CN213241250U - Miner safety helmet detection system - Google Patents
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Abstract
The application relates to a miner's safety helmet detecting system, include: the system comprises a miner image acquisition device, a data storage device, a feature extraction device, a classifier, a category correction device and a safety helmet detection result display device which are mutually connected through a data transmission line; the miner image acquisition device is connected with the data storage device through a wired link or a wireless link, the output end of the data storage device is connected with the input end of the feature extraction device, the output end of the feature extraction device is connected with the input end of the classifier and the input end of the category correction device, and the output end of the classification and category correction device is connected with the input end of the safety helmet detection result display device. The method and the device can reduce the difficulty of dividing the target of the person, thereby being beneficial to the application of the technologies such as person detection, identification, positioning and tracking and the like.
Description
Technical Field
The application relates to the technical field of image segmentation, in particular to a miner safety helmet detection system.
Background
Mine personnel safety helmet segmentation is to independently separate safety helmet pixel areas in personnel images by using a method. The safety helmet segmentation is one of key technologies for realizing intelligent video monitoring of coal mine personnel, is a core content of computer vision in mine intelligent monitoring application, can promote application of related technologies such as mine personnel scheduling management, target detection and identification and position information prediction thereof based on the computer vision, and can improve the management and control efficiency of personnel operation areas.
There are many image segmentation methods, such as threshold method based on image gray feature, pixel point region growing method, edge detection method, graph segmentation method based on graph theory, deep learning neural network method, etc., but these methods have the following problems: the key of the threshold method lies in the reasonable selection of the gray threshold, which is suitable for processing the image with clear gray boundary between the target and the background; the region growing method has ideal image segmentation effect for lacking prior information, but is easy to cause excessive segmentation; the edge detection method is easy to have discontinuous boundary contour lines and poor in image region structure; the graph segmentation method requires a user to specify a target and a background in the image segmentation process, and is not suitable for automatic segmentation; the deep neural network segmentation method requires a large amount of input data, a long processing time and high requirements on computer hardware. The results in mine video image processing are difficult to meet with practical requirements.
SUMMERY OF THE UTILITY MODEL
In order to solve the above-mentioned problems in the background art, the present application provides a miner's safety helmet detection system, including:
the system comprises a miner image acquisition device, a data storage device, a feature extraction device, a classifier, a category correction device and a safety helmet detection result display device which are mutually connected through a data transmission line;
the miner image acquisition device is connected with the data storage device through a wired link or a wireless link, the output end of the data storage device is connected with the input end of the feature extraction device, the output end of the feature extraction device is connected with the input end of the classifier and the input end of the category correction device, and the output end of the classification and category correction device is connected with the input end of the safety helmet detection result display device.
Preferably, the miner image acquisition device comprises a camera and a light supplement lamp; the camera is an industrial explosion-proof color camera or a multispectral camera, and the light supplement lamp comprises a plurality of groups of diffuse reflection LED lamp groups.
Preferably, an explosion-proof housing is arranged outside the camera, an explosion-proof membrane is mounted on the explosion-proof housing, and a lens of the camera is aligned with the explosion-proof membrane in parallel.
Preferably, the data storage device is a local server or a cloud server.
Preferably, the output end of the classifier and the output end of the category correction device are connected with a ground information terminal through a mobile internet.
Preferably, the safety helmet detection result display device is connected with an audible and visual alarm, a voice alarm and a spliced screen display terminal.
In the detection system for the miner safety helmet, a miner image is collected through a miner image collection device, the characteristic extraction device granulizes the miner image into a plurality of superpixel blocks and extracts characteristics, a classifier and a category correction device classify the plurality of superpixel blocks, category correction is carried out on the classified superpixel blocks, and finally the corrected superpixel blocks are adopted to divide a target image, so that the defects that the collected personnel video image has uneven illumination, distorted color information, random shadow distribution, difficulty in distinguishing a target boundary from a background boundary and the like due to the influence of various severe conditions such as dust interference, high noise, poor illumination conditions and the like can be avoided, the difficulty in dividing the personnel target is reduced, and the application in the technologies such as personnel detection, identification, positioning, tracking and the like is facilitated.
Drawings
FIG. 1 shows a block schematic diagram of a miner's helmet detection system of an embodiment of the present application;
FIG. 2 shows a schematic flow diagram of a mine personnel helmet segmentation method of an embodiment of the present application;
fig. 3 shows a block schematic diagram of a mine personnel safety cap segmentation apparatus of an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The underground coal mine environment is special, and is influenced by various severe conditions such as dust interference, large noise, poor illumination conditions and the like, so that the collected personnel video images have the defects of uneven illumination, distorted color information, random shadow distribution, difficulty in distinguishing the target from the background boundary and the like, the personnel target segmentation difficulty is directly increased, and the application of the technologies such as personnel detection, identification, positioning and tracking is not facilitated.
The safety helmet is important safety protection equipment which must be worn by mine workers, is a necessary condition for guaranteeing the safety operation of the workers, and represents the existence of the workers. The safety helmet segmentation can promote the application of related technologies such as mine personnel scheduling management, target detection and identification and position information prediction thereof based on computer vision, can improve the management and control efficiency of the personnel working area, and can also effectively reduce the complexity of the whole body of the segmented personnel and the data processing capacity of a compression algorithm to personnel images.
Therefore, the application provides a miner safety helmet detection system.
Fig. 1 shows a block schematic diagram of a miner's helmet detection system of an embodiment of the present application. As shown in fig. 1, the miner's helmet inspection system includes a miner image collecting device 110, a data storage device 120, a feature extracting device 130, a classifier and category correcting device 140, and a helmet inspection result display device 150, which are connected to each other through a data transmission line 160.
The image acquisition device 110 of the miner is connected with the data storage device 120 through a wired link or a wireless link, the output end of the data storage device 120 is connected with the input end of the feature extraction device 130, the output end of the feature extraction device 130 is connected with the input end of the classifier and category correction device 140, and the output end of the classification and category correction device 140 is connected with the input end of the safety helmet detection result display device 150.
In some embodiments, the miner image acquisition device comprises a camera and a light supplement lamp, the camera is an industrial explosion-proof color camera or a multispectral camera, and the light supplement lamp comprises a plurality of groups of diffuse reflection LED lamp groups. In a specific example, the camera may be an explosion-proof camera, which can support a maximum resolution of 1920 × 1082 and includes a CMOS image sensor or a CCD image sensor therein, and support a temperature range of: -40-60 ℃ and humidity less than 95% RH. Furthermore, in order to increase the light emitting angle of the light supplement lamp, multiple groups of light supplement lamps can be uniformly arranged in a hemispherical manner.
In some embodiments, an explosion proof housing is provided on the exterior of the camera, with an explosion proof membrane mounted on the explosion proof housing, and the lens of the camera is aligned parallel to the explosion proof membrane.
In some embodiments, the data storage 120 is a local server or a cloud server.
In some embodiments, the output end of the classifier and class correcting device 140 is connected to a ground information terminal through a mobile internet.
In some embodiments, the helmet detection result display device 150 is connected with an audible and visual alarm, a voice alarm and a spliced screen display terminal.
In some embodiments, a support vector machine
May be implemented by the classifier and class modification apparatus 140 of fig. 1, or implemented as the classifier and class modification apparatus 140 of fig. 1.
During training, the collected images with the safety helmet can be divided into sample images and target images, and the sample images are used for training the support vector machine
Support vector machine with training completed
For segmenting the target image.
Firstly, carrying out pixel-level labeling on a safety helmet region in a sample image, and simultaneously recording the positions of pixel points of the safety helmet region in a labeled image
Inputting different signals within a certain range
Value of sample image
Performing superpixel granulation, and extracting the pixel point position of each superpixel in the sample image
And is and
intersection finding operation
Will be
In (A) belong to
All pixel points of the safety helmet super pixel block are used as the safety helmet super pixel block
In the middle do not belong to
All pixel points of (2) are used as background superpixel blocks.
Then, respectively extracting color characteristic vectors and texture characteristic vectors of the helmet super-pixel block and the background super-pixel block in the sample image, combining the color characteristic vectors and the texture characteristic vectors as characteristic variables, and adopting the characteristic variables to support a vector machine
And (5) training.
In some embodiments, the actual characteristics of the image of the downhole personnel and the color characteristic requirements of the task of segmenting the safety helmet are combined to select
、
、
、
Four color models describe color characteristics. Therefore, the characteristic components of the super-pixel of the target image under the four color models can form a 12-dimensional color characteristicA eigenvector, which can be calculated using the following equation:
in the formula (I), the compound is shown in the specification,
in order to be a color feature vector,
、
、
、
、
、
、
、
、
、
、
、
are feature components on the four color models.
In some embodiments, due to the fact that the pixel regions of the safety helmet of the underground personnel, the skin of the personnel, the work clothes, the environment background and the like have obvious texture differences, the method for distinguishing the super pixels of the safety helmet and the non-safety helmet by using the texture features is an effective segmentation method. The super-pixel histogram can reflect the frequency of the pixel gray value in the super-pixel of the target image on each gray level, and can select four attributes of dispersion degree, variance, skewness and kurtosis to represent a texture feature vector, which can be calculated by adopting the following formula:
in the formula (I), the compound is shown in the specification,
as the degree of dispersion,
is the variance of the received signal and the received signal,
in order to obtain the degree of skewness,
in order to be the kurtosis,
is gray scaleThe number of stages is such that,
a gray histogram function corresponding to the super pixel block.
Then, the characteristic variables
Is defined as:
finally, setting a safety helmet superpixel block as a positive sample and a label as '1'; setting a background super-pixel block as a negative sample and a label as '0'; and adopts an automatic hyper-parameter optimization mode to train the support vector machine
。
In some embodiments, cross-validation may be employed to evaluate
The accuracy of the classification of the sample images can be calculated, in particular
Is predicted by the error
Evaluating the accuracy of its classification, of a certain value
It is described that
Probability of misprediction for unknown test samples. For example, to increase
Is accurate to predictRate of determination, training by automatic hyper-parametric optimization
Then calculates again after being optimized by hyper-parameters
At the time of mispredicting the loss value
Illustrating training by hyper-parametric optimization
The prediction error for an unknown test sample is relatively small.
Fig. 2 shows a schematic flow diagram of a mine personnel safety cap segmentation method of an embodiment of the present application. In some embodiments, the mine personnel helmet segmentation method may be implemented by the classifier and category correction device 140 of fig. 1, or as the classifier and category correction device 140 of fig. 1.
As shown in fig. 2, the mine personnel safety helmet segmentation method and device comprises the following steps:
step 210, extracting color feature vectors, texture feature vectors and target contour features of a plurality of superpixel blocks formed by target image granulation.
In this embodiment, the method for granulating the target image is the same as the method for granulating the sample image, and the color feature vectors and texture feature vectors of the plurality of superpixel blocks formed by extracting and granulating the target image are also the same as the processing method in the sample image, which is not repeated herein.
In some embodiments, the objects in the image of the objects are helmets, which are produced strictly according to national standards GB2811-2019, are the most salient pixel regions in the image of mine personnel, having fixed geometric outlines in addition to a specific color class.
In the embodiment, the morphological characteristics of the shell part of the safety helmet are mainly studied, and the brim of the safety helmet is removedThe main body part of the safety helmet except the visor is shaped like a hemisphere, the external contour is composed of smooth curves, and the contour line of the safety helmet
Must have a zero slope point
And slope discontinuity
Two types of points, one or two pointed "bumps" are present on the outline of the helmet in the frontal image taken at the central position of the helmet, so that a maximum of two "bumps" may be present, and are located at the zero slope point
Slope discontinuity point
And a curved section in the region of the "bulge
Absolute value of slope of
Monotonously changes in the x direction or the y direction in the plane of the helmet image.
Thus, the target profile features may include: the safety helmet comprises a safety helmet body, a safety helmet image, a slope image and a slope image, wherein a zero slope point, a slope catastrophe point and at most two bulges are arranged on an outer contour line of the safety helmet body, and the absolute value of the slope of a curve section positioned between the zero slope point, the slope catastrophe point and the bulges is monotonously changed in the x direction or the y direction in a plane where the safety helmet image is positioned.
Step 220, inputting the color feature vector and the texture feature vector into a pre-trained support vector machine
In (1),so as to divide all the superpixel blocks into two types of target superpixel blocks and background superpixel blocks.
And step 230, modifying the category of the target superpixel block according to the target contour characteristics.
In the embodiment, the support vector machine is supported due to participation
The trained sample images lack global representativeness in the whole data sample set, and simultaneously, a support vector machine obtained through hyper-parameter optimization training
The prediction capability of the method for detecting the positive sample is limited, so that a small number of negative samples are wrongly classified into the positive samples, and the positive samples need to be subjected to class correction.
In some embodiments, the class correction of the target superpixel block may be performed by:
step 2301, extracting boundary masks of superpixel regions of the target superpixel blocks and analyzing the change characteristics of the slope of straight lines between pixels on the boundary masks.
In the present embodiment, a boundary mask of a superpixel region of a target superpixel block is extracted
And adopting morphological expansion operator to process, then selecting boundary mask of superpixel region of target superpixel block
The contour line of any point above
Starting point of (2)
Calculating the slope of the straight line between adjacent unit pixels in sequence according to the clockwise direction or the counterclockwise direction
Until it returns to the starting point
And analyzing the slope of the straight line
The change characteristic of (c).
Step 2302 corrects the class of the misclassified target superpixel based on the variance characteristics and the target contour features.
First, the boundary mask of the super pixel area of the target super pixel block is judged
Generated from several superpixel blocks.
For example, boundary mask of superpixel region of target superpixel block
Generated from a single superpixel block. Slope of straight line
Is completely in accordance with the target contour feature, the change characteristics are judged as a safety helmet area which is divided by a superpixel block
(ii) a Slope of straight line
If the change characteristic of (a) is partially in accordance with the target profile feature, the slope of the line that will not be in accordance with the target profile feature
Defined as the slope of an abnormal straight line
By a region adjacency graph
Retrieving slope of line due to abnormality
Neighbor superpixel block corresponding to interval
Extracting its boundary mask and
generating a new superpixel region mask after fusing
Updating the slope of the line
And analyzing the change characteristics of the super-pixels, if the proportion of the parts conforming to the target contour features is increased, retaining the newly-added super-pixels, otherwise, regarding the super-pixels as misclassification samples, and filtering the samples until the slope of the straight line conforming to the target contour features
The ratio of (a) is maximized.
As another example, boundary mask of superpixel region of target superpixel block
Generated from a plurality of superpixel blocks. Slope of straight line
Is determined to be a helmet area divided by a plurality of superpixel blocks when the change characteristics of (a) completely accord with the target contour characteristics
(ii) a Slope of straight line
In which only part of the slope of the straight line
Is in accordance with the target contour feature and is in accordance with the slope of the straight line of the target contour feature
And slope of line not conforming to target profile feature
Simultaneously distributed over each super-pixel block boundary, then
Retrieving the slope of a line that does not match the target profile feature
Interval corresponds to
Extracting its boundary mask and
generating a new superpixel region mask after fusing
Updating the slope of the line
And analyzing the change characteristics to conform to the slope of the straight line of the target contour characteristics
If the proportion of the super pixels is increased, the new super pixels are reserved, and the cycle execution is carried out until the slope of the straight line which accords with the target contour characteristics
Maximizing the proportion; if the slope of the straight line of the target contour feature is met
In which the slope of the line only partially conforms to the profile feature of the target
Is in accordance with the target contour feature and is in accordance with the slope of the straight line of the target contour feature
And slope of line not conforming to target profile feature
Distributed on different super-pixel boundaries, is composed of
Retrieving the slope of a line that does not match the target profile feature
Interval corresponds to
And removing the target contour features to update the slope of the straight line conforming to the target contour features
And analyzing the change characteristics to conform to the slope of the straight line of the target contour characteristics
If the proportion is increased, the correction is effective, and the cyclic execution is carried out until the linear slope of the target contour characteristic is met
Maximizing the proportion;
slope of straight line
If the target contour feature is completely not met, the method is characterized by
Retrieving the slope of the line
Corresponding super pixels are removed one by one and the slope of the straight line is updated
Slope of straight line
And judging the target contour characteristics which are not met with the target contour characteristics as a background area, and filtering all superpixel blocks contained in the background area.
And 240, judging whether the corrected target superpixel block has background pixel points.
In some embodiments, the following steps may be employed for the determination:
step 2401, extracting a boundary mask of the superpixel region of the corrected target superpixel block.
Step 2402, adopt
And the operator extracts the contour edge of the corrected target superpixel block.
Step 2403, calculating a difference set of a boundary mask of a superpixel region of the modified target superpixel block and a contour edge of the modified target superpixel block
。
Step 2404, if so
And determining that no background pixel point exists in the corrected target superpixel block.
Step 2405If, if
And determining that background pixel points exist in the corrected target superpixel block.
It should be noted that, if it is determined that there is no background pixel point in the corrected target superpixel block, step 250 is executed, and if it is determined that there is a background pixel point in the corrected target superpixel block, step 260 is executed.
And step 250, segmenting the target image according to the target superpixel blocks.
And step 260, classifying the corrected target superpixel blocks again to obtain target pixel point superpixel blocks and background pixel point superpixel blocks, filtering the background pixel point superpixel blocks, and segmenting the target image according to the target pixel point superpixel blocks.
In this embodiment, the revised target superpixel blocks are again classified according to the difference set
The boundary line of the pixel point of the super pixel block decomposes the super pixel block into a target pixel super pixel block and a background pixel super pixel block.
In the embodiment of the application, a target image is granulated into a plurality of superpixel blocks based on a SLIC model, the superpixel blocks are classified based on a Support Vector Machine (SVM), the classified superpixel blocks are subjected to class correction, and finally the corrected superpixel blocks are adopted to segment the target image, so that the defects that the video image of an acquired person is uneven in illumination, distorted in color information, random in shadow distribution, difficult in distinguishing between a target and a background boundary and the like due to the influence of various severe conditions such as dust interference, high noise, poor illumination condition and the like can be avoided, the difficulty in segmenting the person target is reduced, and the application of the technologies such as person detection, identification, positioning and tracking and the like is facilitated.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
The above is a description of method embodiments, and the embodiments of the present application are further described below by way of apparatus embodiments.
Fig. 3 shows a block schematic diagram of a mine personnel safety cap segmentation apparatus of an embodiment of the present application. In some embodiments, the mine personnel helmet segmentation apparatus may be implemented by the classifier and category modification apparatus 140 of fig. 1, or as the classifier and category modification apparatus 140 of fig. 1.
As shown in fig. 3, the mine personnel safety helmet dividing device includes:
an extracting module 310 is used for extracting the color feature vector, the texture feature vector and the target contour feature of a plurality of superpixel blocks formed by the target image granulation.
The classification module 320 is configured to input the color feature vector and the texture feature vector into a pre-trained support vector machine SVM, so as to divide the plurality of superpixel blocks into two types, namely a target superpixel block and a background superpixel block.
And the correcting module 330 is configured to correct the category of the misclassified target superpixel block according to the target contour feature.
The determining module 340 is configured to determine whether a background pixel exists in the corrected target superpixel block.
And a segmentation module 350, configured to segment the target image according to the target super-pixel block when there is no background pixel point in the corrected target super-pixel block.
In some embodiments, the determining module 340 is specifically configured to:
extracting a boundary mask of a superpixel area of the corrected target superpixel block;
by using
The operator extracts the contour edge of the corrected target superpixel block;
calculating a difference set between a boundary mask of a superpixel region of the modified target superpixel block and a contour edge of the modified target superpixel block
；
If it is
If so, determining that no background pixel point exists in the corrected target superpixel block;
if it is
And determining that background pixel points exist in the corrected target superpixel block.
In some embodiments, the segmentation module 350 is further configured to classify the corrected target super-pixel block again when a background pixel exists in the corrected target super-pixel block, so as to obtain a target pixel super-pixel block and a background pixel super-pixel block; filtering background pixel super-pixel blocks; and segmenting the target image according to the superpixel blocks of the target pixel points.
In some embodiments, the segmentation module 350 is further configured to segment the difference set according to the difference set
The boundary line of the pixel point of the super pixel block decomposes the super pixel block into a target pixel super pixel block and a background pixel super pixel block.
In some embodiments, the target in the target image is a hard hat, and the target contour features include: the safety helmet comprises a safety helmet body, a safety helmet image, a slope image and a slope image, wherein a zero slope point, a slope catastrophe point and at most two bulges are arranged on an outer contour line of the safety helmet body, and the absolute value of the slope of a curve section positioned between the zero slope point, the slope catastrophe point and the bulges is monotonously changed in the x direction or the y direction in a plane where the safety helmet image is positioned.
In some embodiments, the modification module 330 is specifically configured to:
extracting a boundary mask of a superpixel region of a target superpixel block and analyzing the change characteristic of the slope of a straight line between pixels on the boundary mask;
and correcting the classes of the object superpixel blocks which are misclassified according to the change characteristics and the object contour characteristics.
In some embodiments, the modification module 330 is further specifically configured to:
judging whether the change characteristics accord with the target contour characteristics;
if yes, reserving the target superpixel block;
if not, detecting the number of the superpixel blocks contained in the boundary mask of the superpixel region of the target superpixel block;
based on the number, the target superpixel block is modified according to the change characteristics and the target contour features.
In some embodiments, the extraction module 310 is specifically configured to:
selecting
、
、
、
Describing color features by the four color models to obtain color feature vectors;
and describing texture feature vectors by adopting a multi-order matrix of the gray distribution mean value of pixel values in the super-pixel blocks, wherein the texture feature vectors comprise four attributes of dispersion degree, variance, skewness and kurtosis.
In some embodiments, the color feature vector is calculated using the following equation:
in the formula (I), the compound is shown in the specification,
in order to be a color feature vector,
、
、
、
、
、
、
、
、
、
、
、
feature components on the four color models;
the dispersity, the variance, the skewness and the kurtosis of the texture feature vector are respectively calculated by adopting the following formula:
in the formula (I), the compound is shown in the specification,
as the degree of dispersion,
is the variance of the received signal and the received signal,
in order to obtain the degree of skewness,
in order to be the kurtosis,
in order to be a gray scale level,
a gray histogram function corresponding to the super pixel block.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (6)
1. A miner's safety helmet detection system, comprising:
the system comprises a miner image acquisition device, a data storage device, a feature extraction device, a classifier, a category correction device and a safety helmet detection result display device which are mutually connected through a data transmission line;
the miner image acquisition device is connected with the data storage device through a wired link or a wireless link, the output end of the data storage device is connected with the input end of the feature extraction device, the output end of the feature extraction device is connected with the input end of the classifier and the input end of the category correction device, and the output end of the classification and category correction device is connected with the input end of the safety helmet detection result display device.
2. The miner's helmet detection system of claim 1, wherein the miner's image capture device includes a camera and a fill light; the camera is an industrial explosion-proof color camera or a multispectral camera, and the light supplement lamp comprises a plurality of groups of diffuse reflection LED lamp groups.
3. The miner's helmet detection system of claim 2, wherein the camera is externally provided with an explosion proof housing having an explosion proof membrane mounted thereon, the camera lens of the camera being aligned parallel to the explosion proof membrane.
4. The miner's helmet detection system of claim 1, wherein the data storage device is a local server or a cloud server.
5. The miner's helmet detection system of claim 1, wherein the output end of the classifier and the category correction device is connected with a ground information terminal through a mobile internet.
6. The miner's safety helmet detection system of claim 1, wherein the safety helmet detection result display device is connected with an audible and visual alarm, a voice alarm and a spliced screen display terminal.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202022553452.6U CN213241250U (en) | 2020-11-07 | 2020-11-07 | Miner safety helmet detection system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202022553452.6U CN213241250U (en) | 2020-11-07 | 2020-11-07 | Miner safety helmet detection system |
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| CN213241250U true CN213241250U (en) | 2021-05-18 |
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