CN115100519B - Method for identifying hidden danger objects along high-speed rail - Google Patents
Method for identifying hidden danger objects along high-speed rail Download PDFInfo
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- CN115100519B CN115100519B CN202210717660.6A CN202210717660A CN115100519B CN 115100519 B CN115100519 B CN 115100519B CN 202210717660 A CN202210717660 A CN 202210717660A CN 115100519 B CN115100519 B CN 115100519B
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 239000000428 dust Substances 0.000 claims description 18
- 230000001629 suppression Effects 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 16
- 230000002093 peripheral effect Effects 0.000 claims description 12
- 239000002362 mulch Substances 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 3
- 230000003137 locomotive effect Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 2
- 208000012260 Accidental injury Diseases 0.000 abstract 1
- 208000014674 injury Diseases 0.000 abstract 1
- 238000010276 construction Methods 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 208000034699 Vitreous floaters Diseases 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 235000005770 birds nest Nutrition 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 235000005765 wild carrot Nutrition 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Abstract
The invention relates to the field of detection methods of high-speed rail power supply equipment, in particular to a method for identifying hidden danger objects along a high-speed rail. The problem that hidden danger objects near power supply equipment along the line are difficult to identify in the prior art is solved. The method comprises collecting images; selecting an initial acquisition area of the overhead contact system on the acquired image; analyzing pixel points line by line in an initial acquisition area, and judging a suspected hidden danger area; counting the identifiable line type in the suspected foreign body area to form an important observation area; and (5) judging the foreign matter types according to the line-type conditions of the key observation area, and giving a system prompt. The advantages are that: the method for identifying the hidden trouble objects along the high-speed rail samples the high-speed rail along the route, and is convenient to sample; the hidden danger area is combined with the characteristics of the hidden danger area, so that the hidden danger object is identified, the identification efficiency is high, the accidental injury degree is low, the identification is quick, and the cost is low.
Description
Technical Field
The invention relates to a method for detecting power supply equipment of a high-speed rail, in particular to a method for identifying hidden danger matters along the high-speed rail by using a method for identifying hidden danger matters along the high-speed rail.
Background
The power supply system is a system which is composed of a power supply system and a power transmission and distribution system and is used for generating electric energy and supplying and conveying the electric energy to electric equipment. In long-distance power supply systems, problems of difficult maintenance caused by damage to circuit equipment are often faced, such as common bird nest construction, circuit weathering hanging-up, circuit abrasion and the like.
In which the contact net is extremely damaged by the hooking of the foreign matter of the contact net on the contact net. Because of the accident caused by the foreign matters such as kites and the like being hung on the high-speed rail contact net, the train is easy to be late. The overhead contact system is characterized by being erected in open air and not being used for standby. Farmers (covered) mulching films, dust screens on construction sites, plastic greenhouses, plastic packaging bags, kite strings, etc., which float with high winds when they are high, are known as floats. At present, a passenger is generally ridden to add (check) to find foreign matters or floaters of the foreign matters, meanwhile, the foreign matters are in two conditions, one is that the foreign matters are blown up by wind and hung on a contact net and are urgently needed to be solved, and the other is that the foreign matters are on the ground and are not lifted, under the condition that the contact net is temporarily safe, but does not meet the related safety standards, the potential safety hazards are great, the coordination of a safety checking party and a construction party is urgently needed, and the foreign matters are corrected as soon as possible, and at present, a device capable of reacting quickly and finding illegal building behaviors is lacked.
Although we can also take some technological measures, such as finding foreign objects by satellite image comparison. In the prior art, the identification of the foreign matters on the contact network is relatively difficult, and the cost of satellite identification is too high. Relevant technicians put forward to recognize according to unmanned aerial vehicle, but unmanned aerial vehicle duration is short, and the photo recognition in later stage is high in the degree of difficulty, and the procedure is complicated.
Disclosure of Invention
The invention aims to solve the problem of high discovery cost of illegal construction along a high-speed rail in the prior art.
The specific scheme of the invention is as follows:
The method for identifying hidden danger objects along the high-speed rail comprises the following steps:
(1) The image acquisition, namely installing a photographing device outside the locomotive of the carriage of the high-speed rail, and carrying out photographing image acquisition every 0.2 to 0.5 seconds when the high-speed rail runs to more than 150KM/h to form an acquisition image, wherein the acquisition image is input in a communication way or is input into an image processing system in a timing way to start identification screening;
(2) Finding a contact net on the acquired image: a, transversely checking a path pixel point by taking a single pixel as a unit from the right lower corner of an acquired image, judging that a coordinate dark point group is a suspected contact network line initial point group when more than two continuous pixel values similar to each other and different from the pixel values of peripheral points appear in a single-row range, combining at least 3 rows from top to bottom after each contact network line initial point group is obtained, judging that a suspected contact network class is judged when the initial point group starting end point longitudinal coordinate difference between each row is smaller than 5 coordinate units and the distance difference is smaller than 2 coordinate units, and then connecting along each adjacent contact network class, determining that a graph connected with the suspected contact network class is a suspected contact network area when the suspected contact network class presents a continuous state and the total number of the calculated lines exceeds 1/4 of the number of the drawing, and then dividing an expanded area into an expanded area by taking the coordinate units below 100 on both sides of each contact network area as an expanded area;
(3) Finding a vertical rod on the acquired image: setting up a secondary scanning area below the contact network area shown in the step (1) in the acquired image, transversely checking the pixel points of the path by taking a single pixel as a unit in the direction of the upper right corner in the secondary scanning area, judging the coordinate dark point group as a suspected pole setting initial point group when more than two continuous coordinate dark points with similar pixel values and different peripheral point pixel values appear in a single-row range, combining at least 3 rows from top to bottom after each pole setting initial point group is obtained, judging that the vertical coordinate difference of starting and ending points among each row initial point group is less than 5 coordinate units and the distance difference is less than 2 coordinate units, and judging that the area in the suspected pole setting is defined as a pole setting area;
(4) Finding a rail on the acquired image: in the acquired image, the middle right lower corner of two upright post areas corresponding to the horizontal position starts to search the pixel points in the left upper corner direction transversely by taking a single pixel as a unit, when more than two continuous coordinate dark points with similar pixel values and different pixel values of peripheral points appear in a single line range, the coordinate dark point group is judged to be a suspected rail initial point group, after each rail initial point group is obtained, at least 3 lines are combined up and down, the end point longitudinal coordinate difference between the initial point groups between each line is judged to be less than 5 coordinate units, when the distance difference is less than 2 coordinate units, the suspected rail is judged, and when two suspected rails appear in a single line sampling, the region in the two suspected rails is delimited as a rail area;
(5) Analyzing pixel points line by line in an initial acquisition area, and judging a suspected foreign object area: screening out the contact net area and the vertical rod area in an acquired image, setting the rest area as an acquisition area, transversely checking the pixel points in the path by taking a single pixel as a unit in the direction of the upper left corner in the acquisition area, acquiring pixel information of unit coordinate points line by line, when the color of the edge point of the acquisition area is inconsistent with that of the adjacent point, using the distance of 2 to 19 pixels as a radius to define a secondary sampling range, checking line by line along the checking line in the step (2) in the secondary sampling range to form dark point distribution data, analyzing the data, and determining that a suspected boundary is formed when the dark point area of the checking line below the sampling area; then further enlarging the measured pixel distance until the measured pixel distance is contacted with the straight line and the vertical rod or the track or the area boundary, and judging that the measured pixel distance is the foreign matters of the mulching film and the dust suppression net;
(6) In the step (5), after the suspicious boundary is determined, the measured pixel distance is further enlarged until a closed edge is formed, and the suspicious boundary is determined as a nearby working machine;
(7) And (3) transmitting the results of the steps (5) and (6) to a total server, and guiding staff to clean.
And counting the inclination angle of the suspected boundary, and judging suspected to further distinguish the plateau earthwork or the peripheral trees when the horizontal inclination angle is larger than 70 degrees.
Further, pixel color information of each coordinate point in the outline of the foreign matter is counted, and when the color is white and the color outside the edge is blue, the outline is judged to be white cloud, and suspected marks are marked.
And (3) judging that the mulch film and the dust suppression net are provided with the heavy mulch film and the dust suppression net foreign matters when the mulch film and the dust suppression net are in the condition of realizing the interleaving with the closed curve in the measurement result in the step (5).
Pixel color information of each coordinate point in the outline of the foreign matter, when the internal and external color difference of the outline is less than 50, judging the outline as a mulching film; when the outline is green, the dust suppression net is judged, and when the outline is yellow or blue, the engineering truck is judged.
The invention has the beneficial effects that:
the method for identifying hidden danger objects along the high-speed rail is designed, firstly, sampling is convenient, unmanned aerial vehicle participation is not needed, the sampling device is directly hung outside a carriage of the high-speed rail, a line is taken down, foreign matters are identified for staff, and staff in a district can be immediately informed of maintenance after verification;
thirdly, a complete image screening method is provided, a method for reducing sampling areas can be added in the method, so that the image processing memory of an image processing background is saved, and the running speed of equipment is improved;
In the detection method, the characteristics of each foreign matter are combined, the condition and the type of the foreign matter can be analyzed in time, the reference is given to staff, the judgment of the type of the foreign matter is given in real time by combining a database, and the safety manager is effectively guided to quickly make further judgment.
Drawings
FIG. 1 is an image instance acquired in step (1) of the present invention;
A: a catenary area; 1. engineering vehicle; 2. mulching film or dust suppression net.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
A method for identifying hidden danger objects along a high-speed rail, referring to figure 1,
The method comprises the following steps:
(1) The image acquisition, namely installing a photographing device outside the locomotive of the carriage of the high-speed rail, and carrying out photographing image acquisition every 0.2 to 0.5 seconds when the high-speed rail runs to more than 150KM/h to form an acquisition image, wherein the acquisition image is input in a communication way or is input into an image processing system in a timing way to start identification screening;
(2) Finding a contact net on the acquired image: a, transversely checking a path pixel point by taking a single pixel as a unit from the right lower corner of an acquired image, judging that a coordinate dark point group is a suspected contact network line initial point group when more than two continuous pixel values similar to each other and different from the pixel values of peripheral points appear in a single-row range, combining at least 3 rows from top to bottom after each contact network line initial point group is obtained, judging that a suspected contact network class is judged when the initial point group starting end point longitudinal coordinate difference between each row is smaller than 5 coordinate units and the distance difference is smaller than 2 coordinate units, and then connecting along each adjacent contact network class, determining that a graph connected with the suspected contact network class is a suspected contact network area when the suspected contact network class presents a continuous state and the total number of the calculated lines exceeds 1/4 of the number of the drawing, and then dividing an expanded area into an expanded area by taking the coordinate units below 100 on both sides of each contact network area as an expanded area;
(3) Finding a vertical rod on the acquired image: setting up a secondary scanning area below the contact network area shown in the step (1) in the acquired image, transversely checking the pixel points of the path by taking a single pixel as a unit in the direction of the upper right corner in the secondary scanning area, judging the coordinate dark point group as a suspected pole setting initial point group when more than two continuous coordinate dark points with similar pixel values and different peripheral point pixel values appear in a single-row range, combining at least 3 rows from top to bottom after each pole setting initial point group is obtained, judging that the vertical coordinate difference of starting and ending points among each row initial point group is less than 5 coordinate units and the distance difference is less than 2 coordinate units, and judging that the area in the suspected pole setting is defined as a pole setting area;
(4) Finding a rail on the acquired image: in the acquired image, the middle right lower corner of two upright post areas corresponding to the horizontal position starts to search the pixel points in the left upper corner direction transversely by taking a single pixel as a unit, when more than two continuous coordinate dark points with similar pixel values and different pixel values of peripheral points appear in a single line range, the coordinate dark point group is judged to be a suspected rail initial point group, after each rail initial point group is obtained, at least 3 lines are combined up and down, the end point longitudinal coordinate difference between the initial point groups between each line is judged to be less than 5 coordinate units, when the distance difference is less than 2 coordinate units, the suspected rail is judged, and when two suspected rails appear in a single line sampling, the region in the two suspected rails is delimited as a rail area;
(5) Analyzing pixel points line by line in an initial acquisition area, and judging a suspected foreign object area: screening out the contact net area and the vertical rod area in an acquired image, setting the rest area as an acquisition area, transversely checking the pixel points in the path by taking a single pixel as a unit in the direction of the upper left corner in the acquisition area, acquiring pixel information of unit coordinate points line by line, when the color of the edge point of the acquisition area is inconsistent with that of the adjacent point, using the distance of 2 to 19 pixels as a radius to define a secondary sampling range, checking line by line along the checking line in the step (2) in the secondary sampling range to form dark point distribution data, analyzing the data, and determining that a suspected boundary is formed when the dark point area of the checking line below the sampling area; then further enlarging the measured pixel distance until the measured pixel distance is contacted with the straight line and the vertical rod or the track or the area boundary, and judging that the measured pixel distance is the foreign matters of the mulching film and the dust suppression net;
(6) In the step (5), after the suspicious boundary is determined, the measured pixel distance is further enlarged until a closed edge is formed, and the suspicious boundary is determined as a nearby working machine;
(7) And (3) transmitting the results of the steps (5) and (6) to a total server, and guiding staff to clean. And (3) judging that the mulch film and the dust suppression net are provided with the heavy mulch film and the dust suppression net foreign matters when the mulch film and the dust suppression net are in the condition of realizing the interleaving with the closed curve in the measurement result in the step (5).
In the working process, firstly photographing and sampling are carried out, images are transmitted to a server in real time or at regular time, photographing scenes among all the photos are approximately at intervals of 200 meters, the whole line can be basically covered, then scanning is carried out one by one, a contact net, a vertical rod and a rail are screened out in sequence, hidden danger objects along the line such as a mulching film, a dust suppression net and the like are screened out in a relevant area, and meanwhile, a set of screening equipment is introduced to screen out redundant data such as blue sky and white clouds, aircraft flying birds and the like.
According to the position characteristics of the hidden trouble objects, the hidden trouble objects are identified, the identification is accurate, accurate guidance is provided for staff, even the problems can be cooperatively solved by remote telephone contact with related departments, the processing efficiency of the problems is improved, and the potential safety hazards of the operation of the high-speed rail electric network are reduced.
Example 2
The principle is the same as that of embodiment 1, in which in embodiment 1, pixel color information of each coordinate point in the outline of the foreign matter is determined as a mulching film when the internal and external chromatic aberration of the outline is less than 50; when the outline is green, the dust suppression net is judged, when the outline is yellow or blue, the algorithm thought in the application is that firstly, dark points in an image are found, the outline is formed, further, in the progressive scanning, statistics is carried out on the outline graph, then, the statistical graph is combined with the intersection situation of the statistical graph and the contact net, and if the airplane or bird is found in the process of judging whether the foreign matter hung on the contact net is detected, the foreign matter is automatically screened out to possibly belong to misjudgment.
In this embodiment, the pixel color information of each coordinate point in the outline of the foreign object is further counted, and when the color is white and the color outside the edge is blue, it is determined that the foreign object is white and suspected to be marked.
Example 3
The principle of this embodiment is the same as that of embodiment 1, in that the inclination angle of the suspected boundary is counted, and when the horizontal inclination angle is greater than 70 degrees, the suspected boundary is judged to further distinguish the plateau earthwork or the peripheral tree.
Considering the color of the foreign matters, the misjudgment of the clouds under the sunny condition and the missed judgment of the plastic bags under the cloudy condition are effectively avoided.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The method for identifying the hidden danger objects along the high-speed rail is characterized by comprising the following steps of:
(1) The image acquisition, namely installing a photographing device outside the locomotive of the carriage of the high-speed rail, and carrying out photographing image acquisition every 0.2 to 0.5 seconds when the high-speed rail runs to more than 150KM/h to form an acquisition image, wherein the acquisition image is input in a communication way or is input into an image processing system in a timing way to start identification screening;
(2) Finding a contact net on the acquired image: a, transversely checking a path pixel point by taking a single pixel as a unit from the right lower corner of an acquired image, judging that a coordinate dark point group is a suspected contact network line initial point group when more than two continuous pixel values similar to each other and different from the pixel values of peripheral points appear in a single-row range, combining at least 3 rows from top to bottom after each contact network line initial point group is obtained, judging that a suspected contact network class is judged when the initial point group starting end point longitudinal coordinate difference between each row is smaller than 5 coordinate units and the distance difference is smaller than 2 coordinate units, and then connecting along each adjacent contact network class, determining that a graph connected with the suspected contact network class is a suspected contact network area when the suspected contact network class presents a continuous state and the total number of the calculated lines exceeds 1/4 of the number of the drawing, and then dividing an expanded area into an expanded area by taking the coordinate units below 100 on both sides of each contact network area as an expanded area;
(3) Finding a vertical rod on the acquired image: setting up a secondary scanning area below the contact network area shown in the step (1) in the acquired image, transversely checking the pixel points of the path by taking a single pixel as a unit in the direction of the upper right corner in the secondary scanning area, judging the coordinate dark point group as a suspected pole setting initial point group when more than two continuous coordinate dark points with similar pixel values and different peripheral point pixel values appear in a single-row range, combining at least 3 rows from top to bottom after each pole setting initial point group is obtained, judging that the vertical coordinate difference of starting and ending points among each row initial point group is less than 5 coordinate units and the distance difference is less than 2 coordinate units, and judging that the area in the suspected pole setting is defined as a pole setting area;
(4) Finding a rail on the acquired image: in the acquired image, the middle right lower corner of two upright post areas corresponding to the horizontal position starts to search the pixel points in the left upper corner direction transversely by taking a single pixel as a unit, when more than two continuous coordinate dark points with similar pixel values and different pixel values of peripheral points appear in a single line range, the coordinate dark point group is judged to be a suspected rail initial point group, after each rail initial point group is obtained, at least 3 lines are combined up and down, the end point longitudinal coordinate difference between the initial point groups between each line is judged to be less than 5 coordinate units, when the distance difference is less than 2 coordinate units, the suspected rail is judged, and when two suspected rails appear in a single line sampling, the region in the two suspected rails is delimited as a rail area;
(5) Analyzing pixel points line by line in an initial acquisition area, and judging a suspected foreign object area: screening out the contact net area and the vertical rod area in an acquired image, setting the rest area as an acquisition area, transversely checking the pixel points in the path by taking a single pixel as a unit in the direction of the upper left corner in the acquisition area, acquiring pixel information of unit coordinate points line by line, when the color of the edge point of the acquisition area is inconsistent with that of the adjacent point, using the distance of 2 to 19 pixels as a radius to define a secondary sampling range, checking line by line along the checking line in the step (2) in the secondary sampling range to form dark point distribution data, analyzing the data, and determining that a suspected boundary is formed when the dark point area of the checking line below the sampling area; then further enlarging the measured pixel distance until the measured pixel distance is contacted with the straight line and the vertical rod or the track or the area boundary, and judging that the measured pixel distance is the foreign matters of the mulching film and the dust suppression net;
(6) In step (5), after the determination of the suspicious boundary, the measured pixel distance is further increased until a closed edge is formed, and it is determined that the near work machine
(7) And (3) transmitting the results of the steps (5) and (6) to a total server, and guiding staff to clean.
2. The method for identifying potential high-speed rail objects according to claim 1, wherein: and counting the inclination angle of the suspected boundary, and judging suspected to further distinguish the plateau earthwork or the peripheral trees when the horizontal inclination angle is larger than 70 degrees.
3. The method for identifying potential high-speed rail objects according to claim 1, wherein: further, pixel color information of each coordinate point in the outline of the foreign matter is counted, and when the color is white and the color outside the edge is blue, the outline is judged to be white cloud, and suspected marks are marked.
4. A method of identifying high-speed rail potential hazards in a rail as defined in claim 3, wherein: and (3) judging that the mulch film and the dust suppression net are provided with the heavy mulch film and the dust suppression net foreign matters when the mulch film and the dust suppression net are in the condition of realizing the interleaving with the closed curve in the measurement result in the step (5).
5. The method for identifying potential high-speed rail objects according to claim 4, wherein: pixel color information of each coordinate point in the outline of the foreign matter, when the internal and external color difference of the outline is less than 50, judging the outline as a mulching film; when the outline is green, the dust suppression net is judged, and when the outline is yellow or blue, the engineering truck is judged.
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| CN202210717660.6A CN115100519B (en) | 2022-06-23 | 2022-06-23 | Method for identifying hidden danger objects along high-speed rail |
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| CN202210717660.6A CN115100519B (en) | 2022-06-23 | 2022-06-23 | Method for identifying hidden danger objects along high-speed rail |
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| CN115100519B true CN115100519B (en) | 2024-04-26 |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112911221A (en) * | 2021-01-15 | 2021-06-04 | 欧冶云商股份有限公司 | Remote live-action storage supervision system based on 5G and VR videos |
| CN113947731A (en) * | 2021-12-21 | 2022-01-18 | 成都中轨轨道设备有限公司 | Foreign matter identification method and system based on contact net safety inspection |
| CN114266893A (en) * | 2021-12-22 | 2022-04-01 | 智洋创新科技股份有限公司 | Smoke and fire hidden danger identification method and device |
| CN114419825A (en) * | 2022-03-29 | 2022-04-29 | 中国铁路设计集团有限公司 | High-speed rail perimeter intrusion monitoring device and method based on millimeter wave radar and camera |
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- 2022-06-23 CN CN202210717660.6A patent/CN115100519B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112911221A (en) * | 2021-01-15 | 2021-06-04 | 欧冶云商股份有限公司 | Remote live-action storage supervision system based on 5G and VR videos |
| CN113947731A (en) * | 2021-12-21 | 2022-01-18 | 成都中轨轨道设备有限公司 | Foreign matter identification method and system based on contact net safety inspection |
| CN114266893A (en) * | 2021-12-22 | 2022-04-01 | 智洋创新科技股份有限公司 | Smoke and fire hidden danger identification method and device |
| CN114419825A (en) * | 2022-03-29 | 2022-04-29 | 中国铁路设计集团有限公司 | High-speed rail perimeter intrusion monitoring device and method based on millimeter wave radar and camera |
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