CN111626203B - Railway foreign matter identification method and system based on machine learning - Google Patents

Railway foreign matter identification method and system based on machine learning Download PDF

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CN111626203B
CN111626203B CN202010460331.9A CN202010460331A CN111626203B CN 111626203 B CN111626203 B CN 111626203B CN 202010460331 A CN202010460331 A CN 202010460331A CN 111626203 B CN111626203 B CN 111626203B
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苑贵全
骞一凡
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Zhangjiakou Dongchu Technology Co ltd
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Abstract

The application provides a railway foreign matter identification method and a system based on machine learning, and the method comprises the following steps: acquiring a monitoring video of a track in front of a train in the running process of the train, and acquiring a plurality of frames of monitoring images in the monitoring video in real time; establishing a dangerous area foreign matter detection window in the monitoring image, and extracting a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window; preprocessing a track dangerous area detection image; inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, identifying whether foreign matters exist in the rail dangerous area detection image or not by the foreign matter identification classification model, sending an alarm signal if the foreign matters exist in the rail dangerous area detection image, and otherwise, continuously identifying other rail dangerous area detection images. The detection accuracy of this application to the railway foreign matter is high, uses manpower sparingly, and automatic alarm, the cost is lower, and receives environmental impact little.

Description

Railway foreign matter identification method and system based on machine learning
Technical Field
The application relates to the technical field of railway foreign matter intrusion monitoring, in particular to a railway foreign matter identification method and system based on machine learning.
Background
The distribution area of the high-speed railway in China is wide, the distance is long, the geological condition is complex, the natural disaster is serious, and meanwhile, the highway has the characteristics of day and night operation, closed type, only allowing trains to run and the like. Due to the characteristics of long distance, wide distribution area, day and night operation, closed type and the like, when natural disasters such as landslide, geological subsidence and the like occur along the high-speed railway, road traffic fault warning cannot be issued in time, and serious traffic safety accidents and chain reactions are easily caused. In order to reduce traffic accidents of high-speed railways and reduce casualties and property loss, natural disaster early warning and special traffic safety accident alarming along the high-speed railways are urgently needed to be solved.
Rock rolling, foreign matter such as pedestrian or animal invade the railway boundary, have proruption nature, irregular and unpredictable nature, and the railway traffic accident is aroused frequently, has threatened people's life safety seriously, and traditional track detection mainly relies on the manpower, comes the invasion condition of inspection track through set up a large amount of personnel of patrolling and examining nationwide, but comparatively consumes manpower and financial resources to, not rapid enough to urgent accident response.
In addition, video monitor system is used for railway safety monitoring, monitors whether there is the boundary of foreign matter invasion railway, however, present video monitor system needs the special messenger to monitor, and monitoring personnel's work load is very big, produces careless omission easily when tired, causes dangerous hidden danger. The existing method for monitoring the foreign matters by adopting the laser curtain wall has the advantages that the foreign matters are detected by installing a plurality of two-dimensional laser sensors to form the laser curtain wall, the method is high in detection speed and sensitivity, the installation is complex, the influence of the environment is large, only a few cross sections can be detected, and the cost is high.
Disclosure of Invention
The method is high in detection accuracy, labor-saving, automatic in alarming, low in cost and small in environmental influence.
In order to achieve the above object, the present application provides a method for identifying a foreign object in a railway based on machine learning, the method comprising the steps of: acquiring a monitoring video of a track in front of a train in the running process of the train, and acquiring a plurality of frames of monitoring images in the monitoring video in real time; establishing a dangerous area foreign matter detection window in the monitoring image, and extracting a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window; preprocessing a track dangerous area detection image; inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, identifying whether foreign matters exist in the rail dangerous area detection image or not by the foreign matter identification classification model, sending an alarm signal if the foreign matters exist in the rail dangerous area detection image, and otherwise, continuously identifying other rail dangerous area detection images.
As above, the method for establishing the foreign object identification classification model in advance comprises the following steps:
acquiring training image sets of various foreign matters;
respectively inputting the training image set of each foreign matter into a convolutional neural network for training to obtain a foreign matter identification submodel corresponding to the foreign matter;
and fusing the plurality of foreign matter recognition submodels to form a foreign matter recognition classification model.
The above method, wherein, for each foreign object, the foreign object image on the railway is collected in different weather and at different time, and from multiple angles, the foreign object contour image in the foreign object image is extracted, and the plurality of foreign object contour images form the training image set of the foreign object.
As above, the method for obtaining the foreign object recognition submodel includes:
inputting a training image set of foreign matters into an input layer of a convolutional neural network, and outputting an image matrix of the foreign matters by the input layer;
extracting the characteristics of the image matrix of the foreign matters from the convolution layer to obtain a characteristic image matrix of the foreign matters;
the pooling layer is used for pooling the output characteristic image matrix of the convolutional layer to obtain a key characteristic identification image matrix of the foreign matter;
inputting the key characteristic identification image matrix output by the pooling layer into a full-connection layer, and classifying the input key characteristic identification image matrix into a corresponding foreign matter type area by the full-connection layer;
and after the full connection layer classifies the characteristic image matrix into corresponding foreign matter type areas, the output layer outputs a foreign matter identification submodel of each type of foreign matter.
As described above, the calculation formula for obtaining the characteristic image matrix of the foreign object in the convolutional layer is:
Cij＝f(w·xj:j+h-1+b)；
wherein CijA characteristic image matrix which represents the j-th foreign matter with the type i corresponding to the characteristic image matrix after the convolution operation; f (-) represents the convolutional layer convolution kernel; w represents a weight matrix of the convolutional layer filter; h represents the length of the convolutional layer filter; b represents a bias; x is the number ofj:j+h-1And an image matrix representing the image of the j-th foreign object of the type i to the image matrix of the j + h-1-th foreign object.
As described above, the formula for pooling the characteristic image matrix output by the convolutional layer is as follows:
Figure BDA0002510745960000031
q is an odd number.
Wherein the content of the first and second substances,
Figure BDA0002510745960000041
representing the characteristic value of a central pixel point of a window covering convolution layer output image with q multiplied by q dimension, and u representing the transverse coordinate of a pixel point of a window covering convolution layer output characteristic image with q multiplied by q dimension; v represents the vertical coordinate of the pixel point of the window covering convolution layer output characteristic image with q multiplied by q dimension; c (u, v) represents a characteristic value of a pixel point with q multiplied by q dimension window covering convolutional layer output characteristic image coordinates of (u, v).
As above, wherein the method of extracting the rail hazard zone detection image includes:
identifying the shape and position of the track in the monitored image;
establishing a dangerous area foreign matter detection window on the outer periphery of the track according to the shape and the position of the track;
and dividing the image positioned outside the dangerous area foreign matter detection window, reserving the image positioned inside the dangerous area foreign matter detection window, and forming a track dangerous area detection image.
As above, wherein, the method for preprocessing the rail dangerous area detection image comprises:
carrying out gray processing on the detection image of the rail dangerous area;
carrying out smooth filtering processing on the rail dangerous area detection image subjected to graying processing;
and calculating the definition of the detected image of the track dangerous area after the smooth filtering processing, judging whether the definition exceeds a preset qualified threshold value, if not, performing the definition processing on the image, otherwise, finishing the preprocessing.
As above, the calculation formula of the pixel value of the image output after the filtering process is as follows:
Figure BDA0002510745960000042
wherein, p (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the output image after the filtering processing, f (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the track dangerous area detection image, (x, y) represents the coordinate of the pixel, and sigma represents the variance; denotes a convolution operation; e is 2.718, k represents the dimension of the window.
The application also provides a railway foreign matter identification system based on machine learning, includes:
the image acquisition module is used for acquiring a monitoring video of a track in front of a train in the running process of the train and acquiring a plurality of frames of monitoring images in the monitoring video in real time;
the image extraction module is used for establishing a dangerous area foreign matter detection window in the monitoring image and extracting a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window;
the image preprocessing module is used for preprocessing the detection image of the rail dangerous area;
and the foreign matter identification module is used for inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, and the foreign matter identification classification model is used for identifying whether foreign matters exist in the rail dangerous area detection image or not.
The beneficial effect that this application realized is as follows:
(1) this application is through foreign matter discernment classification model, whether there is the foreign matter in the automatic identification image to the kind of output foreign matter, if discern the foreign matter, then report to the police automatically, remind navigating mate the place ahead to have dangerous foreign matter, prevent that danger from taking place.
(2) According to the method and the device, the foreign matter detection window in the dangerous area is established in the monitoring image, so that the influence of houses, plains or mountains and the like in the monitoring image on the monitoring result is eliminated, and the accuracy of foreign matter detection in the dangerous area of the track is improved.
(3) The method and the device for detecting the image sharpness detect whether the image sharpness meets the requirements after filtering processing, if so, the image sharpness is qualified, otherwise, the image sharpness is required, and the detection accuracy is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a railway foreign matter identification method based on machine learning according to the present application.
Fig. 2 is a schematic diagram of an image for detecting a dangerous area of a track according to the present application.
Fig. 3 is a flowchart of a method for extracting a track danger area detection image according to the present application.
Fig. 4 is a flowchart of a method for preprocessing a detection image of a dangerous area of a rail according to the present application.
Fig. 5 is a flowchart of a method for pre-establishing a foreign object identification classification model according to the present application.
FIG. 6 is a schematic structural diagram of a railway foreign object recognition system based on machine learning according to the present application
Reference numerals: 1-a dangerous area foreign matter detection window; 2-a first track; 3-a second track; 4-a foreign body; 10-an image acquisition module; 20-an image extraction module; 30-an image pre-processing module; 40-a foreign object identification module; 41-foreign object recognition classification model; 100-railway foreign body identification system.
Detailed Description
The technical solutions in the embodiments of the present application are 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, but not all, embodiments of the present application. 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.
Example one
As shown in fig. 1, the present application provides a method for identifying a foreign object in a railway based on machine learning, which comprises the following steps:
and step S1, acquiring the monitoring video of the track in front of the train in the running process of the train, and acquiring multi-frame monitoring images in the monitoring video in real time.
Specifically, a plurality of time nodes are selected to obtain a plurality of frames of monitoring images of the monitoring video, each time node corresponds to one frame of monitoring image, and each frame of monitoring image reflects the environment condition of the track when the corresponding time node is located.
When the train runs at night, the front lighting system is started, and the condition that the monitoring video of the track in front of the train is collected is clear is guaranteed.
Step S2, a dangerous area foreign matter detection window is established in the monitoring image, and a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window is extracted.
The foreign matter detection window in the dangerous area is established in the monitoring image, so that the influence of houses, plains or mountains and the like in the monitoring image on the monitoring result is eliminated, and the accuracy of foreign matter detection in the dangerous area of the track is improved.
As shown in fig. 3, step S2 includes the following sub-steps:
step S210, identify the shape and position of the track in the monitored image.
Step S220, according to the shape and position of the track, a dangerous area foreign object detection window is established on the outer periphery of the track, wherein the side of the dangerous area foreign object detection window is parallel to the track.
Step S230, dividing the image outside the dangerous region foreign matter detection window, and retaining the image inside the dangerous region foreign matter detection window to form a track dangerous region detection image.
Specifically, the foreign matter detection window in the danger area is established according to the shape of the rail, and if the railway rail is linear, the foreign matter detection window in the danger area of the linear railway rail is established; and if the railway track is in a curve shape, establishing a curve-shaped dangerous area foreign matter detection window. The side lines of the two sides of the dangerous area foreign matter detection window are parallel to the railway track line and offset to the two sides of the railway track line for a certain distance, the front edge of the dangerous area foreign matter detection window is positioned at the end of the railway track line, and the rear edge of the dangerous area foreign matter detection window is positioned at the edge close to the head of the train.
As shown in fig. 2, a hazardous area foreign matter detection window 1 is established, and both side edges of the hazardous area foreign matter detection window 1 are located at both sides of the first rail 2 and the second rail 3, so that a foreign matter 4 (as shown in fig. 2) can be detected in the hazardous area foreign matter detection window.
In step S3, the rail risk region detection image is preprocessed.
As shown in fig. 4, step S3 includes the following sub-steps:
in step S310, a grayscale process is performed on the rail risk region detection image.
The calculation formula for carrying out gray processing on the rail dangerous area detection image is as follows:
H＝0.3R+0.59G+0.11B；
h represents a gray value; r represents red; g represents green; b represents blue.
Step S320, performing smoothing filtering processing on the rail dangerous region detection image after the graying processing.
And carrying out smooth filtering treatment on the track dangerous area detection image, filtering high-frequency noise, restraining the noise of the track dangerous area detection image under the condition of keeping image detail characteristics as much as possible, and extracting the characteristics of the track dangerous area detection image, wherein the characteristics comprise contours or edges.
According to one embodiment of the invention, a k-dimension-multiplied k-dimension window containing a weighting coefficient is placed on the track dangerous area detection image, the track dangerous area detection image is weighted and averaged, the value of each pixel point is obtained by carrying out weighted average on the pixel point and other pixel values in the neighborhood of the window, namely, each pixel point in the window scanning image replaces the value of the central pixel point of the template by the weighted average value of the pixels in the neighborhood determined by the window.
According to an embodiment of the present invention, the calculation formula of the pixel values of the image output after the filtering process is as follows:
Figure BDA0002510745960000091
p (x, y) represents a pixel value of a pixel with a pixel coordinate (x, y) in an output image after filtering, f (x, y) represents a pixel value of a pixel with a pixel coordinate (x, y) in a track dangerous region detection image, (x, y) represents a coordinate of a pixel, sigma represents variance, and the larger sigma is, the wider frequency band is, and noise can be well suppressed; denotes a convolution operation; e denotes a constant, e is 2.718, and k denotes the dimension of the window.
Step S330, calculating the definition Q of the detection image of the track dangerous area after the smooth filtering processing, and judging whether the definition exceeds a preset qualified threshold QQualifiedAnd if not, performing sharpening processing on the image, otherwise, finishing the preprocessing.
Because the image after the image filtering processing can become fuzzy, whether the definition of the image after the filtering processing meets the requirement is detected, if so, the image is qualified, otherwise, the image needs to be subjected to the definition processing, and the detection accuracy is improved.
The calculation formula of the definition of the detected image of the track dangerous area after the smoothing filtering processing is as follows:
Figure BDA0002510745960000092
wherein Q represents definition, and p (x, y) represents a pixel value of a pixel with pixel point coordinates (x, y) in an output image after filtering processing; n represents the total row number of the output image matrix after filtering processing; m represents the total number of columns of the output image matrix after the filtering process.
Step S340, the track dangerous area detection image is subjected to sharpening processing, and an ideal and clear track dangerous area detection image is obtained.
The formula of the sharpening process is as follows:
Figure BDA0002510745960000101
wherein, T (x, y) represents the pixel value of the pixel with the pixel point coordinate (x, y) in the image after the sharpening processing, and J (r) represents the diffraction limit value of the actual lens; w represents frequency; f-1(. cndot.) denotes the inverse fourier operation.
And step S4, inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, identifying whether foreign matters exist in the rail dangerous area detection image or not by the foreign matter identification classification model, sending an alarm signal if the foreign matters exist in the rail dangerous area detection image, and otherwise, continuously identifying other rail dangerous area detection images.
Specifically, after a preprocessed rail dangerous region detection image is input into a foreign matter identification classification model, the foreign matter identification classification model extracts a characteristic image matrix in the rail dangerous region detection image, compares the extracted characteristic image matrix with a key characteristic identification image matrix of a foreign matter stored in the rail dangerous region detection image, and judges that the foreign matter exists in the rail dangerous region detection image if the similarity between the extracted characteristic image matrix and the key characteristic identification image matrix of the foreign matter is greater than a preset threshold value, and obtains the type of the foreign matter with the maximum similarity value between the extracted characteristic image matrix and the key characteristic identification image matrix of a certain type of foreign matter as the type of the foreign matter existing in the rail dangerous region detection image; and if the similarity of the extracted characteristic image matrix and the key characteristic identification image matrix of the foreign matters is less than or equal to a preset threshold value, judging that the foreign matters do not exist in the detection image of the track dangerous area.
The foreign matter identification and classification model comprises key characteristic identification images of different types of foreign matters and is used for identifying the foreign matters and the types of the foreign matters.
As shown in fig. 5, the method for establishing the foreign object identification classification model in advance includes:
step S410, acquiring training image sets of various foreign matters.
Specifically, a foreign matter image causing an accident of the railway train is selected for training, the foreign matter image on the railway is collected from multiple angles at different weather and different time aiming at each foreign matter, the foreign matter outline image in the foreign matter image is extracted, and the plurality of foreign matter outline images form a training image set of the foreign matter.
The training image set of the foreign object is denoted X ═ Xi1，Xi2，…,Xin}; wherein i represents the type of foreign matter; n represents the total number of foreign body images acquired by each foreign body; xinAnd the n foreign body outline image represents the acquired i foreign body.
Step S420, inputting the training image set of each foreign matter into a convolutional neural network for training, and obtaining a foreign matter identification submodel corresponding to the foreign matter.
The convolutional neural network comprises an input layer, a convolutional layer, a pooling layer, a full-link layer and an output layer. Wherein, the convolution layer is a foreign matter feature extraction layer, and the feature of the foreign matter is extracted through a filter; the pooling layer is a feature mapping layer, and the features obtained after the convolution layer are sampled to obtain a local optimal value.
Specifically, step S420 includes the following sub-steps:
in step S421, a training image set of a foreign object is input to the input layer of the convolutional neural network, and the input layer outputs an image matrix of the foreign object.
In step S422, the features of the image matrix of the foreign object are extracted from the convolution layer, and a feature image matrix of the foreign object is obtained.
After the input layer outputs the image matrix, inputting the image matrix output by the input layer into the convolutional layer, and extracting a characteristic image matrix of the foreign matter in the convolutional layer;
the calculation formula for acquiring the characteristic image matrix of the foreign matter on the convolutional layer is as follows:
Cij＝f(w·xj:j+n-1+b)；
wherein CijA characteristic image matrix which represents the j-th foreign matter with the type i corresponding to the characteristic image matrix after the convolution operation; f (-) represents the convolutional layer convolution kernel; w represents a weight matrix of the convolutional layer filter; h represents the length of the convolutional layer filter; b represents a bias; x is the number ofj:j+h-1And an image matrix representing the image of the j-th foreign object of the type i to the image matrix of the j + h-1-th foreign object.
In step S423, the pooling layer pools the feature image matrix outputted from the convolutional layer to obtain a key feature identification image matrix of the foreign object.
Specifically, a q-by-q dimensional (i.e., q rows and q columns) window is established in the pooling layer, the q-by-q dimensional window is placed on the characteristic image output by the convolutional layer, the characteristic values of all pixel points in the window are summed, and then the average value is calculated to serve as the characteristic value of the central pixel point of the window, so that a key characteristic identification image matrix is obtained in the pooling layer.
The formula for pooling the characteristic image matrix output by the convolutional layer is as follows:
Figure BDA0002510745960000121
q is an odd number.
Wherein the content of the first and second substances,
Figure BDA0002510745960000122
a window representing q times q dimensions covers the characteristic values of the central pixel points of the convolutional layer output image,
Figure BDA0002510745960000123
representing the coordinates of the center pixel of the q-by-q-dimensional window-covering convolution layer output image, u represents the q-by-q-dimensional window-covering convolutionOutputting the horizontal coordinates of the pixel points of the characteristic image in a lamination mode; v represents the vertical coordinate of the pixel point of the window covering convolution layer output characteristic image with q multiplied by q dimension; c (u, v) represents a characteristic value of a pixel point with q multiplied by q dimension window covering convolutional layer output characteristic image coordinates of (u, v).
Step S424, the key feature recognition image matrix output by the pooling layer is input to the full connection layer, and the full connection layer classifies the input key feature recognition image matrix into the corresponding foreign matter type region.
In step S425, the full link layer classifies the feature image matrix into corresponding foreign object type regions, and then the output layer outputs a foreign object identification submodel for each type of foreign object.
The foreign matter identifier model of each foreign matter type comprises a key feature identification image matrix of the foreign matter type.
And step S430, fusing or combining the plurality of foreign matter identification submodels together to form a foreign matter identification classification model.
Example two
As shown in fig. 5, the present application provides a machine learning based railway foreign object recognition system 100, comprising:
the image acquisition module 10 is used for acquiring a monitoring video of a track in front of a train in the running process of the train and acquiring multi-frame monitoring images in the monitoring video in real time;
the image extraction module 20 is configured to establish a dangerous region foreign matter detection window in the monitoring image, and extract a track dangerous region detection image of the monitoring image located in the dangerous region foreign matter detection window;
the image preprocessing module 30 is used for preprocessing the detection image of the rail dangerous area;
and the foreign matter identification module 40 is used for inputting the preprocessed track dangerous area detection image into a pre-established foreign matter identification classification model 41, wherein the foreign matter identification classification model 41 is used for identifying whether foreign matters exist in the track dangerous area detection image or not, if the foreign matters exist, an alarm signal is sent out, and otherwise, other track dangerous area detection images are continuously identified.
The foreign object recognition module 40 includes a foreign object recognition and classification model 41, and the foreign object recognition and classification model 41 is used for recognizing foreign objects and the types of the foreign objects.
The beneficial effect that this application realized is as follows:
(1) this application is through foreign matter discernment classification model, whether there is the foreign matter in the automatic identification image to the kind of output foreign matter, if discern the foreign matter, then report to the police automatically, remind navigating mate the place ahead to have dangerous foreign matter, prevent that danger from taking place.
(2) According to the method and the device, the foreign matter detection window in the dangerous area is established in the monitoring image, so that the influence of houses, plains or mountains and the like in the monitoring image on the monitoring result is eliminated, and the accuracy of foreign matter detection in the dangerous area of the track is improved.
(3) The method and the device for detecting the image sharpness detect whether the image sharpness meets the requirements after filtering processing, if so, the image sharpness is qualified, otherwise, the image sharpness is required, and the detection accuracy is improved.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (5)

1. A railway foreign matter identification method based on machine learning is characterized by comprising the following steps:
acquiring a monitoring video of a track in front of a train in the running process of the train, and acquiring a plurality of frames of monitoring images in the monitoring video in real time;
establishing a dangerous area foreign matter detection window in the monitoring image, and extracting a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window;
preprocessing a track dangerous area detection image;
the method for preprocessing the rail dangerous area detection image comprises the following steps:
carrying out gray processing on the detection image of the rail dangerous area;
carrying out smooth filtering processing on the rail dangerous area detection image subjected to graying processing;
using a k-multiplied k-dimensional window containing a weighting coefficient to be placed on the track dangerous area detection image, carrying out weighted average on the track dangerous area detection image, wherein the value of each pixel point is obtained by carrying out weighted average on the value of each pixel point and other pixel values in the neighborhood of the window, namely scanning each pixel point in the image by using the window, and replacing the value of the central pixel point of the template by using the weighted average value of the pixels in the neighborhood determined by the window;
the calculation formula of the pixel value of the image output after the filtering process is as follows:
Figure FDA0003196659300000011
wherein, p (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the output image after the filtering processing, f (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the track dangerous area detection image, (x, y) represents the coordinate of the pixel, and sigma represents the variance; denotes a convolution operation; e is 2.718, k represents the dimension of the window;
calculating the definition of the detected image in the rail dangerous area after the smooth filtering processing, judging whether the definition exceeds a preset qualified threshold value, if not, performing the definition processing on the image, otherwise, finishing the preprocessing;
the calculation formula of the definition of the track dangerous area detection image after the smooth filtering processing is as follows:
Figure FDA0003196659300000021
wherein Q represents definition, and p (x, y) represents a pixel value of a pixel with pixel point coordinates (x, y) in an output image after filtering processing; n represents the total row number of the output image matrix after filtering processing; m represents the total column number of the output image matrix after filtering processing;
inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, identifying whether foreign matters exist in the rail dangerous area detection image or not by the foreign matter identification classification model, if the foreign matters exist, sending an alarm signal, and if not, continuously identifying other rail dangerous area detection images;
in the process of pre-establishing a foreign matter identification classification model, selecting a foreign matter image causing an accident of a railway train for training, acquiring the foreign matter image on the railway from multiple angles at different days and different moments aiming at each foreign matter, extracting a foreign matter outline image in the foreign matter image, and forming a training image set of the foreign matter by the foreign matter outline images; respectively inputting the training image set of each foreign matter into a convolutional neural network for training to obtain a foreign matter identification submodel corresponding to the foreign matter;
wherein, the training image set of each foreign matter is respectively input into the convolutional neural network for training, which comprises the following steps: respectively and sequentially inputting the training image set of each foreign matter into an input layer, a convolution layer, a pooling layer, a full-link layer and an output layer of a convolutional neural network to obtain a foreign matter identification submodel;
the calculation formula for acquiring the characteristic image matrix of the foreign matter in the convolutional layer is as follows:
Cij＝f(w·xj：j+h-1+b)；
wherein CijA characteristic image matrix which represents the j-th foreign matter with the type i corresponding to the characteristic image matrix after the convolution operation; f (-) represents the convolutional layer convolution kernel; w represents a weight matrix of the convolutional layer filter; h represents the length of the convolutional layer filter; b represents a bias; x is the number ofj：j+h-1An image matrix representing an image of the j-th foreign object of the type i to the j + h-1-th foreign object;
the formula for pooling the characteristic image matrix output by the convolutional layer is as follows:
Figure FDA0003196659300000031
q is an odd number;
wherein the content of the first and second substances,
Figure FDA0003196659300000032
representing the characteristic value of a central pixel point of a window covering convolution layer output image with q multiplied by q dimension, and u representing the transverse coordinate of a pixel point of a window covering convolution layer output characteristic image with q multiplied by q dimension; v represents the vertical coordinate of the pixel point of the window covering convolution layer output characteristic image with q multiplied by q dimension; c (u, v) represents a characteristic value of a pixel point with q multiplied by q dimension window covering convolutional layer output characteristic image coordinates of (u, v).
2. The machine learning-based railway foreign matter identification method according to claim 1, wherein the method for establishing the foreign matter identification classification model in advance comprises the following steps:
acquiring training image sets of various foreign matters;
respectively inputting the training image set of each foreign matter into a convolutional neural network for training to obtain a foreign matter identification submodel corresponding to the foreign matter;
and fusing the plurality of foreign matter recognition submodels to form a foreign matter recognition classification model.
3. The machine learning-based railway foreign matter identification method according to claim 2, wherein the method for obtaining the foreign matter identification submodel is as follows:
inputting a training image set of foreign matters into an input layer of a convolutional neural network, and outputting an image matrix of the foreign matters by the input layer;
extracting the characteristics of the image matrix of the foreign matters from the convolution layer to obtain a characteristic image matrix of the foreign matters;
the pooling layer is used for pooling the output characteristic image matrix of the convolutional layer to obtain a key characteristic identification image matrix of the foreign matter;
inputting the key characteristic identification image matrix output by the pooling layer into a full-connection layer, and classifying the input key characteristic identification image matrix into a corresponding foreign matter type area by the full-connection layer;
and after the full connection layer classifies the characteristic image matrix into corresponding foreign matter type areas, the output layer outputs a foreign matter identification submodel of each type of foreign matter.
4. The machine learning-based railway foreign matter identification method according to claim 1, wherein the method of extracting a rail risk region detection image comprises:
identifying the shape and position of the track in the monitored image;
establishing a dangerous area foreign matter detection window on the outer periphery of the track according to the shape and the position of the track;
and dividing the image positioned outside the dangerous area foreign matter detection window, reserving the image positioned inside the dangerous area foreign matter detection window, and forming a track dangerous area detection image.
5. A railway foreign object recognition system based on machine learning, comprising:
the image acquisition module is used for acquiring a monitoring video of a track in front of a train in the running process of the train and acquiring a plurality of frames of monitoring images in the monitoring video in real time;
the image extraction module is used for establishing a dangerous area foreign matter detection window in the monitoring image and extracting a track dangerous area detection image of the monitoring image in the dangerous area foreign matter detection window;
the image preprocessing module is used for preprocessing the detection image of the rail dangerous area;
the method for preprocessing the rail dangerous area detection image comprises the following steps:
carrying out gray processing on the detection image of the rail dangerous area;
carrying out smooth filtering processing on the rail dangerous area detection image subjected to graying processing;
wherein the smoothing filter process includes: placing a k-multiplied k-dimensional window containing a weighting coefficient on the track dangerous area detection image, carrying out weighted average on the track dangerous area detection image, wherein the value of each pixel point is obtained by carrying out weighted average on the value of each pixel point and other pixel values in the neighborhood of the window, namely scanning each pixel point in the image by using the window, and replacing the value of the central pixel point of the template by using the weighted average value of the pixels in the neighborhood determined by the window;
the calculation formula of the pixel value of the image output after the filtering process is as follows:
Figure FDA0003196659300000051
wherein, p (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the output image after the filtering processing, f (x, y) represents the pixel value of the pixel with the pixel coordinate (x, y) in the track dangerous area detection image, (x, y) represents the coordinate of the pixel, and sigma represents the variance; denotes a convolution operation; e is 2.718, k represents the dimension of the window;
calculating the definition of the detected image in the rail dangerous area after the smooth filtering processing, judging whether the definition exceeds a preset qualified threshold value, if not, performing the definition processing on the image, otherwise, finishing the preprocessing;
the calculation formula of the definition of the track dangerous area detection image after the smooth filtering processing is as follows:
Figure FDA0003196659300000052
wherein Q represents definition, and p (x, y) represents a pixel value of a pixel with pixel point coordinates (x, y) in an output image after filtering processing; n represents the total row number of the output image matrix after filtering processing; m represents the total column number of the output image matrix after filtering processing;
the foreign matter identification module is used for inputting the preprocessed rail dangerous area detection image into a pre-established foreign matter identification classification model, and the foreign matter identification classification model identifies whether foreign matters exist in the rail dangerous area detection image or not;
in the process of pre-establishing a foreign matter identification classification model, selecting a foreign matter image causing an accident of a railway train for training, acquiring the foreign matter image on the railway from multiple angles at different days and different moments aiming at each foreign matter, extracting a foreign matter outline image in the foreign matter image, and forming a training image set of the foreign matter by the foreign matter outline images; respectively inputting the training image set of each foreign matter into a convolutional neural network for training to obtain a foreign matter identification submodel corresponding to the foreign matter;
wherein, the training image set of each foreign matter is respectively input into the convolutional neural network for training, which comprises the following steps: respectively and sequentially inputting the training image set of each foreign matter into an input layer, a convolution layer, a pooling layer, a full-link layer and an output layer of a convolutional neural network to obtain a foreign matter identification submodel;
the calculation formula for acquiring the characteristic image matrix of the foreign matter in the convolutional layer is as follows:
Cij＝f(w·xj：j+h-1+b)；
wherein CijA characteristic image matrix which represents the j-th foreign matter with the type i corresponding to the characteristic image matrix after the convolution operation; f (-) represents the convolutional layer convolution kernel; w represents a weight matrix of the convolutional layer filter; h represents the length of the convolutional layer filter; b represents a bias; x is the number ofj：j+h-1An image matrix representing an image of the j-th foreign object of the type i to the j + h-1-th foreign object;
the formula for pooling the characteristic image matrix output by the convolutional layer is as follows:
Figure FDA0003196659300000061
q is an odd number;
wherein the content of the first and second substances,
Figure FDA0003196659300000071
representing the characteristic value of a central pixel point of a window covering convolution layer output image with q multiplied by q dimension, and u representing the transverse coordinate of a pixel point of a window covering convolution layer output characteristic image with q multiplied by q dimension; v represents the vertical coordinate of the pixel point of the window covering convolution layer output characteristic image with q multiplied by q dimension; c (u, v) represents q times q dimension window covering convolution layer output characteristic image coordinate as (u, v) of the pixel points.
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