CN112288716B - Method, system, terminal and medium for detecting bundling state of steel coil - Google Patents
Method, system, terminal and medium for detecting bundling state of steel coil Download PDFInfo
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 433
- 239000010959 steel Substances 0.000 title claims abstract description 433
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001514 detection method Methods 0.000 claims abstract description 221
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- 238000004891 communication Methods 0.000 claims description 6
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- 230000006870 function Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 239000003086 colorant Substances 0.000 description 3
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- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30136—Metal
Abstract
The invention provides a steel coil bundling state detection method, a system, a terminal and a medium, wherein the method respectively establishes a steel coil target detection model and a steel coil bundling state detection model based on a deep neural network according to a sample image, trains the steel coil target detection model to obtain current image information to be detected, inputs the current image information to be detected into the steel coil target detection model to generate a first detection result, inputs the current image information to be detected into the steel coil bundling state detection model to generate a second detection result, and determines the steel coil bundling state according to the first detection result and the second detection result; the technical problems that the bundling state of the hot rolled wire steel coil still needs to be detected by workers on site, is not timely, accurate and comprehensive, the labor risk of the workers is high and the cost is high are solved, the detection is more timely and accurate, the bundling state detection of the steel coil is realized through a machine, the labor risk of the workers is reduced, and the cost is reduced.
Description
Technical Field
The invention relates to the technical field of image processing in the field of steel, in particular to a steel coil bundling state detection method, a steel coil bundling state detection system, a steel coil bundling state detection terminal and a steel coil bundling state detection medium.
Background
In the production of steel products, hot rolled wire coils are an important product for convenient storage and transportation. The hot rolled wire coil is often banded with a band prior to being transported. Abnormal bundling conditions such as binding band breakage or hot-rolled wire coil binding missing can cause the hot-rolled wire coil to be seriously loosened.
The existing steel coil bundling state often depends on manual identification, and all steel coils with abnormal bundling states cannot be timely, accurately and comprehensively detected, so that the labor risk of workers is high, the risk is high, and the detection cost of the bundling state of the hot rolled wire steel coil is high.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to provide a method, a system, a terminal and a medium for detecting a bundling state of a steel coil, which are used for solving the technical problems that a bundling state of a hot rolled wire steel coil still needs to be detected by a worker on site, and is not timely, accurate and comprehensive, and the labor risk of the worker is high, the risk is high, and the cost is high.
In view of the above problems, the present invention provides a method for detecting a bundling state of a steel coil, comprising:
acquiring a sample image, wherein the sample image comprises at least one of a steel coil and a steel coil binding belt;
respectively establishing a steel coil target detection model and a steel coil binding band target detection model based on a deep neural network according to the sample image, and training;
Acquiring current image information to be detected, inputting the current image information to the steel coil target detection model, and generating a first detection result;
inputting the current image information to be detected into the steel coil binding band target detection model to generate a second detection result;
and determining the bundling state of the steel coil according to the first detection result and the second detection result.
Optionally, before inputting the current image information to be detected to the steel coil binding band target detection model and generating the second detection result, the method further includes:
acquiring range information of a region of interest;
if the target steel coil is determined to be positioned in the region of interest according to the first detection result and the range information of the region of interest, the current image information to be detected is input into the steel coil bandage target detection model.
Optionally, an auxiliary recognition background plate is disposed in the field area corresponding to the region of interest, if the target steel coil is located in the region of interest, the current image to be detected includes the target steel coil and the auxiliary recognition background plate, the target steel coil is located in front of the auxiliary recognition background plate, and the target steel coil is located in the range of the auxiliary recognition background plate.
Optionally, determining an interested rectangular selection frame corresponding to the interested region in the current image to be detected; determining a current steel coil rectangular target frame corresponding to the steel coil in a current image to be detected;
when the target steel coil is positioned in the region of interest, the following conditions are satisfied:
ROI xmin <Hole xmin ；Hole xmax <ROI xmax
ROI ymin <Hole ymin ；Hole ymax <ROI ymax
wherein, ROI xmin ROI for upper left corner coordinates of a rectangular box of interest ymin Upper left-hand ordinate of rectangular selection box of interest, ROI xmax Right lower-hand abscissa of rectangular box of interest, ROI ymax The lower right-hand ordinate of the rectangular selection box of interest, hole xmin The left upper corner coordinate of the rectangular target frame of the current steel coil is Hole ymin The left upper corner ordinate of the rectangular target frame of the current steel coil is Hole xmax Is a rectangular target of the current steel coilLower right horizontal coordinate of frame, hole ymax And the ordinate of the right lower corner of the rectangular target frame of the current steel coil.
Optionally, the method further comprises the steps of,
establishing a steel coil target detection model based on the deep neural network according to the sample image, and training the steel coil target detection model comprises the following steps: labeling the steel coil in the obtained sample image to obtain a steel coil data set, obtaining effective information of the steel coil in the steel coil data set, constructing a steel coil deep learning neural network, and training the steel coil deep learning neural network by combining the effective information of the steel coil to generate a steel coil target detection model;
Establishing a steel coil binding band target detection model based on the deep neural network according to the sample image, and training the model comprises the following steps: labeling the obtained binding bands in the sample image to obtain a binding band data set, obtaining binding band effective information in the binding band data set, constructing a binding band deep learning neural network, training the binding band deep learning neural network by combining the binding band effective information, and generating a steel coil binding band target detection model.
The steel coil in the sample image obtained by optional labeling comprises a position of a steel coil side hole in the sample image through a steel coil rectangular frame selection mark, the steel coil effective information comprises steel coil image basic attributes and steel coil labeling information, the steel coil image basic attributes comprise at least one of steel coil image file names, steel coil widths, steel coil heights and steel coil image depths, and the steel coil labeling information comprises an upper left corner abscissa of the steel coil rectangular frame selection, an upper left corner ordinate of the steel coil rectangular frame selection, a lower right corner abscissa of the steel coil rectangular frame selection and a lower right corner ordinate of the steel coil rectangular frame selection;
labeling the obtained binding band in the sample image comprises marking the position of the binding band in the sample image through a binding band rectangular frame selection, wherein the binding band effective information comprises binding band image basic attributes and binding band labeling information, the binding band image basic attributes comprise at least one of a binding band image file name, a binding band width, a binding band height and a binding band image depth, and the binding band labeling information comprises an upper left corner abscissa of the binding band rectangular frame selection, an upper left corner ordinate of the binding band rectangular frame selection, a lower right corner abscissa of the binding band rectangular frame selection and a lower right corner ordinate of the binding band rectangular frame selection.
Optionally, the determining the bundling state of the steel coil according to the first detection result and the second detection result includes any one of the following:
if the position information of the current steel coil binding band in the second detection result is empty, the steel coil binding state is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, and if the number is less than a preset number threshold, the steel coil binding state is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, acquiring the number of the steel coil binding bands in the second detection result, if the number is not less than a preset number threshold value, acquiring the position information of each steel coil binding band, matching the position information of the steel coil binding bands with the preset position information, and if the matching fails, judging that the binding state of the steel coil is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, if the number is not less than a preset number threshold value, the position information of each steel coil binding band is obtained, the position information of the steel coil binding bands is matched with the preset position information, and if the matching is successful, the steel coil binding state is abnormal.
The invention also provides a steel coil bundling state detection system, which comprises:
the image acquisition module is used for acquiring a sample image, and the sample image comprises at least one of a steel coil and a steel coil binding belt;
the model building training module is used for building a steel coil target detection model and a steel coil binding belt target detection model based on a deep neural network according to the sample image respectively and training the model building training module;
the first generation module is used for acquiring current image information to be detected, inputting the current image information to the steel coil target detection model and generating a first detection result;
the second generation module is used for inputting the current image information to be detected into the steel coil binding band target detection model to generate a second detection result;
and the determining module is used for determining the bundling state of the steel coil according to the first detection result and the second detection result.
The invention also provides a terminal, which comprises a processor, a memory and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute a computer program stored in the memory to implement the method for detecting a bundling state of steel coil according to one or more of the above embodiments.
The present invention also provides a computer-readable storage medium, having stored thereon a computer program,
the computer program is for causing the computer to execute the steel coil bundling state detection method according to any one of the above embodiments.
As described above, the method, the system, the terminal and the medium for detecting the bundling state of the steel coil have the following beneficial effects:
the method comprises the steps of acquiring sample images, respectively establishing a steel coil target detection model and a steel coil binding belt target detection model based on a deep neural network according to the sample images, training, acquiring current image information to be detected, inputting the current image information to be detected into the steel coil target detection model to generate a first detection result, inputting the current image information to be detected into the steel coil binding belt target detection model to generate a second detection result, and determining a steel coil binding state according to the first detection result and the second detection result; the technical problems that the bundling state of the hot rolled wire steel coil still needs to be detected on site by workers, and the detection is not timely, accurate and comprehensive enough, the labor risk of the workers is high, the risk is high, and the cost is high are solved, the detection is more timely and accurate, the steel coil bundling state detection is realized through a machine, the labor risk and the risk of the workers are reduced, and the cost is reduced.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting a bundling state of a steel coil according to an embodiment of the invention;
fig. 2 is a schematic diagram of the relative positions of a lens, a hole on the side surface of a steel coil, and an auxiliary recognition background plate of an image acquisition device according to a first embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for detecting a bundling state of a hot-rolled wire coil according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a current image to be detected according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a steel coil bundling state detection system according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a terminal according to a second embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Example 1
Referring to fig. 1, the method for detecting a bundling state of a steel coil according to the embodiment of the present invention includes:
s101: a sample image is acquired.
The sample image may be collected by a camera or an image collecting device, or may be retrieved from an image or a video stored in another database, which is not limited herein.
Optionally, the sample image includes at least one of a coil of steel and a coil of steel strap, i.e., the sample image includes a plurality of sub-images, which may include only a coil of steel or both a coil of steel and a coil of steel strap.
Optionally, the sample image includes an image of the normal binding of the strap to the coil of steel.
Alternatively, the coil of steel includes, but is not limited to, a coil of hot rolled wire, and the like.
S102: and respectively establishing a steel coil target detection model and a steel coil binding band target detection model based on the deep neural network according to the sample image, and training.
In some embodiments, building a steel coil target detection model based on a deep neural network from the sample image and training comprises: labeling the steel coil in the acquired sample image to obtain a steel coil data set, acquiring effective information of the steel coil in the steel coil data set, constructing a steel coil deep learning neural network, training the steel coil deep learning neural network by combining the effective information of the steel coil, and generating a steel coil target detection model.
Optionally, labeling the steel coil in the acquired sample image to obtain a steel coil data set, and dividing the steel coil data set into a training set, a test set and a verification set according to a certain proportion, for example, according to 8:1:1 and the like. And training the steel coil deep learning neural network by using the data in the training set to generate a steel coil target detection model.
Optionally, the coil effective information in the coil data set includes coil effective information in a training set in the coil data set.
Optionally, labeling the steel coil in the obtained sample image includes labeling the position of the steel coil side hole in the sample image by a steel coil rectangular frame selection, the steel coil effective information includes steel coil image basic attribute and steel coil labeling information, the steel coil image basic attribute includes at least one of a steel coil image file name, a steel coil width, a steel coil height and a steel coil image depth, and the steel coil labeling information includes a steel coil rectangular frame selection Upper left-hand abscissa yHole of frame xmin Left upper-corner ordinate yHole of rectangular steel coil selection frame ymin Right lower corner abscissa yHole of rectangular steel coil frame xmax And the right lower angle ordinate yHole of the rectangular steel coil selection frame ymax 。
Optionally, labeling the steel coil in the acquired sample image to obtain a steel coil data set includes:
and taking the rectangular frame of the image marking tool as a steel coil rectangular frame, marking the positions of holes on the side face of the steel coil in the sample image by using the steel coil rectangular frame, recording the positions of the steel coil rectangular frame, and forming a steel coil data set.
Optionally, the effective information of the steel coil further comprises a class of the target object.
Optionally, training the steel coil deep learning neural network by combining the steel coil effective information, and generating the steel coil target detection model includes:
and obtaining a steel coil target detection model by learning the steel coil target characteristics within the range of the steel coil identification frame in each steel coil training set image.
Alternatively, the coil of strip deep learning neural network includes, but is not limited to, any one of SSD-MobileNet neural network, R-CNN neural network, faster-RCNN neural network, YOLO series neural network, and the like.
In some embodiments, building a steel coil strap target detection model based on a deep neural network from the sample image and training comprises: labeling the binding bands in the acquired sample images to obtain a binding band data set, acquiring binding band effective information in the binding band data set, constructing a binding band deep learning neural network, training the binding band deep learning neural network by combining the binding band effective information, and generating a steel coil binding band target detection model.
Optionally, labeling the binding bands in the acquired sample image to obtain a binding band data set, and dividing the binding band data set into a training set, a test set, a verification set according to a certain proportion, for example, according to 8:1:1 and the like. And training the band deep learning neural network by using the data in the training set to generate a steel coil band target detection model.
Optionally, the steel coil effective information in the strap data set includes strap effective information of a training set of the strap data set.
Optionally, labeling the bands in the acquired sample image includes labeling the positions of the bands in the sample image by a band rectangular selection frame, the band effective information includes band image base attributes including at least one of a band image file name, a band width, a band height, and a band image depth, and band labeling information including an upper left corner abscissa yBand of the band rectangular selection frame xmin Left upper-corner ordinate yBand of rectangular binding band selection frame ymin Right lower corner abscissa yBand of rectangular binding band selection frame xmax And the right lower-corner ordinate yBand of the rectangular selection frame of the binding band ymax 。
Optionally, labeling the bands in the acquired sample image to obtain a band dataset includes:
and marking the position of the binding band in the sample image by using the rectangular frame of the image marking tool as the rectangular frame of the binding band, recording the position of the rectangular frame of the binding band, and forming a binding band data set.
Optionally, the effective information of the binding band further comprises a class of the target object.
Optionally, training the band deep learning neural network by combining the band effective information, and generating the steel coil band target detection model includes:
and obtaining a steel coil binding band target detection model by learning binding band target characteristics in the binding band identification frame range in each binding band training set image.
Alternatively, the strap deep learning neural network includes, but is not limited to, any one of SSD-MobileNet neural network, R-CNN neural network, faster-RCNN neural network, YOLO series neural network, and the like.
Optionally, if the sample image includes a steel coil and a binding belt, the standard of the steel coil and the binding belt can be performed separately in two times during marking, and the steel coil and the binding belt can be marked in the same marking process, which is not limited herein.
S103: and acquiring current image information to be detected, inputting the current image information to a steel coil target detection model, and generating a first detection result.
It should be noted that, the current image information to be detected may be obtained by monitoring the image in real time. At the moment, the steel coil bundling state is monitored according to the image information monitored in real time, and the bundling state of the target steel coil passing through the monitoring equipment can be obtained in real time. If the current image information to be detected is provided by a real-time image, the steel coil bundling state monitoring method provided by the embodiment can realize real-time monitoring.
Optionally, the first detection result includes, but is not limited to, whether the image to be detected includes the target steel coil, and if the image to be detected includes the target steel coil, a detection result further includes position information of a rectangular target frame of the current steel coil corresponding to the target steel coil.
In some embodiments, if the first detection result is null, that is, the target steel coil is not detected by the steel coil target detection model in the current image information to be detected, the detection of the bundling state of the target steel coil is finished. Optionally, if the current image to be detected is a real-time image, the target steel coil may not move to the optimal recognition position at this time, and new current image information to be detected may be acquired again after a preset time interval, and the bundling state detection of the target steel coil is performed again. Optionally, the preset time may be determined according to a motion state of the target steel coil.
In some embodiments, the first detection result includes position information of all the holes on the side surface of the target steel coil in the current image to be detected.
Optionally, the format and content of the first detection result include:
[Hole xmin ,Hole ymin ,Hole xmax ,Hole ymax ],
the four coordinates are the coordinates of the upper left, lower right and upper right points of the rectangular target frame of the current steel coil corresponding to the side holes of the target steel coil respectively. Wherein, hole xmin ,Hole ymin Respectively the horizontal and vertical coordinates of the upper left corner point; hole xmax ,Hole ymax Respectively the horizontal and vertical coordinates of the lower right corner point.
It should be noted that, the first detection result may include one or more target steel coil side holes, and if the first detection result includes a plurality of target steel coil side holes, the format and content of the first detection result are as follows:
if the target steel coil is not detected in the current image information to be detected by the steel coil target detection model, the first detection result is null and can be identified as [ 0,0 ], and the identification method can be other modes, which is not limited herein.
In some embodiments, after the first detection result is generated, inputting the current image information to be detected into the steel coil strap target detection model, and before the second detection result is generated, further including:
acquiring range information of a region of interest;
if the target steel coil is determined to be positioned in the region of interest according to the first detection result and the range information of the region of interest, the current image information to be detected is input into a steel coil binding band target detection model.
Optionally, determining an interested rectangular selection frame corresponding to the interested region in the current image to be detected; determining a rectangular target frame of the current steel coil corresponding to the steel coil in the current image to be detected, wherein the steel coil is positioned in the region of interest and meets the following conditions:
ROI xmin <Hole xmin ；Hole xmax <ROI xmax
ROI ymin <Hole ymin ；Hole ymax <ROI ymax
Wherein, ROI xmin ROI for upper left corner coordinates of a rectangular box of interest ymin Upper left-hand ordinate of rectangular selection box of interest, ROI xmax Right lower-hand abscissa of rectangular box of interest, ROI ymax The lower right-hand ordinate of the rectangular selection box of interest, hole xmin The left upper corner coordinate of the rectangular target frame of the current steel coil is Hole ymin The left upper corner ordinate of the rectangular target frame of the current steel coil is Hole xmax The right lower corner abscissa of the rectangular target frame of the current steel coil is Hole ymax And the ordinate of the right lower corner of the rectangular target frame of the current steel coil.
If the current rectangular target frame of the steel coil does not meet the conditions, the target steel coil is positioned outside the interested area and does not reach the optimal identification position, and the target steel coil still needs to wait to move to a proper position.
Optionally, an auxiliary recognition background plate is arranged in the field area corresponding to the region of interest, if the target steel coil is located in the region of interest, the current image to be detected comprises the target steel coil and the auxiliary recognition background plate, the target steel coil is located in front of the auxiliary recognition background plate, and the target steel coil is located in the range of the auxiliary recognition background plate.
Optionally, the secondary identification background panel comprises a solid color background panel. The detection effect, such as blue, red or yellow, can be enhanced by providing a solid background plate in the field area. Such colors are typically of high contrast to the factory production environment, which may enhance recognition; in addition, the auxiliary recognition background plate has a certain shielding effect, so that the influence of complex industrial scenes in the background on the training process of the steel coil target detection model and the steel coil binding belt target detection model can be avoided, and the consistency of targets is enhanced.
Optionally, referring to fig. 2, in the process of collecting the sample image and the current image to be detected, the view angle of the image collecting device is perpendicular to the auxiliary recognition background plate, and the lens 1 of the image collecting device, the center of the hole 2 on the side face of the steel coil and the center of the auxiliary recognition background plate 3 are located on the same straight line. Of course, the lens of the image acquisition device, the center of the hole on the side face of the steel coil and the center of the auxiliary recognition background plate are not located on the same straight line, and a certain angle of inclination exists.
Optionally, if the detection effect is more when the round side of the hot rolled wire coil faces the camera, the region of the auxiliary recognition background plate in the image imaging range acquired by the image acquisition device is set as the region of interest (Regionof Interest, abbreviated as ROI). In the actual industrial production scene, the round side holes of the hot rolled wire steel coil are opposite to the lens of the image acquisition equipment, and the steel coil target detection model also realizes target detection by identifying the steel coil side holes.
Optionally, the shape of the hole on the side surface of the hot rolled wire steel coil is close to a perfect circle, the target is obvious under the setting off of the solid background plate, the hole on the side surface of the hot rolled wire steel coil is used as the recognition object of the steel coil target recognition model, and the steel coil can be judged to be recognized when the hole is recognized.
Optionally, the hole on the side of the steel coil may be a hole edge near the camera of the image capturing device, or may be a hole edge far from the camera of the image capturing device, which is not limited herein.
S104: and inputting the current image information to be detected into a steel coil binding band target detection model to generate a second detection result.
Optionally, the format and content of the second detection result include:
the four coordinates correspond to the coordinates of the upper left, lower right and upper right points of the rectangular target frame of the current binding band respectively. Wherein, band xmin ,Band ymin Respectively the horizontal and vertical coordinates of the upper left corner point; band (Band) xmax ,Band ymax Respectively the horizontal and vertical coordinates of the lower right corner point.
It should be noted that the format of the second detection result is only one exemplary format, and the format of the second detection result may be flexibly set according to the number of bands.
It should be noted that, a coil of steel may be bound by a plurality of bands, and the second detection result may include position information of the plurality of bands.
Optionally, the second detection result includes location information of at least one binding band, if the steel coil target detection model does not detect the binding band in the current image information to be detected, the second detection result is empty, and may be identified as [ 0,0 ], and the identification method may also be other manners, which is not limited herein.
S105: and determining the bundling state of the steel coil according to the first detection result and the second detection result.
Optionally, determining the bundling state of the steel coil according to the first detection result and the second detection result includes any one of the following:
if the position information of the current steel coil binding band in the second detection result is empty, the steel coil binding state is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, and if the number is less than a preset number threshold value, the steel coil binding state is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, acquiring the number of the steel coil binding bands in the second detection result, if the number is not less than a preset number threshold value, acquiring the position information of each steel coil binding band, matching the position information of the steel coil binding bands with the preset position information, and if the matching fails, judging that the binding state of the steel coil is abnormal;
If the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, if the number is not less than a preset number threshold value, the position information of each steel coil binding band is obtained, the position information of the steel coil binding bands is matched with the preset position information, and if the matching is successful, the steel coil binding state is normal.
Optionally, the method for detecting the bundling state of the steel coil further comprises the following steps:
and if the bundling state of the steel coil is normal, outputting the position information of each binding band and the normal bundling state information of the hot rolled wire steel coil.
Optionally, the method for detecting the bundling state of the steel coil further comprises the following steps:
if the bundling state of the steel coil is abnormal, an abnormal prompt is sent out.
Wherein, abnormal prompt can be sent out through modes such as information, voice, warning light.
Alternatively, the preset number threshold and the preset position information may be set by those skilled in the art according to needs, and specific values are not limited herein. The preset position information can be set into a plurality of position ranges according to the requirement, if the position information of a certain binding band meets one of the position ranges, the position information of the binding band is successfully matched, and if the position information of each binding band is successfully matched, or if the position information of the binding bands exceeding a certain number is successfully matched, the binding state of the steel coil is normal.
Optionally, if the current hot rolled wire coil is located in the region of interest, calling a trained coil binding band target detection model: outputting the position information of each binding band target and the normal binding state information of the hot-rolled wire steel coil if the hot-rolled wire steel coil binding band exists in the current image to be detected, and outputting the abnormal binding state information of the hot-rolled wire steel coil if the hot-rolled wire steel coil binding band is not detected in the image.
Optionally, if the hot rolled wire coil does not enter or has left the region of interest, waiting until the hot rolled wire coil enters the region of interest, and inputting the current image information to be detected into the coil band target detection model.
The invention provides a steel coil bundling state detection method, which comprises the steps of respectively establishing a steel coil target detection model and a steel coil bundling state detection model based on a deep neural network according to a sample image, training the steel coil target detection model to obtain current image information to be detected, inputting the current image information to be detected into the steel coil target detection model to generate a first detection result, inputting the current image information to be detected into the steel coil bundling state detection model to generate a second detection result, and determining the steel coil bundling state according to the first detection result and the second detection result; the technical problems that the bundling state of the hot rolled wire steel coil still needs to be detected on site by workers, and the detection is not timely, accurate and comprehensive enough, the labor risk of the workers is high, the risk is high, and the cost is high are solved, the detection is more timely and accurate, the steel coil bundling state detection is realized through a machine, the labor risk and the risk of the workers are reduced, and the cost is reduced.
Optionally, when there are many production lines to need carry out coil of strip bundling state detection, can realize the detection of coil of strip bundling state through setting up the device that distributes and can realize this embodiment coil of strip bundling state detection at each production line, the suitability is strong, detects more comprehensively in time.
The method for detecting the bundling state of the steel coil according to the present embodiment is described below by way of example with reference to fig. 3, and the method for detecting the bundling state of the hot-rolled wire steel coil includes:
s301: and setting an auxiliary recognition background plate in a specified industrial scene, and acquiring images of a plurality of hot rolled wire steel coils bundled with binding bands by using an industrial camera as sample images.
Optionally, the auxiliary recognition background board comprises a solid color background board, and the solid color background board is arranged on site to enhance the detection effect, such as blue, red or yellow and other vivid colors. Such colors generally have higher contrast with the factory production environment, enhancing the recognition effect of the algorithm; the auxiliary recognition background plate has a certain shielding effect, can avoid the influence of complex industrial scenes in the background on the training process of the steel coil target detection model and the steel coil binding belt target detection model, and enhances the consistency of targets.
S302: and marking the steel coil and the binding belt in the sample image respectively, and manufacturing a hot rolled wire steel coil data set and a hot rolled wire steel coil binding belt data set.
Optionally, image marking is carried out on a sample image shot in a specific industrial scene, a rectangular selection frame of an image marking tool is used as a rectangular selection frame of the steel coil, the position of a round side hole of the hot rolled wire steel coil in the image is marked, the position information of the rectangular selection frame of the steel coil is recorded, a hot rolled wire steel coil data set is manufactured, and the hot rolled wire steel coil data set is divided into three parts: training set, test set and verification set, and training hot rolled wire steel coil target detection model with the data of the training set.
The effective information of the hot rolled wire steel coil training set after image marking, which can be used for training, comprises steel coil image basic attribute and steel coil marking information. The basic attributes of the steel coil picture are as follows: filename-coil file name, width-coil width, height-coil height, depth-coil image depth. The steel coil labeling information comprises: xmin, ymin, xmax and ymax respectively represent the left upper-corner abscissa, the left upper-corner ordinate, the right lower-corner abscissa and the right lower-corner ordinate of the steel coil rectangular selection frame corresponding to the round side hole of the hot rolled wire steel coil in the sample image; class, class of target object.
Image marking is carried out on a sample image shot under a specific industrial scene, a rectangular selection frame of an image marking tool is used as a rectangular selection frame of a binding band, positions of all binding bands in the sample image are marked, position information of the rectangular selection frame of the binding band is recorded, a hot rolled wire steel coil binding band data set is manufactured, and the hot rolled wire steel coil binding band data set is divided into three parts: training set, test set and verification set, and training the hot rolled wire steel coil binding band target detection model by using the data of the training set.
The effective information of the hot rolled wire steel coil binding band training set after image marking, which can be used for training, comprises binding band image basic attribute and binding band marking information. The basic attributes of the binding band picture are as follows: filename-strap file name, width-strap width, height-strap height, depth-strap image depth. The bandage marking information comprises: xmin, ymin, xmax, ymax respectively represent the left upper-corner abscissa, the left upper-corner ordinate, the right lower-corner abscissa and the right lower-corner ordinate of each rectangular binding band selection frame of the hot rolled wire steel coil in the sample image; class, class of target object.
S303: and building a neural network, and respectively training by using the hot-rolled wire steel coil data set and the hot-rolled wire steel coil binding belt to obtain a hot-rolled wire steel coil target detection model and a hot-rolled wire steel coil binding belt target detection model.
And finally obtaining the hot-rolled wire steel coil target detection model by learning target characteristics within the rectangular steel coil frame selection range in each hot-rolled wire steel coil training set image. And finally obtaining the hot rolled wire steel coil binding band target detection model by learning target characteristics within the binding band rectangular frame selection range in each hot rolled wire steel coil binding band training set image. The hot rolled wire steel coil training set image is a sample image marked with steel coils, and the hot rolled wire steel coil binding band training set image is a sample image marked with binding bands.
Alternatively, the neural network includes SSD-MobileNet neural network, R-CNN neural network, faster-RCNN neural network, YOLO series neural network, and other target recognition type neural networks.
S304: and acquiring current image information to be detected, inputting the current image information to a hot rolled wire steel coil target detection model, and generating a first detection result.
The first detection result comprises the position information of all hot rolled wire steel coils in the current image to be detected.
The format and content of the first detection result comprise:
[Hole xmin ,Hole ymin ,Hole xmax ,Hole ymax ],
the four coordinates in the list correspond to the upper left, lower right and upper right points of the rectangular target frame of the current steel coil respectively. Hole xmin ,Hole ymin Respectively the horizontal and vertical coordinates of the upper left corner point; hole xmax ,Hole ymax Respectively the horizontal and vertical coordinates of the lower right corner point.
Referring to fig. 4, fig. 4 is a schematic diagram of a current image to be detected, which includes a current rectangular target frame a of a steel coil and four rectangular target frames B of current binding bands.
S305: and comparing the position information of the rectangular target frame of the current steel coil in the first detection result with the region of interest, and judging whether the hot rolled wire steel coil is positioned in the region of interest.
Alternatively, it may be determined whether the current hot rolled wire coil is located within a range suitable for detection by comparing the position information of the round side hole of the hot rolled wire coil (the position information of the current coil rectangular target frame) with the range of the region of interest. Optionally, the range of the region of interest in the current image to be detected is determined by coordinates of an upper left corner and a lower right corner of the rectangular selection frame of interest of the auxiliary recognition background plate in the current image to be detected, and the content and the format of the position information comprise:
[ROI ymin ,ROI xmin ,ROI ymax ,ROI xmax ]
the conditions for judging whether the hot rolled wire coil is located in the region of interest at this time are:
ROI xmin <Hole xmin ；Hole xmax <ROI xmax
ROI ymin <Hole ymin ；Hole ymax <ROI ymax
wherein, ROI xmin ROI for upper left corner coordinates of a rectangular box of interest ymin Upper left-hand ordinate of rectangular selection box of interest, ROI xmax Right lower-hand abscissa of rectangular box of interest, ROI ymax The lower right-hand ordinate of the rectangular selection box of interest, hole xmin The left upper corner coordinate of the rectangular target frame of the current steel coil is Hole ymin The left upper corner ordinate of the rectangular target frame of the current steel coil is Hole xmax The right lower corner abscissa of the rectangular target frame of the current steel coil is Hole ymax And the ordinate of the right lower corner of the rectangular target frame of the current steel coil.
If the conditions are met, the hot-rolled wire steel coil is positioned in the region of interest, and a hot-rolled wire steel coil binding band target detection model can be called to carry out the next identification; if any condition is not satisfied, the hot rolled wire coil is located outside the region of interest, and the optimal identification position is not reached, and the hot rolled wire coil needs to wait to move to a proper position.
S306: and inputting the current image information to be detected into a hot-rolled wire steel coil binding band target detection model to generate a second detection result.
Optionally, when the hot-rolled wire steel coil is in the region of interest, calling a hot-rolled wire steel coil binding band target detection model, inputting the current image information to be detected into the hot-rolled wire steel coil binding band target detection model, and generating a second detection result.
Optionally, if the second detection result includes the position information of at least one hot rolled wire coil binding band, outputting the detected position information of the hot rolled wire coil binding band together with the normal binding state information of the hot rolled wire coil.
Optionally, if the second detection result is empty, that is, if the binding band of the hot rolled wire coil is not detected, outputting the abnormal information of the binding state of the hot rolled wire coil.
Optionally, the second detection result includes position information of all hot rolled wire coil binding bands in the current image to be detected. The format and content of the second detection result output position information comprise:
the four coordinates in the list correspond to the upper left, lower right, and upper right points of the current rectangular target frame of the strap, respectively. Band (Band) xmin ,Band ymin Respectively the horizontal and vertical coordinates of the upper left corner point; band (Band) xmax ,Band ymax Respectively the horizontal and vertical coordinates of the lower right corner point.
Optionally, when the hot rolled wire steel coil is located in the region of interest, calling a trained hot rolled wire steel coil binding band target detection model: outputting the position information of each binding band and the normal binding state information of the hot-rolled wire steel coil if the hot-rolled wire steel coil binding bands exist in the current image to be detected, and outputting the abnormal binding state information of the hot-rolled wire steel coil if the hot-rolled wire steel coil binding bands are not detected in the image of the current image to be detected. If the hot rolled wire coil has not entered or has left the region of interest, waiting until there is a hot rolled wire coil entering the region of interest.
Example two
Referring to fig. 5, a steel coil bundling state detection system 500 includes:
an image acquisition module 501 for acquiring a sample image, wherein the sample image comprises at least one of a steel coil and a steel coil binding belt;
the model building training module 502 is configured to build a steel coil target detection model and a steel coil binding band target detection model based on a deep neural network according to the sample image, and perform training;
a first generating module 503, configured to obtain current image information to be detected, and input the current image information to a steel coil target detection model to generate a first detection result;
the second generating module 504 is configured to input the current image information to be detected to a steel coil strap target detection model, and generate a second detection result;
the determining module 505 is configured to determine a bundling state of the steel coil according to the first detection result and the second detection result.
In this embodiment, the visual servo-based long nozzle replacement apparatus is essentially provided with a plurality of modules for executing the method for detecting the bundling state of the steel coil in the above embodiment, and specific functions and technical effects are only required by referring to the above embodiment, and are not repeated herein.
Referring to fig. 6, the embodiment of the present invention further provides a terminal 600 including a processor 601, a memory 602, and a communication bus 603;
A communication bus 603 for connecting the processor 601 and the memory 602;
the processor 601 is configured to execute a computer program stored in the memory 602 to implement the steel coil bundling state detection and replacement method according to one or more of the above embodiments.
An embodiment of the application also provides a computer-readable storage medium, characterized in that it has stored thereon a computer program,
the computer program is for causing a computer to execute the steel coil bundling state detection method according to any one of the above embodiments.
The embodiment of the application also provides a non-volatile readable storage medium, in which one or more modules (programs) are stored, where the one or more modules are applied to a device, and the device may be caused to execute instructions (instructions) of a step included in the embodiment one of the embodiment of the application.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products 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.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (9)
1. The steel coil bundling state detection method is characterized by comprising the following steps of:
acquiring a sample image, wherein the sample image comprises at least one of a steel coil and a steel coil binding belt;
respectively establishing a steel coil target detection model and a steel coil binding band target detection model based on a deep neural network according to the sample image, and training;
acquiring current image information to be detected, inputting the current image information to the steel coil target detection model, and generating a first detection result, wherein the first detection result comprises whether the current image to be detected comprises a target steel coil or not, and if the current image to be detected comprises the target steel coil, the first detection result also comprises position information of a rectangular target frame of the current steel coil corresponding to the target steel coil;
Acquiring range information of a region of interest;
if the target steel coil is determined to be positioned in the region of interest according to the position information of the rectangular target frame of the current steel coil and the range information of the region of interest, which correspond to the target steel coil, inputting the current image information to be detected into the steel coil binding band target detection model, and generating a second detection result;
if the target steel coil is located outside the region of interest according to the position information of the rectangular target frame of the current steel coil and the range information of the region of interest, and if the current image to be detected is a real-time image, acquiring new current image information to be detected again after a preset time interval, detecting the bundling state of the target steel coil again, and inputting the newly acquired current image information to be detected into a steel coil binding band target detection model until the steel coil enters the region of interest to obtain a second detection result, wherein the preset time is determined according to the motion state of the target steel coil;
and determining the bundling state of the steel coil according to the first detection result and the second detection result.
2. The method for detecting a bundling state of a steel coil according to claim 1, wherein an auxiliary recognition background plate is provided in a field area corresponding to the region of interest, and if the target steel coil is located in the region of interest, the current image to be detected includes the target steel coil and the auxiliary recognition background plate, the target steel coil is located in front of the auxiliary recognition background plate, and the target steel coil is located in a range of the auxiliary recognition background plate.
3. The steel coil bundling state detection method according to claim 1, further comprising determining an interested rectangular frame corresponding to the interested region in a current image to be detected; determining a current steel coil rectangular target frame corresponding to the target steel coil in a current image to be detected;
when the target steel coil is positioned in the region of interest, the following conditions are satisfied:
ROIx min <Hole xmin ；Hole xmax <ROI xmax
ROI ymin <Hole ymin ；Hole ymax <ROI ymax
wherein, ROI xmin ROI for upper left corner coordinates of a rectangular box of interest ymin Upper left-hand ordinate of rectangular selection box of interest, ROI xmax Right lower-hand abscissa of rectangular box of interest, ROI ymax The lower right-hand ordinate of the rectangular selection box of interest, hole xmin The left upper corner coordinate of the rectangular target frame of the current steel coil is Hole ymin The left upper corner ordinate of the rectangular target frame of the current steel coil is Hole xmax The right lower corner abscissa of the rectangular target frame of the current steel coil is Hole ymax And the ordinate of the right lower corner of the rectangular target frame of the current steel coil.
4. The steel coil bundling state detection method according to any one of claims 1-3, further comprising, establishing a steel coil target detection model based on a deep neural network from the sample image, and training comprising: labeling the steel coil in the obtained sample image to obtain a steel coil data set, obtaining effective information of the steel coil in the steel coil data set, constructing a steel coil deep learning neural network, and training the steel coil deep learning neural network by combining the effective information of the steel coil to generate a steel coil target detection model;
Establishing a steel coil binding band target detection model based on the deep neural network according to the sample image, and training the model comprises the following steps: labeling the obtained binding bands in the sample image to obtain a binding band data set, obtaining binding band effective information in the binding band data set, constructing a binding band deep learning neural network, training the binding band deep learning neural network by combining the binding band effective information, and generating a steel coil binding band target detection model.
5. The method for detecting the bundling state of steel coils according to claim 4, wherein,
marking the position of a steel coil side hole in the obtained sample image through a steel coil rectangular frame selection mark, wherein the steel coil effective information comprises steel coil image basic attributes and steel coil marking information, the steel coil image basic attributes comprise at least one of a steel coil image file name, a steel coil width, a steel coil height and a steel coil image depth, and the steel coil marking information comprises an upper left corner abscissa of the steel coil rectangular frame selection, an upper left corner ordinate of the steel coil rectangular frame selection, a lower right corner abscissa of the steel coil rectangular frame selection and a lower right corner ordinate of the steel coil rectangular frame selection;
Labeling the obtained binding band in the sample image comprises marking the position of the binding band in the sample image through a binding band rectangular frame selection, wherein the binding band effective information comprises binding band image basic attributes and binding band labeling information, the binding band image basic attributes comprise at least one of a binding band image file name, a binding band width, a binding band height and a binding band image depth, and the binding band labeling information comprises an upper left corner abscissa of the binding band rectangular frame selection, an upper left corner ordinate of the binding band rectangular frame selection, a lower right corner abscissa of the binding band rectangular frame selection and a lower right corner ordinate of the binding band rectangular frame selection.
6. A method of detecting a bundling state of a steel coil according to any one of claims 1 to 3, wherein determining the bundling state of the steel coil based on the first detection result and the second detection result comprises any one of:
if the position information of the current steel coil binding band in the second detection result is empty, the steel coil binding state is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, and if the number is less than a preset number threshold, the steel coil binding state is abnormal;
If the position information of the current steel coil binding bands in the second detection result is not empty, acquiring the number of the steel coil binding bands in the second detection result, if the number is not less than a preset number threshold value, acquiring the position information of each steel coil binding band, matching the position information of the steel coil binding bands with the preset position information, and if the matching fails, judging that the binding state of the steel coil is abnormal;
if the position information of the current steel coil binding bands in the second detection result is not empty, the number of the steel coil binding bands in the second detection result is obtained, if the number is not less than a preset number threshold value, the position information of each steel coil binding band is obtained, the position information of the steel coil binding bands is matched with the preset position information, and if the matching is successful, the steel coil binding state is abnormal.
7. A steel coil bundling state detection system, comprising:
the image acquisition module is used for acquiring a sample image, and the sample image comprises at least one of a steel coil and a steel coil binding belt;
the model building training module is used for building a steel coil target detection model and a steel coil binding belt target detection model based on a deep neural network according to the sample image respectively and training the model building training module;
The first generation module is used for acquiring current image information to be detected, inputting the current image information to the steel coil target detection model and generating a first detection result, wherein the first detection result comprises whether the current image to be detected comprises a target steel coil or not, and if the current image to be detected comprises the target steel coil, the first detection result also comprises the position information of a current steel coil rectangular target frame corresponding to the target steel coil;
the second generation module is used for acquiring range information of an interested region, if the target steel coil is determined to be positioned in the interested region according to the position information of the rectangular target frame of the current steel coil and the range information of the interested region, inputting the current image information to be detected into the steel coil binding band target detection model to generate a second detection result, and if the target steel coil is determined to be positioned outside the interested region according to the position information of the rectangular target frame of the current steel coil and the range information of the interested region, and if the current image to be detected is a real-time image, acquiring new current image information to be detected again after a preset time interval, detecting the binding state of the target steel coil again, and inputting the newly acquired current image information to be detected into the steel coil binding band target detection model after the steel coil enters the interested region to obtain the second detection result, wherein the preset time is determined according to the motion state of the target;
And the determining module is used for determining the bundling state of the steel coil according to the first detection result and the second detection result.
8. A terminal comprising a processor, a memory and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute a computer program stored in the memory to implement the steel coil bundling state detection method according to one or more of claims 1-6.
9. A computer-readable storage medium, having a computer program stored thereon,
the computer program is for causing the computer to execute the steel coil bundling state detection method according to any one of claims 1-6.
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