CN112082770A - Rail vehicle body detection system and method based on artificial intelligence technology - Google Patents
Rail vehicle body detection system and method based on artificial intelligence technology Download PDFInfo
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Abstract
The invention discloses a rail vehicle body detection system and method based on artificial intelligence technology, which comprises the following steps: the system comprises a mobile platform, a mechanical arm, an image collector, a remote server, an information transmission unit and a motion control unit; the mechanical arm is mounted at the upper part of the mobile platform, and the image collector is mounted at the tail end of the mechanical arm; the image collector comprises a 2D camera and a 3D camera, is connected with the remote server through the information transmission unit, sends detection data of the vehicle body detection point to the server, and completes detection and recording of the vehicle body through data processing, artificial neural network comparison and data storage backup in sequence, so that the detection range and precision are improved, and the strength of manual inspection is reduced.
Description
Technical Field
The invention relates to the field of image processing, in particular to a rail vehicle body detection system and method based on an artificial intelligence technology.
Background
With the innovation of the rail transit technology in China entering a new era, the machine vision technology is continuously mature, the measurement precision is continuously improved, and more automatic and semi-automatic detection equipment is put into the rail vehicle detection; powerful guarantee is provided for automatic and intelligent promotion of the vehicle from research and development design to quality control stage. Meanwhile, the intensity of manual inspection is reduced, manual inspection can be further replaced completely, and the purposes of reducing the labor intensity of technicians and reducing the detection cost are achieved.
The better automatic detection mode in the market at present adopts the RGV trolley/unpowered trigger/AGV trolley combined with a 2D high-speed camera to perform primary automatic identification on suspicious fault points, and then realizes the setting judgment of the faults by means of manual secondary inspection. However, the detection positions of the method are relatively fixed, and for some problems and hidden dangers which are difficult to find at the skirt bottom and the side edge of the running part of the railway vehicle, related visual inspection needs to be carried out manually, so that omission is easy to generate, the vehicle can run with hidden dangers, the processing method for the collected images in the prior art still stays in the combination of a conventional preset algorithm and manual secondary judgment, and the accuracy and the efficiency of the method are still to be improved.
Chinese patent CN107135373A discloses a skirt bottom plate detection system and a skirt bottom plate detection method. Wherein, this system includes: the image collector is used for shooting the bottom and the side of the skirt bottom plate of the rail train to obtain images; the automatic guidance tool AGV trolley is used for carrying an image collector, so that the collection area of the image collector covers the bottom and the side part of the skirt bottom plate; and the processor is used for carrying out image processing on the image collected by the image collector to obtain a detection result of the skirt bottom plate. According to the technical scheme, the problem of single detection position is solved by increasing the coverage area of the image collector and matching with the movable platform, but the problem of detection of the hidden position of the skirt bottom or the walking part cannot be well solved, and the requirements of precision and detection speed cannot be met by a single image collector.
Chinese patent CN108805868A discloses an image processing method and a fault detection method for detecting faults of running gear equipment under an electric vehicle, and relates to an image processing method and a fault detection method for detecting equipment faults. Acquiring 3D images of the side part and the bottom part of the electric car through 3D cameras arranged on the two sides and the bottom part of the electric car track; acquiring position information of the component to be detected in the distance image, judging whether the component to be detected is lost or deformed based on image processing, performing matrix fusion by combining a part of distance image of the position of the component to be detected and a corresponding part of intensity image of the position of the component to be detected, and correcting a fusion matrix; and acquiring a gray level co-occurrence matrix through the fused matrix, calculating the characteristic change of the gray level co-occurrence matrix, and judging whether the component has a fault. The detection method adopts a method of fixing the detection unit (3D camera), the problems that the detection position is single, the relative distance and the angle between the image acquisition unit and the detection point cannot be adjusted and the like still exist, and manual inspection cannot be completely replaced.
Chinese patent CN107687953A discloses a truck fault automatic detection device, and the scheme is to build a 2D image acquisition module and a 3D image acquisition module around the truck track respectively, and after the truck passes through the 2D image acquisition module and the 3D image acquisition module, the truck acquires a 2D image and a 3D image respectively. And establishing a one-to-one corresponding mapping relation by using the accurate wheel base information in the 2D image and the 3D image. In the 3D image, fault recognition is performed using advanced image processing algorithms and pattern recognition methods. And mapping the identification result into the 2D image according to the mapping relation, and displaying the fault. Above-mentioned technical scheme can be applied to rail vehicle detection to the same reason to having combined 2D, 3D camera enhancement image and having contained information, having promoted the detection precision in other words, but its mode that adopts fixed detecting element, having the condition that the detection position is single with aforementioned technical scheme is the same, image acquisition unit and check point relative distance, angle can not adjust the scheduling problem, can not replace the manual work to patrol and examine completely.
Disclosure of Invention
In view of the above, the present invention provides a rail vehicle body detection system and method based on an artificial intelligence technology, which can solve the above problems.
For this purpose, the present invention is implemented by the following technical means.
Rail vehicle automobile body detecting system based on artificial intelligence technique includes: the system comprises a mobile platform, a mechanical arm, an image collector, a remote server, an information transmission unit and a motion control unit;
the lower part of the moving platform is provided with a walking part, the upper part of the moving platform is provided with the mechanical arm, and the tail end of the mechanical arm is provided with the image collector;
the electric control unit of the mechanical arm is connected with the motion control unit;
the motion control unit is connected with the remote server through the information transmission unit;
the image collector comprises at least one 2D camera and at least one 3D camera, and is connected with the remote server through the information transmission unit.
And furthermore, the bottom and the side of the running track of the railway vehicle are provided with a plurality of image collectors.
Further, the mobile platform is an RGV trolley, and the control mode is one or two of an automatic walking mode and a manual walking mode; the electric control unit of the RGV is connected with the motion control unit; two groups of auxiliary moving devices are symmetrically arranged on two sides of the RGV, and each group of the auxiliary moving devices is at least provided with two handles in parallel.
Further, the remote server comprises an arithmetic unit, a database, an information transceiving unit and an artificial neural network; the arithmetic unit is a central processing unit; the information receiving and transmitting unit comprises one or two combinations of a wireless network card and a wired network card; the database is a disk array; the information transmission unit comprises a network switch and a network cable or the network switch and an industrial router.
On the other hand, the invention also provides a rail vehicle body detection method based on the artificial intelligence technology, and the specific process of the vehicle body detection is as follows:
s1, placing the mobile platform near a vehicle body detection point;
s2, an image collector collects images near a vehicle body detection point, the images are transmitted to a remote server through an information transmission unit, the remote server calculates the deviation of the image collector and the optimal collection position of the vehicle body detection point, an adjustment signal is transmitted back to a motion control unit through the information transmission unit, and the motion control unit adjusts a mechanical arm to perform position compensation;
s3, the image collector scans the detection point of the vehicle body and transmits the scanning information to the remote server through the information transmission unit;
s4, the remote server processes the scanning information of the vehicle body detection points, then brings the processing result into an artificial neural network, judges whether the vehicle body detection points have faults or problems and feeds the faults or problems back to a maintainer;
and S5, the remote server records and backs up the detection data, and sends an instruction through the information transmission unit to detect the next detection point.
Further, a plurality of image collectors are arranged at the bottom and the side of the running track of the railway vehicle in the S1, before the railway vehicle enters the overhaul area, the bottom and the side of the running part are shot, images are transmitted to the remote server to be analyzed, the initial detection condition is obtained, and the detection point is determined according to the initial detection condition.
Further, the image collector in S2 captures an image of the detection point and its vicinity using a 2D camera, and transmits the image information to the remote server through an information transmission unit.
Further, the moving platform is an RGV trolley, and when the position compensation in the S2 is larger than the adjustable range of the mechanical arm, the RGV trolley is controlled to move through manual work or the remote server to complete the position compensation.
Further, in S3, the image collector scans the car body detection point by using a 3D camera, and the process includes: and scanning the surface of a detection point of the vehicle body by a 3D camera of the image collector to obtain point cloud data, and then transmitting the point cloud data to the remote server through the information transmission unit.
Further, the processing procedure of the remote server to scan the vehicle body detection point in S4 is as follows: and carrying out three-dimensional image processing on the received point cloud data, and reconstructing a corresponding point cloud image.
The invention has the following advantages:
the invention works in a mode that a 2D intelligent camera and a 3D measuring camera work cooperatively. The quality inspection points are accurately identified and positioned through the 2D camera, the mechanical arm is guided to adjust the camera to a proper measurement position, the 3D camera quickly scans the quality inspection points, 3D data of the vehicle is transmitted to the server, and the vehicle is analyzed in a neural network matching mode through the 3D camera and the template image, so that whether the vehicle is abnormal or not is inspected, quick, reliable, full-automatic and semi-automatic detection is realized, and the delivery of a fault train is avoided; meanwhile, the manual inspection intensity can be replaced or greatly reduced, and the detection cost is reduced.
Drawings
FIG. 1 is a system connection diagram according to embodiment 1 of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a structural view of the detecting end of the present invention;
FIG. 4 is a system connection diagram according to embodiment 2 of the present invention;
fig. 5 is a schematic diagram of a position of a track-side image collector in embodiment 2 of the present invention.
In the figure:
1-moving a platform; 2, a mechanical arm; 3-image collector.
Detailed Description
The invention will be further explained with reference to the drawings.
Example 1
A rail vehicle body detection system based on artificial intelligence technology, as shown in figures 1 and 3, mainly comprises: the system comprises a mobile platform 1, a mechanical arm 2, an image collector 3, a remote server, an information transmission unit and a motion control unit;
the lower part of the mobile platform 1 is provided with a walking part; preferably, the mobile platform 1 may be an RGV trolley, trolley steering mode walking mode. Further, in order to increase the mobility of the RGV trolley, as shown in fig. 3, two sets of auxiliary moving devices are symmetrically installed on two sides of the RGV trolley, and each set of auxiliary moving devices is provided with two handles in parallel, so that when the trolley needs to be replaced and used with a track, the lifting handles can be hung and transferred through machinery such as a crane or a crane, and the trolley can be lifted up and moved through manpower on two sides. The upper part of the RGV is provided with a mechanical arm 2, and the tail end of the mechanical arm 2 is provided with an image collector 3.
The electric control unit of the mechanical arm 2 is connected with the motion control unit.
The motion control unit is connected with the remote server through the information transmission unit; preferably, the information transmission unit includes a network switch and an industrial router, and signals to be transmitted, which are generated by various devices on the mobile platform 1, all reach the industrial router through the network switch, and are then wirelessly transmitted to the remote server.
The image collector comprises a 2D camera and a 3D camera, and is connected with the remote server through the information transmission unit.
Preferably, the remote server comprises an arithmetic unit, a database, an information transceiving unit and an artificial neural network; the arithmetic unit is a central processing unit; the information receiving and sending unit comprises one or two combinations of a wireless network card and a wired network card; the database is a disk array.
On the other hand, the invention provides a rail vehicle body detection method based on the artificial intelligence technology based on the detection system, and as shown in fig. 2, the vehicle body detection process specifically comprises the following steps:
s1, manually placing the mobile platform 1 on a corresponding track, wherein the mobile platform 1 is placed on a parallel track of a track where a vehicle body is located if the side face of a running part or the side face of the vehicle body needs to be detected under two conditions; if the skirt bottom of the vehicle body needs to be detected, the mobile platform 1 is placed on a track for detecting the lower layer of the vehicle body track; and then the mobile platform 1 is manually pushed or controlled to reach the position near the detection point of the vehicle body.
S2, starting the system, the 2D camera in the image collector 3 firstly collects the vehicle body detection point and the image near the vehicle body detection point, and transmits the image to the remote server through the information transmission unit, the remote server calculates the deviation of the most suitable collection position of the image collector 3 and the vehicle body detection point, and the deviation comprises the contents of the distance between the camera and the detection point, the collection angle and the like. And then the remote server transmits the adjusting signal back to the motion control unit through the information transmission unit, the motion control unit is connected with the mechanical arm electric control unit, and the mechanical arm 2 is adjusted to perform position compensation, so that the image collector 3 is positioned at the optimal detection position of the detection point. And if the position compensation is found to be larger than the adjustable range of the mechanical arm, manually pushing or controlling the RGV to move to complete the position compensation.
S3, the image collector 3 adopts 3D addition to carry out point cloud scanning on the vehicle body detection points to obtain point cloud data, and then the point cloud data are transmitted to a remote server through an information transmission unit.
And S4, the remote server processes the received point cloud data in a three-dimensional image, restores the corresponding point cloud image, brings the processing result into an artificial neural network, judges whether a fault or a problem exists at the detection point of the vehicle body, and feeds the fault or the problem back to a maintainer.
S5, the remote server records and backs up the detection data, sends an instruction through the information transmission unit, detects the next detection point, and manually pushes or controls the mobile platform 1 to move to the next detection point, so that semi-automatic detection is realized, manual strength is greatly reduced, and detection accuracy is improved.
Example 2
A rail vehicle body detection system based on artificial intelligence technology, as shown in fig. 3 and 4, mainly comprises: the system comprises a mobile platform 1, a mechanical arm 2, an image collector 3, a remote server, an information transmission unit and a motion control unit;
the lower part of the mobile platform 1 is provided with a walking part; preferably, the moving platform 1 may be an RGV car, and the car control mode is an automatic walking mode, as shown in fig. 4, an electric control unit of the RGV car is connected with the motion control unit. Further, in order to increase the mobility of the RGV trolley, as shown in fig. 3, two sets of auxiliary moving devices are symmetrically installed on two sides of the RGV trolley, and each set of auxiliary moving devices is provided with two handles in parallel, so that when the trolley needs to be replaced and used with a track, the lifting handles can be hung and transferred through machinery such as a crane or a crane, and the trolley can be lifted up and moved through manpower on two sides. The upper part of the RGV is provided with a mechanical arm 2, and the tail end of the mechanical arm 2 is provided with an image collector 3.
The electric control unit of the mechanical arm 2 is connected with the motion control unit.
The motion control unit is connected with the remote server through the information transmission unit; preferably, the information transmission unit includes a network switch and an industrial router, and signals to be transmitted, which are generated by various devices on the mobile platform 1, all reach the industrial router through the network switch, and are then wirelessly transmitted to the remote server.
The image collector comprises a 2D camera and a 3D camera, and is connected with the remote server through the information transmission unit.
Preferably, the system further comprises a plurality of image collectors 3 arranged at the bottom and the side of the rail before the rail vehicle enters the car factory, and further, as shown in fig. 5, three image collectors 3 are arranged at the bottom side in parallel and two image collectors 3 are arranged at the side, so as to pre-detect the skirt bottom and the side of the rail vehicle before the rail vehicle enters the car factory.
Preferably, the remote server comprises an arithmetic unit, a database, an information transceiving unit and an artificial neural network; the arithmetic unit is a central processing unit; the information receiving and sending unit comprises one or two combinations of a wireless network card and a wired network card; the database is a disk array.
On the other hand, the invention provides a rail vehicle body detection method based on the artificial intelligence technology based on the detection system, and as shown in fig. 2, the vehicle body detection process specifically comprises the following steps:
s1, before the rail vehicle enters the overhaul region, shooting the bottom and the side of the vehicle running part through the image collectors at the bottom and the side of the rail, transmitting the images to the remote server for analysis, obtaining an initial detection condition, and determining a detection point according to the initial detection condition; manually placing the mobile platform 1 on a corresponding track, wherein the mobile platform 1 is placed on a parallel track of the track where the vehicle body is located if the side surface of the walking part or the side surface of the vehicle body needs to be detected under two conditions; if the skirt bottom of the vehicle body needs to be detected, the mobile platform 1 is placed on a track for detecting the lower layer of the vehicle body track; and starting the system, and controlling the mobile platform 1 to move to the vicinity of the detection point by the server according to a preset sending instruction and stopping at a specified position.
S2, the 2D camera in the image collector 3 firstly collects the vehicle body detection point and the nearby image, and transmits the image to the remote server through the information transmission unit, and the remote server calculates the deviation of the image collector 3 and the optimal collection position of the vehicle body detection point, including but not limited to the distance between the camera and the detection point and the collection angle. And then the remote server transmits the adjusting signal back to the motion control unit through the information transmission unit, the motion control unit is connected with the mechanical arm electric control unit, and the mechanical arm 2 is adjusted to perform position compensation, so that the image collector 3 is positioned at the optimal detection position of the detection point. And if the position compensation is larger than the adjustable range of the mechanical arm, the server automatically sends an instruction to control the RGV to move to complete the position compensation.
S3, the image collector 3 adopts 3D addition to carry out point cloud scanning on the vehicle body detection points to obtain point cloud data, and then the point cloud data are transmitted to a remote server through an information transmission unit.
And S4, the remote server processes the received point cloud data in a three-dimensional image, restores the corresponding point cloud image, brings the processing result into an artificial neural network, judges whether a fault or a problem exists at the detection point of the vehicle body, and feeds the fault or the problem back to a maintainer.
S5, the remote server records and backs up the detection data, sends an instruction through the information transmission unit, and the mobile platform 1 automatically goes to the next detection point to perform detection after receiving the instruction, so that manual inspection is completely replaced, and the detection accuracy is also improved.
Although the present invention has been described in detail with reference to examples, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The utility model provides a rail vehicle automobile body detecting system based on artificial intelligence technique which characterized in that includes: the system comprises a mobile platform, a mechanical arm, an image collector, a remote server, an information transmission unit and a motion control unit;
the lower part of the moving platform is provided with a walking part, the upper part of the moving platform is provided with the mechanical arm, and the tail end of the mechanical arm is provided with the image collector;
the electric control unit of the mechanical arm is connected with the motion control unit;
the motion control unit is connected with the remote server through the information transmission unit;
the image collector comprises at least one 2D camera and at least one 3D camera, and is connected with the remote server through the information transmission unit.
2. The rail vehicle car body detection system of claim 1, further comprising a plurality of image collectors disposed on a bottom and a side of a rail vehicle running track.
3. The rail vehicle car body detection system of claim 1, wherein the mobile platform is an RGV car, and the control mode is one or a combination of an automatic walking mode and a manual walking mode; the electric control unit of the RGV is connected with the motion control unit; two groups of auxiliary moving devices are symmetrically arranged on two sides of the RGV, and each group of the auxiliary moving devices is at least provided with two handles in parallel.
4. The rail vehicle car body detection system of claim 1, wherein the remote server includes an arithmetic unit, a database, an information transceiving unit, an artificial neural network; the arithmetic unit is a central processing unit; the information receiving and transmitting unit comprises one or two combinations of a wireless network card and a wired network card; the database is a disk array; the information transmission unit comprises a network switch and a network cable or the network switch and an industrial router.
5. The rail vehicle body detection method based on the artificial intelligence technology is characterized in that the vehicle body detection process specifically comprises the following steps:
s1, placing the mobile platform near a vehicle body detection point;
s2, an image collector collects images near a vehicle body detection point, the images are transmitted to a remote server through an information transmission unit, the remote server calculates the deviation of the image collector and the optimal collection position of the vehicle body detection point, an adjustment signal is transmitted back to a motion control unit through the information transmission unit, and the motion control unit adjusts a mechanical arm to perform position compensation;
s3, the image collector scans the detection point of the vehicle body and transmits the scanning information to the remote server through the information transmission unit;
s4, the remote server processes the scanning information of the vehicle body detection points, then brings the processing result into an artificial neural network, judges whether the vehicle body detection points have faults or problems and feeds the faults or problems back to a maintainer;
and S5, the remote server records and backs up the detection data, and sends an instruction through the information transmission unit to detect the next detection point.
6. The rail vehicle body detection method according to claim 5, wherein a plurality of image collectors are arranged at the bottom and the side of the running track of the rail vehicle in the S1, when the rail vehicle enters the overhaul area, the bottom and the side of the running part are shot, the images are transmitted to the remote server for analysis, an initial detection condition is obtained, and a detection point is determined according to the initial detection condition.
7. The rail vehicle car body detection method according to claim 5, wherein the image collector in S2 captures images of the detection point and its vicinity using a 2D camera and transmits the image information to the remote server through an information transmission unit.
8. The rail vehicle car body detection method according to claim 5, wherein the moving platform is an RGV car, and when the position compensation in S2 is larger than the adjustable range of the mechanical arm, the RGV car is controlled to move manually or by the remote server to complete the position compensation.
9. The rail vehicle body detection method according to claim 7, wherein the image collector in the step S3 scans the body detection point by using a 3D camera, and the process comprises: and scanning the surface of a detection point of the vehicle body by a 3D camera of the image collector to obtain point cloud data, and then transmitting the point cloud data to the remote server through the information transmission unit.
10. The rail vehicle car body detection method according to claim 9, wherein the processing procedure of scanning information on the car body detection point by the remote server in S4 is as follows: and carrying out three-dimensional image processing on the received point cloud data, and reconstructing a corresponding point cloud image.
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| EP4339581A1 (en) * | 2022-09-16 | 2024-03-20 | CAM Engineering doo Novi Sad | System and method for automated visual inspection of an undercarriage of a vehicle such as a train and use thereof |
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Application publication date: 20201215 |