CN110806411A - Unmanned aerial vehicle rail detecting system based on line structure light - Google Patents
Unmanned aerial vehicle rail detecting system based on line structure light Download PDFInfo
- Publication number
- CN110806411A CN110806411A CN201911081182.9A CN201911081182A CN110806411A CN 110806411 A CN110806411 A CN 110806411A CN 201911081182 A CN201911081182 A CN 201911081182A CN 110806411 A CN110806411 A CN 110806411A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- image
- module
- image data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
- G01N2021/8861—Determining coordinates of flaws
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention discloses an unmanned aerial vehicle rail detection system based on line structured light. The unmanned aerial vehicle is provided with an image acquisition module, a line structure light source and a positioning module, wherein the line structure light source is used for vertically emitting linear laser to a measured object; the image acquisition module is used for shooting a tested object and transmitting image data to the ground control system; the positioning module is used for acquiring positioning information of the unmanned aerial vehicle; the ground control system is used for accurately positioning the position of the unmanned aerial vehicle, planning the flight route of the unmanned aerial vehicle and controlling the unmanned aerial vehicle to fly; and the system is used for processing the received image data to obtain damage data and a foreign body invasion result of the detected object. The invention organically combines the line structured light technology and the unmanned aerial vehicle technology, realizes the detection of rail abrasion and foreign matter invasion, and greatly improves the detection precision and efficiency.
Description
Technical Field
The invention belongs to the technical field of rail detection, and particularly relates to an unmanned aerial vehicle rail detection system based on line structured light.
Background
With the continuous enlargement of the scale of the high-speed railway and the continuous improvement of the running speed, the reliable and efficient detection of the rail and the timely discovery and solution of potential safety hazards existing in the rail become more important.
At present, most of the high-speed rail loss detection in China is manual detection and large-scale flaw detection vehicle detection. The manual detection has low automation and intelligence degree and low efficiency, and the detection personnel are difficult to operate in regions with severe environment and are not beneficial to digital storage and analysis. The large-scale flaw detection vehicle is slow in development in China and high in cost, the train running time period needs to be avoided in detection, and secondary damage to the steel rail is possible.
Unmanned aerial vehicle can accomplish automatic flight through wireless remote control's mode, carries out various tasks, has the security height, low energy consumption, reuse rate height, control advantage such as convenient. The most perfect at present in the aspect of rail detection is that an unmanned aerial vehicle track that certain area researched and developed patrols and examines flaw detection platform, utilizes the mode of many unmanned aerial vehicle cooperations to realize track contact detection, can improve the track greatly and patrol and examine efficiency. But because this track patrols and examines platform adopts unmanned aerial vehicle to carry the detection mode of detecting a flaw the wheel, cause further damage to the track in track detection easily, detection efficiency is also comparatively low. Therefore, a rail detection system with high precision and high speed, intelligent and visual effects is needed.
Disclosure of Invention
The invention aims to solve the defects in the background technology, and provides an unmanned aerial vehicle rail detection system based on line structured light, which can perform high-precision and high-efficiency real-time measurement on rail loss of a high-speed railway.
The technical scheme adopted by the invention is as follows: an unmanned aerial vehicle rail detection system based on line structure light comprises a ground control system and an unmanned aerial vehicle, wherein the unmanned aerial vehicle is provided with an image acquisition module, a line structure light source and a positioning module, and the line structure light source is used for vertically emitting linear laser to a detected object; the image acquisition module is used for shooting a tested object with linear stripes to form first image data, shooting the tested object without the linear stripes to form second image data, and transmitting the first image data and the second image data to the ground control system through the image transmission module; the positioning module is used for acquiring positioning information of the unmanned aerial vehicle and sending the positioning information to the ground control system;
the ground control system is used for accurately positioning the position of the unmanned aerial vehicle according to the map information, the train operation information, the second image data and the positioning information, planning the flight route of the unmanned aerial vehicle and controlling the unmanned aerial vehicle to fly; the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring a section profile of a measured object by processing received first image data and comparing the section profile with a standard profile to acquire damage data of the measured object; and the foreign body intrusion result of the tested object is obtained by comparing the processed second image data with a set threshold value.
Further, the image acquisition module is including the first camera module of the measurand that has the linear stripe of shooting and the second camera module that is used for shooting the measurand that does not take the linear stripe, first camera module and line structure light source are installed in unmanned aerial vehicle's side, and the optical axis of first camera module is certain contained angle with the laser of line structure light source transmission, the second camera module is installed in unmanned aerial vehicle's front end.
Further, the ground control system processes the received first image data through an ICP algorithm of closest point iteration to obtain a section profile of the measured object, compares the section profile with a standard profile to obtain deformation information of the measured object, and further judges whether the measured object is damaged.
Further, the ground control system performs Gaussian blur processing on the received second image data to reduce the sharpness of the image; then, performing open algorithm processing to detect edge information of the image; then, linear parts in the image are extracted through Hough algorithm processing, and the detection window is divided; and finally, extracting the gray information of the window, and comparing the gray information with a set threshold value to judge the foreign matter invasion result of the measured object.
Furthermore, data and signal interaction is carried out between the image acquisition module and the ground control system through the 5.8GHz wireless image transmission module.
According to the invention, the track and the target object are measured by the line structured light measurement and the unmanned aerial vehicle technology and the bottom surface control system, so that high-precision rail wear data is obtained; the front-end camera of the unmanned aerial vehicle is used for processing the acquired track image information to realize track foreign matter detection, and the track foreign matter detection is in butt joint with a centralized control system to ensure the safety of the train running environment; the rail detection speed and efficiency are greatly improved by comparing the train running information with the unmanned aerial vehicle position information to realize that the unmanned aerial vehicle automatically avoids the obstacle of the running train.
The invention fills the gap of detecting the steel rail by a large-scale flaw detection vehicle which has complicated manual measurement and high cost and needs special running time, and realizes a non-contact type rail detection system by utilizing the technologies of combining an unmanned aerial vehicle with line structure light and the like; the rail information is acquired by using the characteristics of high flexibility, high precision of structured light, high scanning speed and machine vision of the unmanned aerial vehicle. The flight and positioning of the unmanned aerial vehicle adopt GPS coarse positioning and steel rail image identification combined positioning; the rail foreign matter image recognition function is added to ensure the train running safety while the rail abrasion condition is detected. Compared with the existing track detection technology, the rail damage detection method has the advantages that the working efficiency, the measurement accuracy and the speed are greatly improved, and the rail damage detection method is an effective rail damage detection method.
Drawings
Fig. 1 is a schematic diagram of the principle of the present invention.
Fig. 2 is a schematic diagram of the principle of the unmanned aerial vehicle flight control of the present invention.
Fig. 3 is a schematic diagram illustrating the principle of rail damage detection according to the present invention.
Fig. 4 is a schematic view illustrating the principle of the foreign matter intrusion detection according to the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the invention provides an unmanned aerial vehicle rail detection system based on line structured light, which comprises a ground control system 1 and an unmanned aerial vehicle 2, wherein the unmanned aerial vehicle 2 is provided with an image acquisition module, a line structured light source 3 and a positioning module 5, and the line structured light source 3 is used for vertically emitting linear laser to a measured object (namely a track 8); the image acquisition module is used for shooting a measured object with linear stripes to form first image data, shooting the measured object without the linear stripes to form second image data, and transmitting the first image data and the second image data to the ground control system 1 through the image transmission module; the positioning module is used for acquiring positioning information of the unmanned aerial vehicle and sending the positioning information to the ground control system 1;
the ground control system 1 is used for accurately positioning the position of the unmanned aerial vehicle 2 according to the map information, the train operation information, the second image data and the positioning information, planning the flight route of the unmanned aerial vehicle and controlling the unmanned aerial vehicle to fly; the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring a section profile of a measured object by processing received first image data and comparing the section profile with a standard profile to acquire damage data of the measured object; and the system is used for comparing the received second image data with a set threshold value to obtain a foreign matter invasion result of the detected object after processing the received second image data, and sending the obtained damage data, the foreign matter invasion result and the position information to a dispatching centralized control system (CTC system 7) so as to process in time and avoid track safety accidents.
A dispatching centralized control system (CTC system) is an important technology of railway modernization, a TDCS is taken as a platform, dispatching centralization is taken as a core, the aim of traffic command automation is fulfilled, train running information, track information and track map information are provided for a ground control system, a traveling route is manually set and planned on a given rail schematic diagram, and the functions of positioning an unmanned aerial vehicle and automatically avoiding a train are realized. Unmanned aerial vehicle carries on laser instrument and camera, at first adopts satellite positioning to carry out coarse positioning to unmanned aerial vehicle, utilizes the camera on the unmanned aerial vehicle to carry out image recognition to the rail afterwards, cooperates map information to accomplish unmanned aerial vehicle accurate positioning and track following.
In the above scheme, the image acquisition module includes first camera module 4.1 of shooting the measurand that has linear stripe and is used for shooting the measurand that does not take linear stripe second camera module 4.2, first camera module 4.1 and line structure light source 3 install in unmanned aerial vehicle 2's side, and the optical axis of first camera module 4.1 and the laser of line structure light source 3 transmission are certain contained angle, can the contained angle between 30-60, second camera module 4.2 installs in unmanned aerial vehicle's front end.
According to the requirements of the invention on the camera function, the most suitable camera module is a CMOS high-speed camera. When the frame rate of the camera is 1000fps, the flight detection speed of the unmanned aerial vehicle is 18 km/h. When the frame rate of the camera is higher and the speed of the data transmission module is higher, the detection speed of the unmanned aerial vehicle can be correspondingly improved.
The camera (i.e. the first camera module) and the line laser (i.e. the line-structured light source) are fixed in relative position, the fan-shaped laser plane projected by the line laser cuts the measured object, a highlight optical strip is formed on the surface of the measured object, the camera shoots from another fixed angle, the camera projects an object in the three-dimensional world onto the two-dimensional image plane of the camera through the lens and the imaging element, and a two-dimensional distortion image of the structured optical strip of the measured object can be obtained. Transmitting the collected image to a bottom surface control system in the form of a video signal, knowing the self parameters of a vision sensor and the three-dimensional shape of the surface of an object, and determining the pixel coordinates of the light bars in the image; when the relative position parameters between the laser and the camera are known, the world coordinates of the surface of the object can be recovered from the image coordinates of the distorted two-dimensional light bars based on the optical triangulation measurement principle, and the measurement of the surface of the object is completed.
In the above scheme, the ground control system is provided with a point cloud image processing module, the point cloud image processing module processes the received first image data (i.e., point cloud data information) through an ICP algorithm of closest point iteration to obtain a tangent plane profile of the object to be measured, and compares the tangent plane profile with a standard profile to obtain deformation information of the object to be measured, thereby determining whether the object to be measured is damaged.
In the scheme, the ground control system performs Gaussian blur processing on the received second image data to reduce the sharpness of the image; then, performing open algorithm processing to detect edge information of the image; then, linear parts in the image are extracted through Hough algorithm processing, and the detection window is divided; and finally, extracting the gray information of the window, and comparing the gray information with a set threshold value to judge the foreign matter invasion result of the measured object.
In the scheme, bidirectional data and signal interaction is carried out between the image acquisition module and the ground control system through the 5.8GHz wireless image transmission module, and control information, image data information and unmanned aerial vehicle position information are transmitted respectively. The 5.8GHz wireless image transmission module has the characteristics of small volume, low power consumption, high sensitivity and the like, and can be widely applied to various fields.
As shown in fig. 2, in the aspect of positioning the unmanned aerial vehicle, the ground control system performs track tracking by using GPS coarse positioning in combination with image processing to obtain accurate positioning of the unmanned aerial vehicle (i.e., a specific position of the unmanned aerial vehicle corresponding to a rail segment); in the aspect of route planning, a flight route is intuitively set on a schematic diagram through computer-aided operation.
As shown in fig. 3, the unmanned aerial vehicle combines with a ground control system, and light bars emitted by a laser on the unmanned aerial vehicle are projected to the surface of a measured object to form light bars with certain characteristics, and a high-speed industrial CMOS camera is used for shooting to obtain images. And extracting the laser light bar irradiated on the steel rail by the line laser by using an image processing algorithm to recover the surface profile of the object, comparing the extracted laser light bar with the standard profile to obtain the deformation information of the detected steel rail, and further judging whether the steel rail is damaged.
As shown in fig. 4, the foreign matter intrusion detection system first performs gaussian blur processing to reduce the sharpness of an image; then, performing open algorithm processing to detect edge information of the image; then, linear parts in the image are extracted through Hough algorithm processing, and the detection window is divided; and finally, extracting the gray information of the window, and realizing rail intrusion detection by setting a proper threshold value.
The track detection process of the invention is as follows:
firstly, track map information and train operation information of a road section to be measured are imported, and a ground station automatically confirms a flight route of an unmanned aerial vehicle and the automatic avoidance of the unmanned aerial vehicle on a train through a track planning algorithm so as to control the unmanned aerial vehicle to fly. The unmanned aerial vehicle carries a laser and a camera to automatically track and carry out flight detection, and a joint positioning technology (namely GPS positioning and image recognition are combined) is adopted in the positioning aspect to accurately acquire the position information of the unmanned aerial vehicle. The method comprises the steps of obtaining track image information and point cloud data information of a track tangent plane through a line structure optical measurement method, compressing the information on an unmanned aerial vehicle, sending the information to a ground control system through a map transmission module for processing, processing the information through an ICP algorithm of closest point iteration by the ground control system to obtain a track tangent plane outline, and comparing and analyzing the track tangent plane outline with a standard outline to obtain high-precision rail damage data. Meanwhile, in the track-seeking flight process of the unmanned aerial vehicle, the front-end camera acquires images of the front view and transmits the images to the ground control system, and the track and foreign matter information in the images are detected through a foreign matter detection algorithm.
The invention fills the gap of detecting the steel rail by a large-scale flaw detection vehicle which has complicated manual measurement and high cost and needs special running time, and realizes a non-contact type rail detection system by utilizing the technologies of unmanned aerial vehicles, line structure light and the like. The rail information is acquired by using the characteristics of high flexibility, high precision of structured light, high scanning speed and machine vision of the unmanned aerial vehicle. The flight and positioning of the unmanned aerial vehicle adopt GPS coarse positioning and steel rail image identification combined positioning; the rail foreign matter image recognition function is added to ensure the train running safety while the rail abrasion condition is detected. The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (5)
1. The utility model provides an unmanned aerial vehicle rail detecting system based on line structure light which characterized in that: the unmanned aerial vehicle is provided with a line structure light source, an image acquisition module and a positioning module, wherein the line structure light source is used for vertically emitting linear laser to a measured object; the image acquisition module is used for shooting a measured object with linear stripes to form first image data, shooting the measured object without the linear stripes to form second image data, and transmitting the first image data and the second image data to the ground control system; the positioning module is used for acquiring positioning information of the unmanned aerial vehicle and sending the positioning information to the ground control system;
the ground control system is used for accurately positioning the position of the unmanned aerial vehicle according to the map information, the train operation information, the second image data and the positioning information, planning the flight route of the unmanned aerial vehicle and controlling the unmanned aerial vehicle to fly; the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring a section profile of a measured object by processing received first image data and comparing the section profile with a standard profile to acquire damage data of the measured object; and the foreign body intrusion result of the tested object is obtained by comparing the processed second image data with a set threshold value.
2. The line structured light based unmanned aerial vehicle rail detection system of claim 1, wherein: the image acquisition module is including the first camera module of the measurand who shoots the measurand that has linear stripe and the second camera module that is used for shooting the measurand that does not take linear stripe, first camera module and line structure light source are installed in unmanned aerial vehicle's side, and the optical axis of first camera module is certain contained angle with the laser of line structure light source transmission, the second camera module is installed in unmanned aerial vehicle's front end.
3. The line structured light based unmanned aerial vehicle rail detection system of claim 1, wherein: the ground control system processes the received first image data through an ICP algorithm of closest point iteration to obtain a section outline of the measured object, compares the section outline with a standard outline to obtain deformation information of the measured object, and further judges whether the measured object is damaged.
4. The line structured light based unmanned aerial vehicle rail detection system of claim 1, wherein: the ground control system carries out Gaussian blur processing on the received second image data to reduce the sharpness of the image; then, performing open algorithm processing to detect edge information of the image; then, linear parts in the image are extracted through Hough algorithm processing, and the detection window is divided; and finally, extracting the gray information of the window, and comparing the gray information with a set threshold value to judge the foreign matter invasion result of the measured object.
5. The line structured light based unmanned aerial vehicle rail detection system of claim 1, wherein: and the image acquisition module, the positioning module and the ground control system perform data and signal interaction through the 5.8GHz wireless image transmission module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911081182.9A CN110806411A (en) | 2019-11-07 | 2019-11-07 | Unmanned aerial vehicle rail detecting system based on line structure light |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911081182.9A CN110806411A (en) | 2019-11-07 | 2019-11-07 | Unmanned aerial vehicle rail detecting system based on line structure light |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110806411A true CN110806411A (en) | 2020-02-18 |
Family
ID=69501477
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911081182.9A Pending CN110806411A (en) | 2019-11-07 | 2019-11-07 | Unmanned aerial vehicle rail detecting system based on line structure light |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110806411A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114119477A (en) * | 2021-10-25 | 2022-03-01 | 华南理工大学 | Line structured light-based method for detecting foreign matters in high-voltage power transmission line at night |
| CN114554030A (en) * | 2020-11-20 | 2022-05-27 | 空客(北京)工程技术中心有限公司 | Device detection system and device detection method |
| CN115188091A (en) * | 2022-07-13 | 2022-10-14 | 国网江苏省电力有限公司泰州供电分公司 | Unmanned aerial vehicle grid inspection system and method integrating power transmission and transformation equipment |
| TWI800137B (en) * | 2021-12-03 | 2023-04-21 | 國立虎尾科技大學 | Intelligent unmanned aerial vehicle railway monitoring system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106741890A (en) * | 2016-11-28 | 2017-05-31 | 北京交通大学 | A kind of high-speed railway safety detecting system based on the dual-purpose unmanned plane of empty rail |
| US20170236270A1 (en) * | 2014-08-12 | 2017-08-17 | Mectron Engineering Company, Inc. | Video Parts Inspection System |
| CN108986082A (en) * | 2018-06-28 | 2018-12-11 | 武汉理工大学 | A kind of profile of steel rail detection method and system based on EPNP |
| CN208270445U (en) * | 2018-06-25 | 2018-12-21 | 南昌工程学院 | Track component surface defect detection apparatus based on three-dimensional measurement |
| CN110209200A (en) * | 2019-07-12 | 2019-09-06 | 上海电气泰雷兹交通自动化系统有限公司 | Train rail obstacle detection method and detection system for obstacle |
-
2019
- 2019-11-07 CN CN201911081182.9A patent/CN110806411A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170236270A1 (en) * | 2014-08-12 | 2017-08-17 | Mectron Engineering Company, Inc. | Video Parts Inspection System |
| CN106741890A (en) * | 2016-11-28 | 2017-05-31 | 北京交通大学 | A kind of high-speed railway safety detecting system based on the dual-purpose unmanned plane of empty rail |
| CN208270445U (en) * | 2018-06-25 | 2018-12-21 | 南昌工程学院 | Track component surface defect detection apparatus based on three-dimensional measurement |
| CN108986082A (en) * | 2018-06-28 | 2018-12-11 | 武汉理工大学 | A kind of profile of steel rail detection method and system based on EPNP |
| CN110209200A (en) * | 2019-07-12 | 2019-09-06 | 上海电气泰雷兹交通自动化系统有限公司 | Train rail obstacle detection method and detection system for obstacle |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114554030A (en) * | 2020-11-20 | 2022-05-27 | 空客(北京)工程技术中心有限公司 | Device detection system and device detection method |
| CN114554030B (en) * | 2020-11-20 | 2023-04-07 | 空客(北京)工程技术中心有限公司 | Device detection system and device detection method |
| CN114119477A (en) * | 2021-10-25 | 2022-03-01 | 华南理工大学 | Line structured light-based method for detecting foreign matters in high-voltage power transmission line at night |
| TWI800137B (en) * | 2021-12-03 | 2023-04-21 | 國立虎尾科技大學 | Intelligent unmanned aerial vehicle railway monitoring system and method |
| CN115188091A (en) * | 2022-07-13 | 2022-10-14 | 国网江苏省电力有限公司泰州供电分公司 | Unmanned aerial vehicle grid inspection system and method integrating power transmission and transformation equipment |
| CN115188091B (en) * | 2022-07-13 | 2023-10-13 | 国网江苏省电力有限公司泰州供电分公司 | Unmanned aerial vehicle gridding inspection system and method integrating power transmission and transformation equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110806411A (en) | Unmanned aerial vehicle rail detecting system based on line structure light | |
| CN106680290B (en) | Multifunctional detection vehicle in narrow space | |
| CN110979321B (en) | Obstacle avoidance method for unmanned vehicle | |
| CN110471085B (en) | Track detecting system | |
| CN102508246B (en) | Method for detecting and tracking obstacles in front of vehicle | |
| CN100495274C (en) | Control method for automatic drive of large engineering vehicle and system thereof | |
| CN109359409A (en) | A kind of vehicle passability detection system of view-based access control model and laser radar sensor | |
| CN112132896B (en) | Method and system for detecting states of trackside equipment | |
| EP4079597A1 (en) | Method for in-situ and real-time collection and processing of geometric parameters of railway lines | |
| CN103413313A (en) | Binocular vision navigation system and method based on power robot | |
| CN103400392A (en) | Binocular vision navigation system and method based on inspection robot in transformer substation | |
| EP1981748A1 (en) | System for measuring speed and/or position of a train | |
| CN102914290A (en) | Metro gauge detecting system and detecting method thereof | |
| CN109001743A (en) | Tramcar anti-collision system | |
| WO2023109501A1 (en) | Train active obstacle detection method and apparatus based on positioning technology | |
| CN113537046A (en) | Map lane marking method and system based on vehicle track big data detection | |
| CN111506069B (en) | All-weather all-ground crane obstacle identification system and method | |
| CN109785431A (en) | A kind of road ground three-dimensional feature acquisition method and device based on laser network | |
| Tianwen et al. | Research on obstacle detection method of urban rail transit based on multisensor technology | |
| CN208847836U (en) | Tramcar anti-collision system | |
| CN113885504A (en) | Autonomous inspection method and system for train inspection robot and storage medium | |
| KR20150034860A (en) | Pole position detection system through number recognition | |
| CN210879689U (en) | Intelligent robot suitable for subway vehicle train inspection work | |
| Espino et al. | Rail and turnout detection using gradient information and template matching | |
| CN110696016A (en) | Intelligent robot suitable for subway vehicle train inspection work |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200218 |
|
| RJ01 | Rejection of invention patent application after publication |