upper edge roller and the lower edge roller;
determining an upper datum line and a lower datum line according to the endpoint coordinates;
And determining a rolling shaft datum plane according to the upper datum line and the lower datum line.
The invention also provides a conveyor belt edge wear detection device, comprising:
the image acquisition unit is used for acquiring the image of the conveyor belt to obtain an image to be detected;
the segmentation unit is used for carrying out binarization semantic segmentation on the picture to be detected so as to obtain a semantic segmentation picture containing a conveyor belt region and a background;
the key point detection unit is used for detecting the edge key points of the picture to be detected so as to obtain the edge key points;
the edge reference plane determining unit is used for determining a parameter equation of a straight line according to the edge key points and determining an edge reference plane of the conveyor belt according to the parameter equation of the straight line;
the roller detection unit is used for detecting the roller of the picture to be detected so as to obtain an upper edge roller and a lower edge roller;
a roller reference surface determining unit for determining a roller reference surface based on the upper edge roller and the lower edge roller;
the degree determining unit is used for determining the abrasion degree of the edge of the conveyor belt according to the roller reference surface, the edge reference surface of the conveyor belt and the area of the conveyor belt;
and the alarm processing unit is used for carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
The invention also provides a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the method when executing the computer program.
The present invention also provides a storage medium storing a computer program which, when executed by a processor, performs the above-described method.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the edge defect is detected in a picture mode, so that the cost is greatly saved and the deployment step is simplified; the method comprises the steps of carrying out semantic segmentation on pictures, key point detection, roller and conveyor belt edge reference plane determination and conveyor belt edge abrasion degree determination, identifying edge abrasion based on deep learning, identifying edge abrasion detection, simultaneously providing abrasion degree, carrying out semantic segmentation on the parts of a conveyor belt based on a semantic segmentation network, and carrying out key point detection on the basis of the obtained semantic segmentation result, thereby obtaining accurate conveyor belt edges, and avoiding the problem of inaccurate parameter equation of edge straight lines caused by conveyor belt edge abrasion.
The invention is further described below with reference to the drawings and specific embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention;
FIG. 5 is a schematic sub-flowchart of a method for detecting belt edge wear according to an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a conveyor belt edge wear detection device provided by an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a segmentation unit of a conveyor belt edge wear detection device provided by an embodiment of the present invention;
Fig. 8 is a schematic block diagram of a roller detecting unit of a conveyor belt edge wear detecting device according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of a roller reference surface determination unit of a conveyor belt edge wear detection apparatus according to an embodiment of the present invention
Fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification 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 be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram of an application scenario of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention. Fig. 2 is a schematic flow chart of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention. The conveyor belt edge wear detection method is applied to a server. The server performs data interaction with the terminal and the camera, wherein the camera is used for shooting pictures of the transmission belt, and semantic segmentation, edge key point detection, roller detection, reference surface detection and edge abrasion detection are performed by the server to determine the edge abrasion degree and perform alarm processing.
Fig. 2 is a schematic flow chart of a method for detecting edge wear of a conveyor belt according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S180.
S110, acquiring a picture of the conveyor belt to obtain a picture to be detected.
In this embodiment, the picture to be detected refers to a picture formed by collecting picture data at a fixed position set by the cargo transportation conveyor belt, and a fixed preset value is given to a focal length and a height of the pan-tilt during the collection.
The dependence on hardware such as a sensor is eliminated, and the edge abrasion detection of the cargo transportation conveyor belt can be performed only according to the image, so that the cost is greatly saved and the deployment steps are simplified.
S120, performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background.
In this embodiment, the semantically segmented picture refers to a picture including a conveyor belt region and a background.
In one embodiment, referring to fig. 3, the step S120 may include steps S121 to S122.
S121, carrying out semantic segmentation on the picture to be detected by adopting a semantic segmentation network so as to obtain a segmented picture.
In this embodiment, the split pictures refer to two types of semantic split pictures of the conveyor belt mask and the background mask.
Specifically, the semantic segmentation network is formed by adopting a resnet50 model as a skeleton network, the resnet50 model is formed by combining a context path and a space path mechanism and performing feature fusion through a feature fusion module, a loss function of the semantic segmentation network is changed into a Dice loss function, an attention refinement module before the feature fusion module is selected to output and add two features to serve as auxiliary loss functions, and the Dice loss function and the auxiliary loss function are added according to a proportion to form a final loss function of the semantic segmentation network.
Specifically, the semantic segmentation of the cargo transportation conveyor belt adopts a reset 50 as a backbone network and combines a context path and a spatial path mechanism to perform Feature Fusion through a Feature Fusion module, so as to finally obtain a semantic segmentation network, wherein a loss function used in the semantic segmentation network is changed by a dice loss function l 1 . ARM before Feature Fusion (attention refinement module, attention Refinement Modul)e) Output adds two features as auxiliary loss function 2 And l 3 Finally, they are added in a ratio of 1:1:1 as a final Loss function loss=l 1 +l 2 +l 3 。
In order to ensure the accuracy of prediction of the conveyor belt mask, firstly, pixels with the original width of 1920 and the original height of 1080 of a picture to be detected are used as inputs of a network, and the output types of the semantic segmentation network are respectively two types of semantic segmentation graphs of the conveyor belt mask and the background mask.
S122, performing black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture containing a conveyor belt region and a background.
In black-and-white binary mapping of the segmented picture, three channels of pixels of the conveyor belt part are set as pixel points with a value of 255, and three channels of the background part are set as pixel points with a value of 0, so that a semantically segmented picture to be detected containing a conveyor belt region and a background is formed, and meanwhile, the conveyor belt region is defined as s Conveyor belt 。
S130, detecting edge key points of the picture to be detected to obtain the edge key points.
In this embodiment, the edge key points refer to four points, that is, the start and end points of the upper edge of the conveyor belt, the start and end points of the lower edge.
Specifically, a CPN network is adopted to identify coordinates of four key points of the picture to be detected, so as to obtain edge key points.
The confidence coefficient of key points of the CPN network is 0.5-0.7, k-means clustering is carried out on coordinates of four key points in space, and a central point of the cluster is selected as an edge key point.
In the detection of the belt edge, since the belt edge is not flat, the straight line detection based on hough change is not effective, and here, the detection is based on the key points of the belt, and the points of the start end and the end of the upper and lower edges of the belt are respectively identified. The model used here is the CPN (cascading pyramid network, cascaded Pyramid Network) network to identify four keypoints.
Two key points of the starting end and the ending end of the upper edge can be obtained through the neural network. The upper edge point is p s,top ，p e,top The lower edge point is p s,bottom ，p e,bottom . In order to prevent the occurrence of the conditions of detection of the redundant key points and missed detection of the key points due to the edges, the confidence coefficient of the key points is slightly reduced to be 0.6, k-means clustering is simultaneously carried out on the coordinates of the key points in space on the basis of improving the recall rate of the key points, k=4 is set, and the central point of the cluster is selected as the representative point.
And S140, determining a parameter equation of the straight line according to the edge key points, and determining a conveyor belt edge reference plane according to the parameter equation of the straight line.
In this embodiment, the parameter equation of the straight line refers to the parameter equation of the straight line of the edge of the conveyor belt.
In this embodiment, the belt edge reference plane refers to an area formed by straight lines of the upper and lower edges of the belt.
The parameter equation of the straight line of the upper and lower conveyer belt edges can be determined according to the two key points of the upper edge point, namely the upper conveyer belt edge, and the two key points of the lower edge point, namely the lower conveyer belt edge, and the intersection point coordinates of the two straight line equations and the original image boundary are obtained according to the parameter equation of the straight line of the upper and lower conveyer belt edges and the size of the image, and the conveyer belt edge reference plane S is calculated by a closed frame enclosed by the intersection point coordinates (Edge) 。
The polygon is segmented into triangles and then all triangle areas are added together, the area of the triangle being calculated using the cross-product of the vectors. I.e. The vector is obtained by the coordinates of the points A and B; the A/B is->Is a mold of (2); sin (BAC) is the sin value of the ++BAC. The method has the advantages that the existing point coordinates can be directly utilized to convert into vectors, and the method also has the advantages of supporting the calculation of the area of the convex polygon and the calculation of the area of the concave polygon.
The feature extraction part is optimized for key point detection, the FPN (feature map pyramid network, feature Pyramid Networks) network and a transducer mechanism are combined together to perform model feature extraction, and the Globalnet and the RefineNet are combined to perform feature point recognition. In order to prevent the occurrence of the conditions of the detection of the critical points and the omission of the critical points due to the detection of the edge redundant critical points, the confidence coefficient of the critical points is reduced, k-means clustering is carried out on the coordinates of the critical points in space, k=4 is set, and the central point of the cluster is selected as the representative point. In the training process, an OHEM strategy is adopted, and only key points of top2 with the largest loss are returned, because individual key points are difficult to identify due to dust such as light rays, and after two corresponding conveyor belt edge lines are obtained, the conveyor belt edge reference plane is determined.
And S150, detecting the roller of the picture to be detected to obtain an upper edge roller and a lower edge roller.
In this embodiment, the rollers of the conveyor belt include an upper edge roller and a lower edge roller.
In one embodiment, referring to fig. 4, the step S150 may include steps S151 to S153.
And S151, carrying out target positioning on the rolling shafts in the picture to be detected by adopting a yolov5 target detection model to obtain the image coordinate position of each rolling shaft.
In this embodiment, the image coordinate position of each roller refers to the coordinates of the upper edge roller and the lower edge roller within the image.
Specifically, the roller is subjected to target positioning through a yolov5 target detection model, and the image coordinate position (x min ，y min ，x max ，y max ). Detection for selecting yolov5 target detection modelThe measurement confidence was 0.6. In order to detect the accuracy of the top end coordinates of the rolling shafts, a selected structure is yolo5x, the loss function of the regression frame coordinates selects CIOUloss as the corresponding loss function, and the CIOUloss and the classification cross entropy loss are combined to form an integral loss function.
Here, it should be noted that if there is a roller at the edge of the image, the condition of the roller being insufficient may be easily caused to cause the end point value to be inaccurate, the position of the roller is determined based on the condition, and the end point coordinates of the roller are not considered if any of the following conditions is satisfied.
(1)x min <α；(2)width-x max <α；(3)y min <α；(4)height-y max <Alpha; where width is the width of the picture, height is the height of the picture, and α=10 is set.
And S152, calculating the center point coordinates of each roller according to the image coordinate positions of each roller.
In the present embodiment, the center point coordinates of each roller frame are calculated separately
And S153, calculating the distance from the center point coordinates to the upper edge conveyor belt and the distance from the center point coordinates to the lower edge conveyor belt to determine an upper edge roller and a lower edge roller.
In the present embodiment, by calculating the center point coordinates (c x ，c y ) And to the upper edge l Conveyor belt Distance d of (2) Upper edge To the lower edge l Conveyor belt Distance d of (2) Lower edge of . If d Lower edge of <d Upper edge The roller is determined to be a lower edge roller and the opposite is called an upper edge roller.
And S160, determining a reference plane according to the upper edge roller and the lower edge roller.
In this embodiment, the roller reference surface refers to an area between two reference lines defined by the upper edge roller and the lower edge roller.
In one embodiment, referring to fig. 5, the step S160 may include steps S161 to S163.
S161, calculating the endpoint coordinates of the upper edge roller and the lower edge roller;
s162, determining an upper datum line and a lower datum line according to the endpoint coordinates;
and S163, determining a roller datum plane according to the upper datum line and the lower datum line.
If the slope k of the conveyor belt Conveyor belt >0, for the upper edge rollers, selecting the upper right corner coordinates of each upper edge roller as the endpoint coordinates p i(k＞0,top) (x max ，y min ) The lower edge rollers select the upper left corner of each lower edge roller as the endpoint coordinate p i(k＞0,bottom) (x min ，y min ). If the slope k of the conveyor belt Conveyor belt <0, for the upper edge roller, the upper left corner coordinate of the roller is selected as the endpoint coordinate p i(k＜0,top) (x min ，y min ) The lower edge roller selects the upper right corner coordinate of the roller as the endpoint coordinate p i(k＜0,top) (x max ，y min ) Finally, two datum lines l are fitted through the endpoints Datum . According to the two obtained datum lines l Datum The intersection point coordinates of the two straight line equations and the boundary of the original image are obtained by the dimensions of the straight line equations and the image, and the roller reference surface s is calculated by a closed frame surrounded by the intersection point coordinates Roller shaft 。
Specifically, a polygon is cut into a plurality of triangles, and then all triangle areas are added up, and the areas of the triangles are calculated by using the cross-product of vectors. I.e. Wherein (1)>The vector is obtained by the coordinates of the points A and B; the A/B is->Is a mold of (2); sin (BAC) is the sin value of the ++BAC. The method can directly convert ready-made point coordinates into vectors, and has the other advantage of supporting the calculation of the area of the convex polygon and the calculation of the area of the concave polygon.
S170, determining the abrasion degree of the edge of the conveyor belt according to the roller reference surface, the edge reference surface of the conveyor belt and the area of the conveyor belt.
Based on the above-obtained upper and lower roller areas S Roller shaft By the formulaHere->Indicated is an exclusive or operation. Based on the formula->To evaluate the extent of belt edge wear, μ representing the more severe the belt wear.
The edge abrasion is identified based on deep learning, the edge abrasion detection can be used for identifying, and meanwhile, the abrasion degree can be given, so that early warning can be carried out on the abrasion of the conveying belt.
And S180, carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
When mu > lambda, a wear alarm will be given, where lambda is set to 0.03, which of course may be set according to the actual situation.
According to the method for detecting the edge defects of the conveyor belt, the edge defects are detected in the form of pictures, so that the cost is greatly saved, and the deployment steps are simplified; the method comprises the steps of carrying out semantic segmentation on pictures, key point detection, roller and conveyor belt edge reference plane determination and conveyor belt edge abrasion degree determination, identifying edge abrasion based on deep learning, identifying edge abrasion detection, simultaneously providing abrasion degree, carrying out semantic segmentation on the parts of a conveyor belt based on a semantic segmentation network, and carrying out key point detection on the basis of the obtained semantic segmentation result, thereby obtaining accurate conveyor belt edges, and avoiding the problem of inaccurate parameter equation of edge straight lines caused by conveyor belt edge abrasion.
Fig. 6 is a schematic block diagram of a conveyor belt edge wear detection device 300 provided in an embodiment of the present invention. As shown in fig. 6, the present invention also provides a conveyor belt edge wear detection apparatus 300 corresponding to the above conveyor belt edge wear detection method. The conveyor belt edge wear detection apparatus 300 includes means for performing the conveyor belt edge wear detection method described above, which may be configured in a server. Specifically, referring to fig. 6, the conveyor belt edge wear detection apparatus 300 includes a picture acquisition unit 301, a division unit 302, a key point detection unit 303, an edge reference surface determination unit 304, a roller detection unit 305, a roller reference surface determination unit 306, a degree determination unit 307, and an alarm processing unit 308.
A picture obtaining unit 301, configured to obtain a picture of the conveyor belt to obtain a picture to be detected; the segmentation unit 302 is configured to perform binarization semantic segmentation on the to-be-detected picture to obtain a semantic segmentation picture that includes a conveyor belt region and a background; the key point detection unit 303 is configured to perform edge key point detection on the to-be-detected picture to obtain an edge key point; an edge reference plane determining unit 304, configured to determine a parameter equation of a straight line according to the edge key point, and determine a conveyor belt edge reference plane according to the parameter equation of the straight line; a roller detecting unit 305, configured to perform roller detection on the picture to be detected, so as to obtain an upper edge roller and a lower edge roller; a roller reference surface determining unit 306 for determining a roller reference surface from the upper edge roller and the lower edge roller; a degree determining unit 307 for determining a degree of belt edge wear based on the roller reference surface, the belt edge reference surface, and the belt region; and the alarm processing unit 308 is used for performing alarm processing according to the abrasion degree of the edge of the conveyor belt.
In one embodiment, as shown in fig. 7, the partitioning unit 302 includes a semantic partitioning subunit 3021 and a mapping subunit 3022.
A semantic segmentation subunit 3021, configured to perform semantic segmentation on the to-be-detected picture by using a semantic segmentation network, so as to obtain a segmented picture; the mapping subunit 3022 is configured to perform black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture that includes a conveyor belt region and a background.
In an embodiment, the key point detection unit 303 is configured to identify coordinates of four key points of the picture to be detected by using a CPN network, so as to obtain an edge key point.
In one embodiment, as shown in fig. 8, the roller detecting unit 305 includes a target positioning subunit 3051, a center point coordinate calculating subunit 3052, and a distance calculating subunit 3053.
The target positioning subunit 3051 is configured to perform target positioning on the rollers in the image to be detected by using a yolov5 target detection model, so as to obtain an image coordinate position of each roller; the central point coordinate calculating subunit 3052 is configured to calculate a central point coordinate of each roller according to the image coordinate position of each roller; and a distance calculating sub-unit 3053 for calculating a distance from the center point coordinates to the upper edge conveyor and a distance from the center point coordinates to the lower edge conveyor to determine an upper edge roller and a lower edge roller.
In one embodiment, as shown in fig. 9, the roller reference plane determining unit 306 includes an end point coordinate calculating subunit 3061, a reference line determining subunit 3062, and a plane determining subunit 3063.
An endpoint coordinate calculation subunit 3061, configured to calculate endpoint coordinates of the upper edge roller and the lower edge roller; a reference line determining subunit 3062, configured to determine an upper reference line and a lower reference line according to the endpoint coordinates; the surface determining subunit 3063 is configured to determine a roller reference surface based on the upper reference line and the lower reference line.
It should be noted that, as will be clearly understood by those skilled in the art, the specific implementation process of the above-mentioned conveyor belt edge wear detection device 300 and each unit may refer to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, the description is omitted here.
The conveyor belt edge wear detection apparatus 300 described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, where the server may be a stand-alone server or may be a server cluster formed by a plurality of servers.
With reference to FIG. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a conveyor belt edge wear detection method.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a conveyor belt edge wear detection method.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of a portion of the architecture in connection with the present application and is not intended to limit the computer device 500 to which the present application is applied, and that a particular computer device 500 may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to execute a computer program 5032 stored in a memory to implement the steps of:
acquiring a picture of the conveyor belt to obtain a picture to be detected; performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background; detecting edge key points of the picture to be detected to obtain the edge key points; determining a parameter equation of a straight line according to the edge key points, and determining a conveyor belt edge reference plane according to the parameter equation of the straight line; detecting the picture to be detected by a roller to obtain an upper edge roller and a lower edge roller; determining a roller reference surface according to the upper edge roller and the lower edge roller; determining the abrasion degree of the edge of the conveyor belt according to the roller datum plane, the conveyor belt edge datum plane and the conveyor belt area; and carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
In an embodiment, when the processor 502 performs the step of performing semantic segmentation on the to-be-detected picture to obtain a semantically segmented picture including a conveyor belt region and a background, the following steps are specifically implemented:
carrying out semantic segmentation on the picture to be detected by adopting a semantic segmentation network so as to obtain a segmented picture; and performing black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture containing a conveyor belt region and a background.
The semantic segmentation network is formed by adopting a resnet50 model as a skeleton network, combining a context path and a space path mechanism to perform feature fusion through a feature fusion module, changing a loss function of the semantic segmentation network into a Dice loss function, selecting an attention refinement module before the feature fusion module to output and add two features as auxiliary loss functions, and adding the Dice loss function and the auxiliary loss function according to a proportion to form a final loss function of the semantic segmentation network.
In an embodiment, when implementing the step of performing edge keypoint detection on the to-be-detected picture to obtain an edge keypoint, the processor 502 specifically implements the following steps:
and identifying coordinates of the four key points of the picture to be detected by adopting a CPN network so as to obtain edge key points.
And the confidence coefficient of the key points of the CPN is 0.5-0.7, the coordinates of the four key points are spatially clustered by k-means, and the central point of the clustered cluster is selected as an edge key point.
In an embodiment, when implementing the roller detection on the to-be-detected picture to obtain the upper edge roller and the lower edge roller, the processor 502 specifically implements the following steps:
Carrying out target positioning on the rolling shafts in the picture to be detected by adopting a yolov5 target detection model to obtain the image coordinate position of each rolling shaft; calculating the center point coordinates of each roller according to the image coordinate position of each roller; the distance of the center point coordinates to the upper edge conveyor belt and the distance of the center point coordinates to the lower edge conveyor belt are calculated to determine the upper edge rollers and the lower edge rollers.
In one embodiment, the processor 502 performs the following steps when performing the step of determining the roller reference plane according to the upper edge roller and the lower edge roller:
calculating the endpoint coordinates of the upper edge roller and the lower edge roller; determining an upper datum line and a lower datum line according to the endpoint coordinates; and determining a rolling shaft datum plane according to the upper datum line and the lower datum line.
It should be appreciated that in embodiments of the present application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that all or part of the flow in a method embodying the above described embodiments may be accomplished by computer programs instructing the relevant hardware. The computer program comprises program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer readable storage medium. The storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring a picture of the conveyor belt to obtain a picture to be detected; performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background; detecting edge key points of the picture to be detected to obtain the edge key points; determining a parameter equation of a straight line according to the edge key points, and determining a conveyor belt edge reference plane according to the parameter equation of the straight line; detecting the picture to be detected by a roller to obtain an upper edge roller and a lower edge roller; determining a roller reference surface according to the upper edge roller and the lower edge roller; determining the abrasion degree of the edge of the conveyor belt according to the roller datum plane, the conveyor belt edge datum plane and the conveyor belt area; and carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
In an embodiment, when the processor executes the computer program to implement the step of semantically segmenting the picture to be detected to obtain semantically segmented pictures including a conveyor belt region and a background, the method specifically includes the following steps:
carrying out semantic segmentation on the picture to be detected by adopting a semantic segmentation network so as to obtain a segmented picture; and performing black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture containing a conveyor belt region and a background.
The semantic segmentation network is formed by adopting a resnet50 model as a skeleton network, combining a context path and a space path mechanism to perform feature fusion through a feature fusion module, changing a loss function of the semantic segmentation network into a Dice loss function, selecting an attention refinement module before the feature fusion module to output and add two features as auxiliary loss functions, and adding the Dice loss function and the auxiliary loss function according to a proportion to form a final loss function of the semantic segmentation network.
In an embodiment, when the processor executes the computer program to perform the step of performing edge keypoint detection on the to-be-detected picture to obtain an edge keypoint, the method specifically includes the following steps:
And identifying coordinates of the four key points of the picture to be detected by adopting a CPN network so as to obtain edge key points.
And the confidence coefficient of the key points of the CPN is 0.5-0.7, the coordinates of the four key points are spatially clustered by k-means, and the central point of the clustered cluster is selected as an edge key point.
In an embodiment, when the processor executes the computer program to perform the step of detecting the roller of the picture to be detected to obtain an upper edge roller and a lower edge roller, the following steps are specifically implemented:
carrying out target positioning on the rolling shafts in the picture to be detected by adopting a yolov5 target detection model to obtain the image coordinate position of each rolling shaft; calculating the center point coordinates of each roller according to the image coordinate position of each roller; the distance of the center point coordinates to the upper edge conveyor belt and the distance of the center point coordinates to the lower edge conveyor belt are calculated to determine the upper edge rollers and the lower edge rollers.
In one embodiment, the processor, when executing the computer program to perform the step of determining the roller reference plane based on the upper edge roller and the lower edge roller, performs the following steps:
Calculating the endpoint coordinates of the upper edge roller and the lower edge roller; determining an upper datum line and a lower datum line according to the endpoint coordinates; and determining a rolling shaft datum plane according to the upper datum line and the lower datum line.
The storage medium may be a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, or other various computer-readable storage media that can store program codes.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. 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 invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs. In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The integrated unit may be stored in a storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A conveyor belt edge wear detection method comprising:
acquiring a picture of the conveyor belt to obtain a picture to be detected;
performing binarization semantic segmentation on the picture to be detected to obtain a semantic segmentation picture containing a conveyor belt region and a background;
detecting edge key points of the picture to be detected to obtain the edge key points;
determining a parameter equation of a straight line according to the edge key points, and determining a conveyor belt edge reference plane according to the parameter equation of the straight line;
detecting the picture to be detected by a roller to obtain an upper edge roller and a lower edge roller;
determining a roller reference surface according to the upper edge roller and the lower edge roller;
determining the abrasion degree of the edge of the conveyor belt according to the roller datum plane, the conveyor belt edge datum plane and the conveyor belt area; based on the formulaTo evaluate the extent of belt edge wear, with larger μ representing more severe belt wear; s is(s) Conveyor belt An area that is the conveyor belt area; s is(s) (Edge) Refers to the area of the conveyor belt edge datum plane; s is S Roller shaft The area of the datum plane of the roller is referred to; indicated is an exclusive or operation;
and carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
2. The method for detecting the edge wear of a conveyor belt according to claim 1, wherein the performing semantic segmentation on the picture to be detected to obtain a semantically segmented picture including a conveyor belt region and a background comprises:
carrying out semantic segmentation on the picture to be detected by adopting a semantic segmentation network so as to obtain a segmented picture;
and performing black-and-white binarization mapping on the segmented picture to obtain a semantic segmented picture containing a conveyor belt region and a background.
3. The method for detecting the edge wear of the conveyor belt according to claim 2, wherein the semantic segmentation network is formed by feature fusion of a feature fusion module by adopting a resnet50 model as a skeleton network, the resnet50 model is combined with a context path and a space path mechanism, a loss function of the semantic segmentation network is changed to a Dice loss function, an attention refinement module before the feature fusion module is selected to output and add two features as auxiliary loss functions, and the Dice loss functions and the auxiliary loss functions are added proportionally to form a final loss function of the semantic segmentation network.
4. The method for detecting the edge wear of the conveyor belt according to claim 1, wherein the detecting the edge key point of the picture to be detected to obtain the edge key point includes:
And identifying coordinates of the four key points of the picture to be detected by adopting a CPN network so as to obtain edge key points.
5. The method for detecting the edge wear of the conveyor belt according to claim 4, wherein the confidence of the key points of the CPN is 0.5-0.7, the coordinates of the four key points are spatially clustered by k means, and the central point of the clustered cluster is selected as the edge key point.
6. The method according to claim 1, wherein the roller detecting the picture to be detected to obtain an upper edge roller and a lower edge roller comprises:
carrying out target positioning on the rolling shafts in the picture to be detected by adopting a yolov5 target detection model to obtain the image coordinate position of each rolling shaft;
calculating the center point coordinates of each roller according to the image coordinate position of each roller;
the distance of the center point coordinates to the upper edge conveyor belt and the distance of the center point coordinates to the lower edge conveyor belt are calculated to determine the upper edge rollers and the lower edge rollers.
7. The conveyor belt edge wear detection method of claim 6 wherein the determining a roller datum plane from the upper edge roller and the lower edge roller comprises:
Calculating the endpoint coordinates of the upper edge roller and the lower edge roller;
determining an upper datum line and a lower datum line according to the endpoint coordinates;
and determining a rolling shaft datum plane according to the upper datum line and the lower datum line.
8. Conveyer belt edge wear detection device, its characterized in that includes:
the image acquisition unit is used for acquiring the image of the conveyor belt to obtain an image to be detected;
the segmentation unit is used for carrying out binarization semantic segmentation on the picture to be detected so as to obtain a semantic segmentation picture containing a conveyor belt region and a background;
the key point detection unit is used for detecting the edge key points of the picture to be detected so as to obtain the edge key points;
the edge reference plane determining unit is used for determining a parameter equation of a straight line according to the edge key points and determining an edge reference plane of the conveyor belt according to the parameter equation of the straight line;
the roller detection unit is used for detecting the roller of the picture to be detected so as to obtain an upper edge roller and a lower edge roller;
a roller reference surface determining unit for determining a roller reference surface based on the upper edge roller and the lower edge roller;
the degree determining unit is used for determining the abrasion degree of the edge of the conveyor belt according to the roller reference surface, the edge reference surface of the conveyor belt and the area of the conveyor belt; based on the formula To evaluate the extent of belt edge wear, with larger μ representing more severe belt wear; s is(s) Conveyor belt An area that is the conveyor belt area; s is(s) (Edge) Refers to the area of the conveyor belt edge datum plane; s is S Roller shaft The area of the datum plane of the roller is referred to; /> Indicated is an exclusive or operation;
and the alarm processing unit is used for carrying out alarm processing according to the edge abrasion degree of the conveyor belt.
9. A computer device, characterized in that it comprises a memory on which a computer program is stored and a processor which, when executing the computer program, implements the method according to any of claims 1-7.
10. A storage medium storing a computer program which, when executed by a processor, performs the method of any one of claims 1 to 7.
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| CN117333727A (en) * | 2023-12-01 | 2024-01-02 | 山东瀚邦胶带有限公司 | Conveyer belt wearing and tearing area discernment analysis system based on visual image processing technique |
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