CN115830019B

CN115830019B - Three-dimensional point cloud calibration processing method and device for steel rail detection

Info

Publication number: CN115830019B
Application number: CN202310109398.1A
Authority: CN
Inventors: 黄碗明; 鲁鑫; 黄汝成
Original assignee: Nanjing Huiran Technology Co ltd
Current assignee: Nanjing Huiran Technology Co ltd
Priority date: 2023-02-14
Filing date: 2023-02-14
Publication date: 2023-04-28
Anticipated expiration: 2043-02-14
Also published as: CN115830019A

Abstract

The invention provides a three-dimensional point cloud calibration processing method and device for steel rail detection, wherein a point cloud acquisition device is controlled to acquire a point cloud three-dimensional image of a position of a steel rail at a preset height and a preset position, and point three-dimensional coordinates of all points in the point cloud three-dimensional image are determined; calculating according to the three-dimensional coordinates of the equipment, the distance information between the rails and the width information of the rails to obtain a target coordinate acquisition interval; determining a corresponding target area in the point cloud three-dimensional image as a steel rail detection calibration area according to the target coordinate acquisition interval, detecting and judging all the three-dimensional coordinates of points in the steel rail detection calibration area according to standard detection information, and determining an abnormal area in the steel rail detection calibration area; and generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the points in the abnormal region, combining the processing display frame with the abnormal region, displaying the combined processing display frame in the three-dimensional image of the point cloud, and generating a processing result.

Description

Three-dimensional point cloud calibration processing method and device for steel rail detection

Technical Field

The invention relates to the technical field of data processing, in particular to a three-dimensional point cloud calibration processing method and device for steel rail detection.

Background

Along with the high-speed development of Chinese economic construction, the modern high-speed running capability and town construction lead population to be more concentrated, high-speed rail transportation and urban traffic pressure to be rapidly increased, and the construction of rail traffic facilities is actively carried out in the country, so that high-speed rail, subway and urban rail traffic networks with the total number of hundreds of thousands of kilometers are constructed at present, and train running safety is the primary problem of rail traffic. The steel rail is used as the most basic part in the track, and the working state of the steel rail is directly related to train running safety; the rail detection is mainly to detect the state of the rail by means of detection equipment manually, and mainly to detect the defects of corrosion, cracks, pits, bulges and the like of the steel rail.

In the prior art, inspection is carried out on the surface of a steel rail mainly through manual utilization of detection equipment, and compared with three-dimensional point cloud data, the inspection accuracy is not high, the manual continuous inspection efficiency is quite low, and great potential safety hazards exist.

Therefore, how to provide an efficient and high-accuracy automatic steel rail detection method is a problem to be solved.

Disclosure of Invention

The embodiment of the invention provides a three-dimensional point cloud calibration method and device for steel rail detection, which can automatically perform de-duplication processing on data outside a steel rail, automatically detect the steel rail by utilizing the three-dimensional point cloud data and automatically display related problems, and better improve accuracy and efficiency.

In a first aspect of the embodiment of the present invention, a method for processing three-dimensional point cloud calibration for rail detection is provided, including:

the method comprises the steps that a point cloud acquisition device is controlled to acquire a point cloud three-dimensional image of a position where a steel rail is located at a preset height and a preset position, and point three-dimensional coordinates of all points in the point cloud three-dimensional image are determined;

receiving dimension data configured by a user on a steel rail, wherein the dimension data at least comprises rail distance information and steel rail width information, and calculating according to equipment three-dimensional coordinates, the rail distance information and the steel rail width information to obtain a target coordinate acquisition interval;

determining a corresponding target area in the point cloud three-dimensional image as a steel rail detection calibration area according to the target coordinate acquisition interval, detecting and judging three-dimensional coordinates of all points in the steel rail detection calibration area according to standard detection information, and determining an abnormal area in the steel rail detection calibration area;

and generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the points in the abnormal area, combining the processing display frame with the abnormal area, displaying the combined processing display frame and the abnormal area in the three-dimensional image of the point cloud, and generating a processing result.

Optionally, in one possible implementation manner of the first aspect, the controlling the point cloud collecting device collects a point cloud three-dimensional image of a position where the steel rail is located at a preset height and a preset position, and determining point three-dimensional coordinates of all points in the point cloud three-dimensional image includes:

Controlling the point cloud acquisition equipment to take the middle point between two steel rails as a preset position at a preset height, and controlling the point cloud acquisition equipment to downwards acquire a point cloud three-dimensional image corresponding to the steel rails after judging that the point cloud acquisition equipment reaches the preset height and the preset position;

and constructing a three-dimensional coordinate system by taking the point cloud acquisition equipment as a coordinate origin, and determining the point three-dimensional coordinates of all points in the point cloud three-dimensional image.

Optionally, in one possible implementation manner of the first aspect, the receiving dimension data configured by the user on the steel rail, where the dimension data includes at least information of a distance between the steel rails and information of a width of the steel rail, and calculating according to the three-dimensional coordinates of the device, the information of the distance between the steel rails and the information of the width of the steel rails, to obtain a target coordinate acquisition interval includes:

calculating a half distance value corresponding to the distance information between the rails, and extending the half distance value to the positive and negative sides of the X axis by taking the abscissa of the three-dimensional coordinate of the equipment as a starting point to obtain a corresponding positive starting X point coordinate and a negative starting X point coordinate;

respectively determining coordinate points in the positive direction and the negative direction corresponding to the positive starting X point coordinate and the negative starting X point coordinate as starting points according to the width information of the steel rail to obtain a positive ending X point coordinate and a negative ending X point coordinate;

And counting all X-axis coordinate points from the positive initial X-point coordinates to the positive ending X-point coordinates to obtain a positive X-axis coordinate interval, counting all X-axis coordinate points from the negative initial X-point coordinates to the negative ending X-point coordinates to obtain a negative X-axis coordinate interval, and obtaining a target coordinate acquisition interval according to the positive X-axis coordinate interval and the negative X-axis coordinate interval.

Optionally, in one possible implementation manner of the first aspect, the determining, according to the target coordinate acquisition interval, a corresponding target area in the point cloud three-dimensional image as a rail detection calibration area, detecting and judging, according to standard detection information, three-dimensional coordinates of all points in the rail detection calibration area, and determining an abnormal area in the rail detection calibration area includes:

respectively comparing X-axis coordinates in the three-dimensional coordinates of all points in the three-dimensional image of the point cloud with a target coordinate acquisition interval, and determining the three-dimensional coordinates of the points with the X-axis coordinates in the target coordinate acquisition interval as target points;

counting a target area formed by a target point corresponding to positive X-axis coordinates as a positive rail detection calibration area, and counting a target area formed by a target point corresponding to negative X-axis coordinates as a negative rail detection calibration area;

And retrieving standard detection information corresponding to the steel rail, determining Z-axis coordinates of all target points in the steel rail detection calibration area, comparing the Z-axis coordinates with the standard detection information to carry out detection judgment, and determining an abnormal area in the steel rail detection calibration area.

Optionally, in one possible implementation manner of the first aspect, the retrieving standard detection information corresponding to the steel rail, determining Z-axis coordinates of all target points in the steel rail detection calibration area, comparing the Z-axis coordinates with the standard detection information to perform detection judgment, and determining an abnormal area in the steel rail detection calibration area includes:

comparing the Z-axis coordinate of each target point with the standard Z-axis coordinate in the standard detection information to obtain a Z-axis coordinate difference value, and judging the target point corresponding to the corresponding Z-axis coordinate as an abnormal target point if the Z-axis coordinate difference value is larger than a preset coordinate difference value;

if the Z-axis coordinate difference value is smaller than or equal to the preset coordinate difference value, judging the target point corresponding to the corresponding Z-axis coordinate as a normal target point;

calculating the distance between any two abnormal target points to obtain the distance between the target points, and classifying all the abnormal target points with the distance between the target points smaller than the preset distance between the target points into the same abnormal area.

Optionally, in one possible implementation manner of the first aspect, before determining, according to the target coordinate acquisition interval, a corresponding target area in the point cloud three-dimensional image as a rail detection calibration area, detecting and judging, according to standard detection information, three-dimensional coordinates of all points in the rail detection calibration area, and determining an abnormal area in the rail detection calibration area, the method further includes:

acquiring preset widths of inclined surfaces on two sides of a steel rail, and respectively determining coordinate points in the corresponding positive direction and negative direction by taking positive starting X point coordinates and negative starting X point coordinates as starting points according to the preset widths to obtain first positive X point removing coordinates and first negative X point removing coordinates;

respectively determining coordinate points in the corresponding negative direction and positive direction by taking the positive termination X point coordinate and the negative termination X point coordinate as starting points according to the preset width to obtain a second positive removal X point coordinate and a second negative removal X point coordinate;

and counting all X-axis coordinate points of the first positive removal X-point coordinates and the second positive removal X-point coordinates to obtain positive X-axis coordinate intervals, counting all X-axis coordinate points of the first negative removal X-point coordinates and the second negative removal X-point coordinates to obtain negative X-axis coordinate intervals, and obtaining a target coordinate acquisition interval according to the positive X-axis coordinate intervals and the negative X-axis coordinate intervals.

Optionally, in one possible implementation manner of the first aspect, the calculating a distance between any two abnormal target points to obtain a distance between the target points, classifying all the abnormal target points with the distance between the target points being smaller than the preset distance between the target points into the same abnormal area includes:

adding different area marks to all abnormal target points, and calculating the distance between any two abnormal target points to obtain the distance between the target points;

synchronously modifying the area marks of the abnormal target points with the distance between the target points smaller than the distance between preset points in a way of copying the information of the area marks of other abnormal target points and adding the copied information into the information of the corresponding area marks;

if judging that the distance between the abnormal target point and the other abnormal target points is smaller than the preset distance, synchronously modifying the corresponding area marks of the abnormal target point and the other abnormal target points;

after judging that the area marks of all the abnormal target points are respectively and synchronously modified, classifying the abnormal target points with the same area marks into the same abnormal area.

Optionally, in one possible implementation manner of the first aspect, the generating a corresponding processing display frame according to the attribute of the three-dimensional coordinate of the point in the abnormal area, and combining the processing display frame with the abnormal area to display and generate a processing result in the three-dimensional image of the point cloud includes:

determining the attribute of the three-dimensional coordinates of points in each abnormal area, wherein the attribute of the three-dimensional coordinates of points comprises the number of all abnormal target points and the three-dimensional coordinates of each abnormal target point;

extracting an X-axis extremum and a Y-axis extremum in three-dimensional coordinates of an abnormal target point corresponding to each abnormal region, and determining two groups of X-axis parallel lines and Y-axis parallel lines according to the X-axis extremum and the Y-axis extremum;

taking a frame line formed by the two groups of X-axis parallel lines and Y-axis parallel lines as a processing display frame, obtaining an X-axis distance and a Y-axis distance of the processing display frame according to the X-axis extreme value and the Y-axis extreme value, and calculating according to the X-axis distance, the Y-axis distance and the number of three-dimensional coordinates of all points to obtain the display color of the processing display frame;

and determining the center point of the processing display frame and the center point of the abnormal area, overlapping the center point of the processing display frame and the center point of the abnormal area, so that the processing display frame and the abnormal area are combined to be displayed in the point cloud three-dimensional image, and generating a processing result, wherein the processing result comprises the number of the three-dimensional coordinates of the point.

Optionally, in one possible implementation manner of the first aspect, the frame line formed according to the two sets of X-axis parallel lines and Y-axis parallel lines is used as a processing display frame, an X-axis distance and a Y-axis distance of the processing display frame are obtained according to the X-axis extremum and the Y-axis extremum, and a display color of the processing display frame is obtained by calculating according to the X-axis distance, the Y-axis distance and the number of three-dimensional coordinates of all points, including:

determining a frame line formed by the intersection area of two groups of X-axis parallel lines and Y-axis parallel lines as a processing display frame, and amplifying the processing display frame by preset times;

calculating according to the X-axis extreme value, the Y-axis extreme value and the preset multiple to obtain an amplified X-axis distance and a amplified Y-axis distance of the processing display frame, and obtaining the area of the processing display frame according to the amplified X-axis distance and the amplified Y-axis distance;

obtaining three-dimensional coordinate density according to the area of the display frame and the number of the three-dimensional coordinates of the points, comparing the three-dimensional coordinate density with preset density intervals to obtain corresponding display colors, calculating the three-dimensional coordinate density through the following formula,

wherein ,

for three-dimensional coordinate density>

For the number of abnormal target points +. >

Is the maximum in the X-axis extremum, < ->

Is the minimum value in the X-axis extreme value, +.>

Is a preset multiple of->

Is the maximum in the Y-axis extremum, < +.>

Is the minimum value in the Y-axis extremum.

In a second aspect of the embodiment of the present invention, a three-dimensional point cloud calibration processing apparatus for detecting a steel rail is provided, including:

the control module is used for controlling the point cloud acquisition equipment to acquire a point cloud three-dimensional image of the position of the steel rail at a preset height and a preset position and determining the point three-dimensional coordinates of all points in the point cloud three-dimensional image;

the calculation module is used for receiving dimension data configured by a user on the steel rail, wherein the dimension data at least comprises steel rail distance information and steel rail width information, and calculating is carried out according to the equipment three-dimensional coordinates, the steel rail distance information and the steel rail width information to obtain a target coordinate acquisition interval;

the determining module is used for determining a corresponding target area in the point cloud three-dimensional image as a steel rail detection calibration area according to the target coordinate acquisition interval, detecting and judging all the three-dimensional coordinates of points in the steel rail detection calibration area according to standard detection information, and determining an abnormal area in the steel rail detection calibration area;

and the generation module is used for generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the points in the abnormal area, combining the processing display frame with the abnormal area, displaying the combined processing display frame in the three-dimensional image of the point cloud, and generating a processing result.

In a third aspect of embodiments of the present invention, there is provided a storage medium having stored therein a computer program for implementing the method of the first aspect and the various possible designs of the first aspect when the computer program is executed by a processor.

According to the three-dimensional point cloud calibration processing method and device for steel rail detection, point cloud three-dimensional images at the steel rail are collected by utilizing point cloud collection equipment, a corresponding three-dimensional coordinate system is established in each point cloud three-dimensional image, and the point cloud three-dimensional images are subjected to de-duplication processing according to the three-dimensional coordinate system, the distance information between the steel rails and the width information of the steel rail to obtain a corresponding target coordinate collection interval (steel rail), so that sundries except the steel rail are automatically removed, and compared with a neural network, the de-duplication mode is directly processed by adopting the point cloud collection equipment through coordinate values, so that the working efficiency is improved without training in advance; and comparing the three-dimensional coordinate values of all points in the target coordinate acquisition interval with the standard in the standard detection information to determine an abnormal region (crack, dent and the like), generating a corresponding processing display frame at the abnormal region, and enabling a user to more intuitively determine and position the abnormal region through the processing display frame.

According to the technical scheme, corresponding point cloud acquisition equipment can be carried on equipment such as an unmanned plane to acquire a point cloud three-dimensional image in the middle of a steel rail, a coordinate system is established by taking a laser emission point of the point cloud acquisition equipment as a central point, so that coordinate values of all points in the point cloud three-dimensional image are obtained, starting point coordinate points (positive termination X point coordinates and negative termination X point coordinates) on the inner side of the steel rail are respectively positioned according to the distance information between the steel rails, ending point coordinate points (positive termination X point coordinates and negative termination X point coordinates) on the outer side of the steel rail are determined according to the width information of the steel rail, the positive termination X point coordinates and the negative termination X point coordinates, and a coordinate interval of the upper surface of the steel rail to be acquired is determined according to the starting point coordinate points on the inner side of the steel rail and the ending point coordinate points on the outer side of the steel rail.

According to the technical scheme provided by the invention, the coordinate values of all coordinate points on the surface of the steel rail, namely the coordinate values of the target points, are determined through the target coordinate acquisition interval, the coordinate value of the Z axis in each coordinate value is differentiated from the coordinate value of the Z axis in the corresponding standard value (standard detection information) to obtain the Z axis coordinate difference value, so that the abnormal region (concave, crack and the like) in the steel rail is determined according to the Z axis coordinate difference value and the comparison preset coordinate difference value; if the steel rail has corresponding inclined surfaces on two sides, the method and the device can remove the steel rail by utilizing a coordinate system according to the preset width corresponding to the inclined surfaces, only keep the plane of the steel rail, then compare and determine abnormal areas (pits, cracks and the like) in the steel rail by utilizing the coordinate value of a Z axis, compare the distance between target points of any two points with the distance between preset points, and synchronously modify the area marks into the same area marks if the distance between the target points is smaller than the distance between the target points, so that the area marks are classified according to the area marks, and realize automatic positioning of the abnormal areas.

According to the technical scheme provided by the invention, different processing display frames can be automatically generated according to the different sizes of abnormal areas (pits, cracks and the like) to display, so that a user can observe conveniently, and the processing display frames with different colors can be processed according to the areas of the processing display frames and the number of abnormal target points to obtain the three-dimensional coordinate density.

Drawings

FIG. 1 is a flow chart of a three-dimensional point cloud calibration processing method for steel rail detection provided by the invention;

FIG. 2 is a schematic view of an abnormal region of a rail according to the present invention;

FIG. 3A is a schematic view of a rail plane provided by the present invention;

FIG. 3B is a schematic view of a rail inclined surface according to the present invention;

fig. 4 is a schematic structural diagram of a three-dimensional point cloud calibration processing device for steel rail detection.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.

It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present invention, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.

It should be understood that in the present invention, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.

As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.

The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.

The invention provides a three-dimensional point cloud calibration processing method for steel rail detection, which is shown in fig. 1 and comprises the following steps of S1 to S5:

s1, controlling point cloud acquisition equipment to acquire point cloud three-dimensional images of positions of the steel rail at preset heights and preset positions, and determining point three-dimensional coordinates of all points in the point cloud three-dimensional images.

The point cloud acquisition equipment can be point cloud acquisition equipment carried by an unmanned aerial vehicle, and the point cloud data is acquired through laser emitted by the point cloud acquisition equipment.

The preset height is preset in advance according to the actual situation, for example, 1 meter, 2 meters and the like, and is not too high due to the fact that defects need to be detected, and the accuracy of acquisition is related to the quality degree and the preset height of the point cloud acquisition equipment. The preset position is manually preset in advance according to the actual situation, and can be the position between two steel rails.

It can be understood that the server controls the point cloud acquisition device to acquire three-dimensional coordinates of all objects at the steel rail at a preset height and a preset position so as to obtain a three-dimensional image.

In some embodiments, the step S1 (controlling the point cloud collecting device to collect the point cloud three-dimensional image of the position of the steel rail at the preset height and the preset position and determining the point three-dimensional coordinates of all points in the point cloud three-dimensional image) includes S11-S12:

and S11, controlling the point cloud acquisition equipment to take the middle point between the two steel rails as a preset position at a preset height, and controlling the point cloud acquisition equipment to downwards acquire the point cloud three-dimensional image corresponding to the steel rails after judging that the point cloud acquisition equipment reaches the preset height and the preset position.

It can be understood that the midpoint between the two steel rails is taken as a preset position, and it is to be noted that the middle position of the sliding rail can be connected with the unmanned aerial vehicle by arranging the sliding rail between the two steel rails, so that the laser emission point in the point cloud three-dimensional image acquired by the unmanned aerial vehicle is ensured to be positioned at the middle position of the steel rail, and the method is not limited.

When the server judges that the point cloud acquisition equipment corresponding to the unmanned aerial vehicle reaches the corresponding preset height and the preset position, the point cloud acquisition equipment is controlled to acquire images of the steel rail below, so that a point cloud three-dimensional image is obtained.

And S12, constructing a three-dimensional coordinate system by taking the point cloud acquisition equipment as a coordinate origin, and determining the point three-dimensional coordinates of all points in the point cloud three-dimensional image.

It can be understood that the point cloud acquisition device is provided with a corresponding laser emission port, the laser emission port is used as a coordinate origin in the three-dimensional image so as to establish a corresponding three-dimensional coordinate system, and coordinate values of all points in the image can be determined according to the point cloud three-dimensional image and the three-dimensional coordinate system.

For example: the length of the acquisition steel rail set by the point cloud acquisition equipment can be 1 meter, so that three-dimensional images corresponding to 1 meter are obtained, and each three-dimensional image is provided with a coordinate origin corresponding to the three-dimensional image.

And S2, receiving dimension data configured by a user on the steel rail, wherein the dimension data at least comprises rail distance information and steel rail width information, and calculating according to the equipment three-dimensional coordinates, the rail distance information and the steel rail width information to obtain a target coordinate acquisition interval.

The steel rail distance information is distance information between two steel rails, and the steel rail width information is width information of each steel rail; it will be appreciated that the distance information and the width information between the rails are fixed.

It can be understood that the three-dimensional coordinates of the device are three-dimensional coordinates established by establishing a coordinate system by taking a laser emission port of the device as a coordinate origin, and the coordinate system is positioned between two steel rails according to the coordinate origin, and the distance information between the steel rails and the width information of the steel rails are fixed, so that a corresponding target coordinate acquisition interval (the coordinate interval rail of the steel rails) can be obtained.

Through the mode, other sundries except the steel rail can be removed, for example: compared with the prior art, the method has the advantages that a large amount of training is not needed, and the steel rail selection is automatically performed relative to manual detection, so that the treatment efficiency is improved.

In some embodiments, the step S2 (receiving the dimension data configured by the user for the steel rail, where the dimension data includes at least the rail distance information and the steel rail width information, and calculating according to the equipment three-dimensional coordinates, the rail distance information and the steel rail width information to obtain the target coordinate acquisition interval) includes S21-S23:

s21, calculating a half distance value corresponding to the distance information between the rails, and extending the half distance value to the positive side and the negative side of the X axis by taking the abscissa of the three-dimensional coordinate of the equipment as a starting point to obtain a corresponding positive starting X point coordinate and a negative starting X point coordinate.

The origin of coordinates of the three-dimensional coordinate system is located at the middle position of the steel rail, and corresponding positive start X point coordinates and negative start X point coordinates (start point coordinates on the inner side of the steel rail) can be determined according to one half of the distance information on the premise that the distance information between the steel rails is known.

And S22, respectively determining coordinate points in the corresponding positive direction and negative direction by taking positive starting X point coordinates and negative starting X point coordinates as starting points according to the width information of the steel rail to obtain positive ending X point coordinates and negative ending X point coordinates.

It will be appreciated that after the start point coordinates of the rail inner side are determined, the positive termination X point coordinates and the negative termination X point coordinates (end point coordinates of the rail outer side) of the two rails can be determined based on the rail width information inherent to the rails.

The corresponding 2 steel rails are conveniently and subsequently determined according to the initial point coordinates of the inner side of the steel rail and the end point coordinates of the outer side of the steel rail.

And S23, counting all X-axis coordinate points from positive starting X-point coordinates to positive ending X-point coordinates to obtain a positive X-axis coordinate interval, counting all X-axis coordinate points from negative starting X-point coordinates to negative ending X-point coordinates to obtain a negative X-axis coordinate interval, and obtaining a target coordinate acquisition interval according to the positive X-axis coordinate interval and the negative X-axis coordinate interval.

The positive X-axis coordinate section is a section generated by all X coordinate values of the rail in the positive X-axis direction of the three-dimensional coordinate system, and the negative X-axis coordinate section is a section generated by all X coordinate values of the rail in the negative X-axis direction of the three-dimensional coordinate system.

The target coordinate acquisition section is a section generated by all X coordinate values of the steel rail in the positive direction and the negative direction of the X axis of the three-dimensional coordinate system.

And S3, determining a corresponding target area in the point cloud three-dimensional image as a steel rail detection calibration area according to the target coordinate acquisition interval, detecting and judging three-dimensional coordinates of all points in the steel rail detection calibration area according to standard detection information, and determining an abnormal area in the steel rail detection calibration area.

It is understood that all coordinate values of the rail on all X-axes in the three-dimensional coordinate system are known, all three-dimensional coordinate values corresponding to the coordinate values on the X-axes can be determined according to all coordinate values on the X-axes, and the area generated by the three-dimensional coordinate values is taken as a rail detection calibration area.

By the method, the coordinate points of the steel rail detection calibration area are compared with all standard coordinate points in the standard detection information, so that the coordinate points with problems are directly positioned.

In some embodiments, before step S3 (determining, according to the target coordinate acquisition interval, a corresponding target area in the point cloud three-dimensional image as a rail detection calibration area, performing detection and judgment on three-dimensional coordinates of all points in the rail detection calibration area according to standard detection information, and determining an abnormal area in the rail detection calibration area), the method further includes A1-A3:

A1, acquiring preset widths of inclined surfaces on two sides of a steel rail, and respectively determining coordinate points in the corresponding positive direction and negative direction by taking positive starting X point coordinates and negative starting X point coordinates as starting points according to the preset widths to obtain first positive X point removing coordinates and first negative X point removing coordinates.

The steel rail has 2 types, one is a smooth plane steel rail, and the other is a trapezoid steel rail consisting of a smooth plane and 2 inclined planes.

It can be understood that if the steel rail is a trapezoid steel rail formed by a smooth plane and 2 inclined planes, the preset width, positive initial X point coordinates and negative initial X point coordinates of the inclined planes at two sides of the steel rail are obtained, so that corresponding first positive X point removal coordinates and first negative X point removal coordinates (points at the inner side of the smooth plane) at the steel rail are positioned, and the subsequent points at the outer side of the smooth plane can conveniently determine the coordinate set of the upper surface of the steel rail.

And A2, respectively determining coordinate points in the corresponding negative direction and positive direction by taking the positive termination X point coordinate and the negative termination X point coordinate as starting points according to the preset width to obtain a second positive removal X point coordinate and a second negative removal X point coordinate.

It can be understood that if the steel rail is a trapezoid steel rail formed by a smooth plane and 2 inclined planes, the preset width, positive termination X point coordinates and negative termination X point coordinates of the inclined planes at two sides of the steel rail are obtained, so that two corresponding positive removal X point coordinates and a second negative removal X point coordinates (points outside the smooth plane) at the steel rail are positioned.

And A3, counting all X-axis coordinate points of the first positive removal X-point coordinates and the second positive removal X-point coordinates to obtain positive X-axis coordinate intervals, counting all X-axis coordinate points of the first negative removal X-point coordinates and the second negative removal X-point coordinates to obtain negative X-axis coordinate intervals, and obtaining a target coordinate acquisition interval according to the positive X-axis coordinate intervals and the negative X-axis coordinate intervals.

It will be appreciated that the coordinate interval (target coordinate acquisition interval) of the smoothed plane at the upper surface of each rail can be determined from the points outside the smoothed plane and the points inside the smoothed plane at each rail.

Through the mode, the inclined surface at the steel rail can be removed.

In some embodiments, the step S3 (determining, according to the target coordinate acquisition interval, a corresponding target area in the point cloud three-dimensional image as a rail detection calibration area, performing detection and judgment on three-dimensional coordinates of all points in the rail detection calibration area according to standard detection information, and determining an abnormal area in the rail detection calibration area) includes S31-S33:

S31, respectively comparing X-axis coordinates in the three-dimensional coordinates of all points in the three-dimensional image of the point cloud with the target coordinate acquisition interval, and determining the three-dimensional coordinates of the points with the X-axis coordinates in the target coordinate acquisition interval as target points.

It can be understood that the interval generated by all coordinate points on the corresponding X axis can be determined based on the width and the distance of the steel rail, and the point three-dimensional coordinates consistent with the interval generated by all coordinate points on the X axis in the point cloud three-dimensional image are determined as the target points according to the point three-dimensional coordinates of all points in the point cloud three-dimensional image.

S32, counting a target area formed by a target point corresponding to positive X-axis coordinates as a positive rail detection calibration area, and counting a target area formed by a target point corresponding to negative X-axis coordinates as a negative rail detection calibration area.

It can be understood that the X-axis coordinate direction in the three-dimensional coordinate system has both a positive direction and a negative direction; and counting all X coordinate values of the steel rail in the positive direction of the X axis as a positive steel rail detection and calibration area, and counting all X coordinate values of the steel rail in the negative direction of the X axis as a negative steel rail detection and calibration area.

S33, standard detection information corresponding to the steel rail is called, Z-axis coordinates of all target points in the steel rail detection and calibration area are determined, the Z-axis coordinates are compared with the standard detection information for detection and judgment, and abnormal areas in the steel rail detection and calibration area are determined.

The steel rail detection calibration area is an area of the steel rail in a three-dimensional space. The Z-axis coordinates of all target points (all rail surfaces) in the rail detection calibration area (rail) are determined, and it is understood that the actual Z-axis coordinates are the rail surfaces.

It can be understood that the data of both steel rails are standard and consistent in load, and standard detection information (standard coordinate values) corresponding to the steel rails is retrieved.

And comparing the actual Z-axis coordinate of the rail surface with the Z-axis coordinate in the standard detection information (standard coordinate value), thereby determining an abnormal region in the steel rail.

In some embodiments, the step S33 (retrieving standard detection information corresponding to the steel rail, determining Z-axis coordinates of all target points in the steel rail detection calibration area, comparing the Z-axis coordinates with the standard detection information to perform detection judgment, and determining an abnormal area in the steel rail detection calibration area) includes S331-S333:

s331, comparing the Z-axis coordinate of each target point with the standard Z-axis coordinate in the standard detection information to obtain a Z-axis coordinate difference value, and if the Z-axis coordinate difference value is larger than the preset coordinate difference value, judging the target point corresponding to the corresponding Z-axis coordinate as an abnormal target point.

Wherein the target point is an actual coordinate point at the steel rail. It is understood that the Z-axis coordinates of the rail surface change when a large crack or dent occurs in the rail.

Therefore, the Z-axis coordinate difference value is obtained by comparing the Z-axis coordinate at the steel rail with the standard Z-axis coordinate in the standard detection information, and it can be understood that a three-dimensional coordinate system is established, and the Z-axis coordinate at the target point becomes larger when cracks, pits and the like appear on the surface of the steel rail in the forward direction of the Z-axis downwards, so that the Z-axis coordinate difference value after the difference is larger than 0; the preset coordinate difference may be a coordinate difference preset manually according to actual practice, may be 0, etc., and is not limited herein.

It can be understood that when the difference value of the Z-axis coordinates is equal to 0, it indicates that the rail is not changed as a normal target point, and when the difference value of the Z-axis coordinates is greater than 0, it indicates that the rail is changed as an abnormal target point.

And S332, if the Z-axis coordinate difference value is smaller than or equal to the preset coordinate difference value, judging the target point corresponding to the corresponding Z-axis coordinate as a normal target point.

It can be understood that if the Z-axis coordinate difference is less than or equal to the preset coordinate difference, it is indicated that the rail at the position has no defects such as pits and cracks for long-term use.

S333, calculating the distance between any two abnormal target points to obtain the distance between the target points, and classifying all the abnormal target points with the distance between the target points smaller than the preset distance between the target points into the same abnormal area.

It will be appreciated that the crack, dent, etc. defect is often an abnormal region composed of a plurality of abnormal target points.

Therefore, the invention calculates the distance between any two abnormal target points, when the distance between the two abnormal target points is smaller than the distance between preset points, the two abnormal target points are close together by default, and the two abnormal target points are classified into the same abnormal area. The distance between preset points may be a distance preset in advance by a person, for example, 0.01, etc., which is not limited herein.

In some embodiments, step S333 (calculating the distance between any two abnormal target points to obtain the inter-target point distance, classifying all the abnormal target points with the inter-target point distances smaller than the preset inter-target point distance into the same abnormal region) includes S3331-S3334:

s3331, adding different area marks to all abnormal target points, and calculating the distance between any two abnormal target points to obtain the distance between the target points.

The method can add corresponding different area marks to all the abnormal target points, so that different abnormal target points can be conveniently distinguished, the distance between any two points is calculated, and whether the abnormal target points are in the same area or not, such as whether the abnormal target points are in the same pit or crack or not, can be conveniently judged according to the distance between the two points.

For example: abnormal target point: A. b, C, D, E, etc. may be 1, 2, 3, etc., and is not limited herein, and may be used to distinguish different abnormal target points and calculate the inter-target point distance between any two points.

S3332, synchronously modifying the area marks of all abnormal target points with the distance between the target points smaller than the distance between preset points in a way of copying the information of the area marks of other abnormal target points and adding the copied information into the information of the corresponding area marks.

It can be understood that when the distance between the target points is smaller than the preset distance, it indicates that the two target points are close to each other and are the same defect, and the area marks of the two abnormal target points are synchronously modified in such a way that 2 target points copy each other's area marks and are put in the information of the corresponding area marks again.

For example: if the inter-target point distance between the abnormal target point a and the abnormal target point B is smaller than the preset inter-target point distance, the abnormal target point a copies the area mark B of the abnormal target point B, the abnormal target point B copies the area mark a of the abnormal target point a, both of which are the area marks of AB, and replaces the area mark with the original A, B.

The server can directly judge whether the abnormal part is the same area through the area mark, compared with the prior art, the method has the advantages that the image defect identification and treatment are carried out, and the classification is unified, so that the treatment efficiency is better improved, and the method is simpler and faster.

S3333, if it is determined that the distance between the abnormal target point and the other abnormal target points is smaller than the preset distance, synchronously modifying the corresponding area marks of the abnormal target point and the other abnormal target points.

It will be appreciated that the abnormal target point may be fused with other regions of multiple abnormal target points to form a complete abnormal region.

For example: the abnormal target point C and the abnormal target point AB (an abnormal part composed of the abnormal target point a and the abnormal target point B), and if the inter-target point distance of one of the abnormal target points C and the abnormal target point AB is smaller than the preset inter-point distance, the inter-target point C and the abnormal target point AB are duplicated to obtain the region mark: ABC such that the region labels for the 3 points are all ABC.

The server can directly perform classification unification of the area marks in a data processing mode, and can realize identification of the same area in an area mark identification mode, so that the working efficiency is improved better

S3334, after judging that the area marks of all the abnormal target points are respectively and synchronously modified, classifying the abnormal target points with the same area marks into the same abnormal area.

It can be understood that after all abnormal target points are synchronously modified, the server can directly classify the abnormal target points according to the region marks, and classify the region marks into the same type.

In other embodiments, the step S33 (retrieving standard detection information corresponding to the steel rail, determining Z-axis coordinates of all target points in the steel rail detection calibration area, comparing the Z-axis coordinates with the standard detection information to perform detection judgment, and determining an abnormal area in the steel rail detection calibration area) includes S334-S337:

s334, counting the points of 3 adjacent X-axis coordinates and/or Y-axis coordinates in the steel rail detection calibration area to obtain a plurality of detection sets, and calculating the distance between the middle point and the two sides points in each detection set in the Z axis to obtain a first distance value and a second distance value.

It will be appreciated that rails can be divided into rails having inclined surfaces on both sides and rails having no inclined surface; the invention checks through the form of 3 adjacent X-axis coordinates and/or Y-axis coordinates, so that the inclined plane is not required to be removed.

It should be noted that, the present invention selects any 3 adjacent target points of X-axis coordinates and/or Y-axis coordinates in the steel rail area, and may perform a check in a horizontal direction or a vertical direction, and calculates the distance from the adjacent points on both sides to the middle point, thereby obtaining a first distance value and a second distance value.

S335, extracting a first standard distance value and a second standard distance value from the standard detection information, and judging that a target point corresponding to a point in the middle of the checking set is an abnormal target point if the absolute values of the first distance value and the second distance value are respectively larger than the first standard distance value and the absolute value of the difference value between the first distance value and the second distance value is smaller than the second standard distance value.

It will be appreciated that if the points on the middle and both sides of the inspection set are at a distance (drop) in the Z-axis direction, then the explanation here may be that the recess is likely to be an inclined surface; that is, if the absolute values of the first distance value and the second distance value are respectively greater than the first standard distance value, the first distance value and the second distance value may be concave at this time, and may also be inclined; the first standard distance value is a standard preset distance value, and the first standard distance value may be 0, which is not limited herein.

When the absolute value of the difference between the first distance value and the second distance value is smaller than the second standard distance value, determining the area as a concave area; the second standard distance value is similarly 0.01, and is specifically set according to practical situations to be a smaller allowable error value.

As shown in fig. 2, for example: checking three points of the set A, B, C, wherein the coordinate value of the Z axis in the point A is 2, the coordinate value of the Z axis in the point C is 2, the coordinate value of the Z axis in the point B is 2.2, the difference value from the point A to the point B is 2-2.2= -0.2, the difference value from the point C to the point B is 2-2.2= -0.2, and the absolute values of the two are larger than 0, but the difference (-0.2) - (-0.2) = 0 between the first distance value and the second distance value is smaller than the corresponding second standard distance value, and determining as abnormal parts such as a dent or a crack.

S336, if the first distance value and the second distance value are respectively smaller than or equal to the first standard distance value, or the absolute value of the difference value between the first distance value and the second distance value is larger than or equal to the second standard distance value, judging the target point corresponding to the middle point in the checking set as a normal target point.

It can be understood that when the first distance value and the second distance value from the two side points to the middle point are respectively smaller than or equal to the first standard distance value, it is indicated that the 3 points are all on the same horizontal plane; or the difference value between the first distance value and the second distance value is larger than or equal to the second standard distance value, if the difference value is larger than or equal to the second standard distance value, the 3 points are at the inclined plane, and if the corresponding target point of the middle point in the checking set is determined to be a normal target point, namely, no corresponding defect exists.

For example: as shown in fig. 3A, three points of the inspection set D, E, F are inspected, wherein the coordinate value of the Z axis in the D point is 2, the coordinate value of the Z axis in the E point is 2,F, the coordinate value of the Z axis in the E point is 2, the difference value from the D point to the E point is 0,F, and the difference value from the E point is 0, so that the 3 points in the inspection set are all on the same horizontal plane; as shown in fig. 3B, three points of the check set G, H, I are normal points of the inclined plane, where the coordinate value of the Z axis in the G point is 2.2, the coordinate value of the Z axis in the H point is 2.1, the coordinate value of the Z axis in the i point is 2, the difference from the i point to the H point is 2-2.1= -0.1, the difference from the G point to the H point is 2.2-2.1=0.1, and the absolute value of the difference between the two is 0.1- (-0.1) =0.2 and is greater than 0, so that the target point corresponding to the point in the middle of the check set is the normal target point.

S337, calculating the distance between any two abnormal target points to obtain the distance between the target points, and classifying all the abnormal target points with the distance between the target points smaller than the preset distance between the target points into the same abnormal region.

It can be understood that, when determining the abnormal target point, the abnormal target point needs to be fused to the corresponding abnormal region in life, the principle of the fusion mode is consistent, and the fusion is performed by adopting region marks, which is not described herein.

And S4, generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the points in the abnormal area, combining the processing display frame and the abnormal area, displaying the combined processing display frame and the abnormal area in the point cloud three-dimensional image, and generating a processing result.

The processing display frame is a to-be-processed display frame generated according to the abnormal region, and it can be understood that the server generates the corresponding processing display frame according to the abnormal region for highlighting, so that a user can conveniently observe a defective region directly.

By the mode, the abnormal area is placed in the processing display frame and displayed in the point cloud three-dimensional image, so that direct observation processing by a later-stage user is facilitated.

In some embodiments, the step S4 (generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the point in the abnormal area, and combining the processing display frame with the abnormal area to display in the three-dimensional image of the point cloud and generating a processing result) includes S41-S44:

s41, determining the attribute of the three-dimensional coordinates of the points in each abnormal area, wherein the attribute of the three-dimensional coordinates of the points comprises the number of all abnormal target points and the three-dimensional coordinates of each abnormal target point.

It can be understood that the server determines the number of abnormal target points in each abnormal region and the three-dimensional coordinates of each abnormal target point, so that the corresponding processing display frame can be conveniently generated by using the number of abnormal target points and the three-dimensional coordinates of each abnormal target point.

S42, extracting an X-axis extremum and a Y-axis extremum in the three-dimensional coordinates of the abnormal target point corresponding to each abnormal region, and determining two groups of X-axis parallel lines and Y-axis parallel lines according to the X-axis extremum and the Y-axis extremum.

It can be understood that the corresponding parallel lines can be determined by acquiring the maximum value and the minimum value of the defect areas such as the concave areas and the cracks on the plane, so that the rectangular processing display frame can be conveniently generated later.

S43, taking a frame line formed by the two groups of X-axis parallel lines and Y-axis parallel lines as a processing display frame, obtaining an X-axis distance and a Y-axis distance of the processing display frame according to the X-axis extreme value and the Y-axis extreme value, and calculating according to the X-axis distance, the Y-axis distance and the number of three-dimensional coordinates of all points to obtain the display color of the processing display frame.

It is understood that, the rectangular area formed by intersecting parallel lines corresponding to the respective X-axis and Y-axis is generated as the processing display frame from the maximum value and the minimum value of the X-axis and the maximum value and the minimum value of the Y-axis in the abnormal area.

It will be appreciated that the X-axis distance can be determined by the difference between the X-axis extremum (maxima and minima) and the Y-axis distance can be determined by the difference between the Y-axis extremum (maxima and minima), i.e., the side length of the processed display frame, and the present invention will generate the display color of the corresponding display frame based on the side length of the processed display frame and the number of three-dimensional coordinates within the frame.

By the method, a user can directly see the corresponding defect area through the display frame, and the severity of the defect can be judged according to different colors or different shades of the processed display frame.

In some embodiments, the step S43 (the frame line formed by the two sets of X-axis parallel lines and Y-axis parallel lines is used as the processing display frame, the X-axis distance and the Y-axis distance of the processing display frame are obtained according to the X-axis extremum and the Y-axis extremum, and the display color of the processing display frame is obtained by calculating according to the X-axis distance, the Y-axis distance and the number of three-dimensional coordinates of all points) includes S431-S433:

s431, determining a frame line formed by the intersection area of the two groups of X-axis parallel lines and the Y-axis parallel lines as a processing display frame, and amplifying the processing display frame by a preset multiple.

It should be noted that, because the sizes of the cracks at the steel rail are different and are smaller, the processing display frame is amplified by corresponding preset times, wherein the preset times are amplification times set in advance according to actual conditions; thereby facilitating the observation of users and avoiding omission.

S432, calculating according to the X-axis extreme value, the Y-axis extreme value and the preset multiple to obtain the amplified X-axis distance and Y-axis distance of the processing display frame, and obtaining the area of the processing display frame according to the amplified X-axis distance and Y-axis distance.

It will be appreciated that the X-axis distance can be determined by the difference between the X-axis extremum (maximum and minimum), and the Y-axis distance can be determined by the difference between the Y-axis extremum (maximum and minimum), that is, the edge length of the processed display frame is multiplied by a corresponding preset multiple, so that the edge length of the actual display can be obtained, and the area of the processed display frame is obtained according to the edge length.

S433, obtaining three-dimensional coordinate density according to the area of the processing display frame and the number of the three-dimensional coordinates of the points, comparing the three-dimensional coordinate density with preset density intervals to obtain corresponding display colors, calculating the three-dimensional coordinate density through the following formula,

wherein ,

for three-dimensional coordinate density>

For the number of abnormal target points +.>

Is the maximum in the X-axis extremum, < ->

Is the minimum value in the X-axis extreme value, +.>

Is a preset multiple of->

Is the maximum in the Y-axis extremum, < +.>

Is the minimum value in the Y-axis extremum, +.>

For the area of the display frame, the number of abnormal target points +.>

Density of three-dimensional coordinates->

Proportional, three-dimensional coordinate Density->

And->

Inversely proportional, it will be appreciated that the area of the display frame is handled +.>

The larger and the number of abnormal target points +.>

The smaller the number, the smaller the crack, and the opposite processing shows the area of the frame

The smaller and the number of abnormal target points +.>

The greater the number, the greater the crack, the more the three-dimensional coordinate density>

The magnitude of the value determines the color of the corresponding process display frame, for example: the larger the crack, the darker the color of the corresponding treatment display frame, the smaller the crack, the lighter the color of the corresponding treatment display frame, or the different colors are set to represent different severity levels, which is not limited herein.

S44, determining the center point of the processing display frame and the center point of the abnormal area, overlapping the center point of the processing display frame and the center point of the abnormal area to enable the processing display frame and the abnormal area to be combined and displayed in the point cloud three-dimensional image, and generating a processing result, wherein the processing result comprises the number of the point three-dimensional coordinates.

It can be understood that the center point of the processing display frame and the center point of the abnormal region are selected at the same time, and the center points of the processing display frame and the abnormal region are overlapped, so that the processing display frame and the abnormal region are combined to be displayed in the point cloud three-dimensional image, and the number of corresponding abnormal target points is displayed.

The invention can not only carry out frame selection display on the defect area, is convenient for a user to observe, but also display the corresponding abnormal target point number, so that the user can check through combining the numerical value and the processing display frame, and the size of the defect area is more intuitively determined.

In order to better realize the three-dimensional point cloud calibration processing method for steel rail detection provided by the invention, the invention also provides a three-dimensional point cloud calibration processing device for steel rail detection, as shown in fig. 4, which comprises the following steps:

The present invention also provides a storage medium having stored therein a computer program for implementing the methods provided by the various embodiments described above when executed by a processor.

The storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). In addition, the ASIC may reside in a user device. The processor and the storage medium may reside as discrete components in a communication device. The storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tape, floppy disk, optical data storage device, etc.

The present invention also provides a program product comprising execution instructions stored in a storage medium. The at least one processor of the device may read the execution instructions from the storage medium, the execution instructions being executed by the at least one processor to cause the device to implement the methods provided by the various embodiments described above.

In the above embodiments of the terminal or the server, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: applicationSpecific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims

1. The three-dimensional point cloud calibration processing method for steel rail detection is characterized by comprising the following steps of:

2. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 1, wherein,

The control point cloud acquisition equipment acquires a point cloud three-dimensional image of the position of the steel rail at a preset height and a preset position, and determines the point three-dimensional coordinates of all points in the point cloud three-dimensional image, and the control point cloud acquisition equipment comprises:

3. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 2, wherein,

the receiving user configures size data for the steel rail, the size data at least comprises steel rail distance information and steel rail width information, the size data is calculated according to equipment three-dimensional coordinates, the steel rail distance information and the steel rail width information to obtain a target coordinate acquisition interval, and the receiving user comprises the following steps:

4. A method for calibrating and processing a three-dimensional point cloud for rail detection according to claim 3, wherein,

the method for determining the corresponding target area in the point cloud three-dimensional image as the steel rail detection calibration area according to the target coordinate acquisition interval, detecting and judging three-dimensional coordinates of all points in the steel rail detection calibration area according to standard detection information, and determining the abnormal area in the steel rail detection calibration area comprises the following steps:

5. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 4, wherein,

the step of retrieving standard detection information corresponding to the steel rail, determining Z-axis coordinates of all target points in a steel rail detection calibration area, comparing the Z-axis coordinates with the standard detection information for detection and judgment, and determining an abnormal area in the steel rail detection calibration area, comprises the following steps:

6. The method for three-dimensional point cloud calibration processing for rail detection according to claim 5, wherein before determining a corresponding target area in the point cloud three-dimensional image according to the target coordinate acquisition interval as a rail detection calibration area, detecting and judging three-dimensional coordinates of all points in the rail detection calibration area according to standard detection information, and determining an abnormal area in the rail detection calibration area, further comprises:

7. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 5, wherein,

calculating the distance between any two abnormal target points to obtain the distance between the target points, classifying all the abnormal target points with the distance between the target points smaller than the preset distance between the target points into the same abnormal area, wherein the method comprises the following steps:

8. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 7, wherein,

generating a corresponding processing display frame according to the attribute of the three-dimensional coordinates of the points in the abnormal area, combining the processing display frame and the abnormal area, displaying the processing display frame and the abnormal area in the point cloud three-dimensional image, and generating a processing result, wherein the processing result comprises the following steps:

9. The method for three-dimensional point cloud calibration processing for steel rail detection according to claim 8, wherein,

the frame line formed by the two groups of X-axis parallel lines and Y-axis parallel lines is used as a processing display frame, the X-axis distance and the Y-axis distance of the processing display frame are obtained according to the X-axis extreme value and the Y-axis extreme value, and the display color of the processing display frame is obtained by calculating according to the X-axis distance, the Y-axis distance and the number of three-dimensional coordinates of all points, and the processing display frame comprises the following steps:

wherein ,

for three-dimensional coordinate density>

For the number of abnormal target points +.>

Is the maximum in the X-axis extremum, < ->

Is the minimum value in the X-axis extreme value, +.>

Is a preset multiple of->

Is the maximum in the Y-axis extremum, < +.>

Is the minimum value in the Y-axis extremum.

10. Three-dimensional point cloud calibration processing apparatus that rail detected, its characterized in that includes: