CN116758465A - Gas pipeline vehicle-mounted inspection system based on dynamic AI vision - Google Patents
Gas pipeline vehicle-mounted inspection system based on dynamic AI vision Download PDFInfo
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- CN116758465A CN116758465A CN202310161320.4A CN202310161320A CN116758465A CN 116758465 A CN116758465 A CN 116758465A CN 202310161320 A CN202310161320 A CN 202310161320A CN 116758465 A CN116758465 A CN 116758465A
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- 238000007689 inspection Methods 0.000 title claims abstract description 47
- 238000010276 construction Methods 0.000 claims abstract description 78
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000004891 communication Methods 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 14
- 239000007789 gas Substances 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 8
- 239000002737 fuel gas Substances 0.000 claims description 3
- 238000011282 treatment Methods 0.000 abstract 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
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- 238000009412 basement excavation Methods 0.000 description 1
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- 238000002955 isolation Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
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Abstract
The invention discloses a dynamic AI vision-based gas pipeline vehicle-mounted inspection system, which comprises inspection equipment and terminal equipment, wherein the inspection equipment comprises a camera for collecting image information of a construction area and a relay processing device, and the relay processing device comprises a processing module for processing the image information, an alarm module for alarming a monitoring area, an AI identification unit for identifying and classifying the construction area, a serial port communication module for communicating with the camera, a wireless communication module for communicating with the terminal equipment and a power supply module for supplying power; according to the invention, the vehicle-mounted dynamic identification inspection is realized, and the identification result is transmitted to the terminal equipment through the wireless communication module, so that a worker at the terminal equipment can grasp the condition of the construction area in real time and can carry out alarm and other treatments according to the condition of the construction area, so that the gas pipeline is ensured not to be damaged, the intelligent identification of the construction condition near the gas pipeline is realized, and the intelligent degree is high.
Description
Technical Field
The invention relates to the technical field of fuel gas, in particular to a fuel gas pipeline vehicle-mounted inspection system based on dynamic AI vision.
Background
With the continuous development of cities, natural gas pipe networks are continuously expanded, and the gas pipe networks in the current I area are distributed over two streets and five towns, so that underground gas pipe networks are complicated. In order to prevent the damage of a third party, I invest a large amount of manpower to patrol the gas pipe network.
At present, no intelligent inspection equipment for the buried gas pipe network has been found in society, and similar unmanned aerial vehicle AI inspection equipment with a power supply system is provided, but the AI system model is single, and high-precision equipment is required. Meanwhile, the unmanned aerial vehicle is only suitable for inspection in open field environments and is not suitable for working in the air in cities.
The publication number is CN108256598A, a construction monitoring method is disclosed, which comprises the following steps: step one: registering the identity information of all constructors; step two: identifying the identity information of constructors, and allowing the constructors to enter a construction area; step three: recording a site structure of a construction site, dividing a safety area, and monitoring structural potential safety hazards of the site in real time; step four: remote monitoring of construction sites and command construction; step five: and tracking and shooting the construction process, analyzing the construction information in real time, and automatically classifying and archiving. The electronic tag containing the identity information of the constructor can be used for comparing with preset information, constructors entering the construction site can be accurately managed, remote management of the construction site is facilitated, potential safety hazards of the construction site are monitored in real time, construction processes are recorded and analyzed in real time, safety of the construction site is improved to a certain extent, and the constructors and the manager can communicate with the site conveniently through voice communication and an infrared camera, so that errors possibly caused by simple voice communication are avoided. The method can not realize the monitoring and identification of various conditions, articles and personnel in the construction area.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a vehicle-mounted inspection system of a gas pipeline based on dynamic AI vision, which has high intelligent degree.
In order to achieve the above purpose, the present invention provides the following solutions: the system comprises inspection equipment and terminal equipment, wherein the inspection equipment comprises a camera and a relay processing device, the camera is used for collecting image information of a construction area, the relay processing device comprises a processing module for processing the image information, an alarm module for alarming a monitoring area, an AI (analog to digital) identification unit for identifying and classifying the construction area, a serial port communication module for communicating with the camera, a wireless communication module for communicating with the terminal equipment and a power supply module for supplying power; the terminal equipment is used for receiving information sent by the inspection equipment and remotely controlling the inspection equipment; after the AI identification unit identifies the monitoring area information, the inspection equipment sends the identified video and image to the terminal equipment.
The beneficial effects of the invention are as follows: realize dynamic intelligent identification, this inspection system is through setting up inspection equipment, terminal equipment, wherein, utilize the camera of inspection equipment in order to accurately collect the video and the image of construction region, then carry out discernment through the AI identification cell of inspection equipment to video and the picture that the camera carried, enclose the worker in order to accurately discern the building site, construction machinery, construction tool, gas pipe sign etc., realize on-vehicle dynamic identification inspection, rethread wireless communication module carries the recognition result to terminal equipment, make the staff of terminal equipment department grasp the construction region condition in real time, and can report to the police etc. according to the construction region condition and handle, so as to ensure that the gas pipeline can not be destroyed, realize the intelligent identification of the nearby construction condition of gas pipeline, intelligent degree is high.
Further, a plurality of sensors are arranged in the camera. By adopting the structure, the invention realizes the collection of different types of videos and images.
Further, the information includes one or more of a construction vehicle, a construction article, a covered construction site, a constructor, a miniature construction site, a video and an image of a construction font.
Further, the AI identification unit comprises a first identification module, a second identification module, a third identification module, a fourth identification module, a fifth identification module and a sixth identification module;
the first identification module is used for identifying the engineering vehicle;
the second identification module is used for identifying construction articles;
the third identification module is used for identifying the enclosing construction site;
the fourth identification module is used for identifying constructors;
the fifth identification module is used for identifying a small-sized worksite;
and the sixth identification module is used for identifying the construction fonts.
Further, the AI identification unit includes a target detection algorithm, where the target detection algorithm is used to control the first identification module, the second identification module, the third identification module, the fourth identification module, the fifth identification module, and the sixth identification module to identify the image, and the target detection algorithm includes an input end module, a reference network module, a neg network module, and a Head output layer module, where the input end modules are respectively basic network modules, the reference network module is connected with the neg network module, and the neg network module is connected with the Head output layer module.
Further, the relay processing device further comprises a GPS module, and the GPS module is used for positioning the inspection equipment. After the structure is adopted, the inspection equipment has a positioning function, so that a worker can conveniently control the mobile inspection equipment remotely.
In the invention, the method for identifying the construction area by the inspection system comprises the following steps:
s1, setting a construction area to be monitored in the background, setting coordinates, and monitoring the construction area in real time;
s2, extracting a high-definition image from a video frame sequence shot in a construction area through a camera, and then sending the high-definition image to an AI identification unit through a serial port communication module for identification;
s3, presetting an information comparison image in the background, and then enabling a processing module to operate a first recognition module, a second recognition module, a third recognition module, a fourth recognition module, a fifth recognition module and a sixth recognition module in the AI recognition unit in parallel;
s4, the AI recognition unit performs comparison judgment on the high-definition image and the preset information comparison image based on a target detection algorithm of the depth convolution network, so that dynamic target recognition is completed at the edge end;
s5, sending the identified result to the terminal equipment through the wireless communication module, and controlling the alarm module to alarm by a background personnel according to the result.
Drawings
Fig. 1 is a block diagram of the present invention.
Fig. 2 is a schematic diagram of the objective algorithm of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made more complete and less obvious to those skilled in the art, based on the embodiments of the present invention, for a part, but not all of the embodiments of the present invention, without making any inventive effort.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, a vehicle-mounted inspection system of a gas pipeline based on dynamic AI vision comprises inspection equipment and terminal equipment, wherein the inspection equipment comprises a camera for collecting image information of a construction area and a relay processing device, and the relay processing device comprises a processing module for processing the image information, an alarm module for alarming a monitoring area, an AI (analog input) identification unit for identifying and classifying the construction area, a serial port communication module for communicating with the camera, a wireless communication module for communicating with the terminal equipment and a power supply module for supplying power; the terminal equipment is used for receiving the information sent by the inspection equipment and remotely controlling the inspection equipment; after the AI identification unit identifies the monitoring area information, the inspection equipment sends the identified video and image to the terminal equipment.
In this embodiment, a plurality of sensors are provided in the camera.
In this embodiment, the information includes one or more of a construction vehicle, a construction article, a covered construction site, a constructor, a small construction site, a video and an image of a construction font.
In this embodiment, the AI identification unit includes a first identification module, a second identification module, a third identification module, a fourth identification module, a fifth identification module, and a sixth identification module;
the first identification module is used for identifying the engineering vehicle; engineering vehicles such as pick-up cards, common trucks, box trucks, bulldozers, fork trucks, cranes, road rollers, excavators, concrete trucks, pipe push bench, drilling rigs and the like can be identified.
The second identification module is used for identifying the construction article; construction articles such as construction fence, road cone, iron horse guardrail, anti-collision barrel, ice cream barrel, plastic isolation pier, construction warning board and the like can be identified.
The third identification module is used for identifying the enclosing construction site; articles such as enclosed construction sites and the like, such as blue large fences, can be identified; the enclosing change of the same construction site at different times can be compared and identified.
The fourth identification module is used for identifying constructors; the personnel wearing the safety helmet and wearing the specific clothes can be identified.
The fifth identification module is used for identifying the miniature construction site; the small-sized excavation construction site surrounded by the gas construction fence can be identified.
And the sixth identification module is used for identifying the construction fonts. The warning board with the construction two words and other warning marks can be identified.
In this embodiment, the AI identification unit includes a target detection algorithm, where the target detection algorithm is used to control the first identification module, the second identification module, the third identification module, the fourth identification module, the fifth identification module, and the sixth identification module to identify images, and the target detection algorithm includes an input end module, a reference network module, a neg network module, and a Head output layer module, where the input end modules are respectively basic network modules, the reference network module is connected to the neg network module, and the neg network module is connected to the Head output layer module;
the target detection algorithm adopts a YOLOV5 algorithm, wherein an input end module: in the model training stage, adding functions, mainly comprising the steps of Mosaic data enhancement, self-adaptive anchor frame calculation and self-adaptive picture scaling;
reference network module: fusing partial functions and structures in other detection algorithms, wherein the partial functions and structures mainly comprise a Focus structure and a CSP structure;
the Neck network module: the target detection network often inserts layers between the BackBone and the last Head output layer module, and adds an FPN+PAN structure;
head output layer module: the anchor frame mechanism of the output layer module is the same as that of YOLOv4, and the main improvement is the Loss function giou_loss during training and diou_nms of prediction frame screening.
In this embodiment, the relay processing apparatus further includes a GPS module, where the GPS module is configured to locate the inspection device.
In this embodiment, the method for identifying a construction area by the inspection system includes the following steps:
s1, setting a construction area to be monitored in the background, setting coordinates, and monitoring the construction area in real time.
S2, extracting high-definition images from a video frame sequence shot in a construction area through a camera, enabling a plurality of sensors in the camera to rapidly move different types of videos and pictures in the construction area so as to accurately acquire the high-definition images and the videos, and then sending relevant high-definition images to a processing module through a serial port communication module.
S3, presetting an information comparison image in the background, and then enabling the processing module to operate a first recognition module, a second recognition module, a third recognition module, a fourth recognition module, a fifth recognition module and a sixth recognition module in the AI recognition unit in parallel.
S4, the AI recognition unit is based on a target detection algorithm of the deep convolutional network, and then the first recognition module, the second recognition module, the third recognition module, the fourth recognition module, the fifth recognition module and the sixth recognition module sequentially conduct comparison judgment on the high-definition image and the preset information comparison image, so that dynamic target recognition is completed at the edge end.
S5, sending the identified result to the terminal equipment through the wireless communication module, and controlling the alarm module to alarm by a background personnel according to the result.
The method comprises the steps that a background person can determine the position of the inspection equipment through a GPS module, then the background person can control a vehicle with the inspection equipment to move in a construction area according to the condition of a construction site so as to accurately collect information of the construction area, ensure whether a gas pipeline is damaged or not and give an alarm in time.
The above-described embodiments are merely preferred embodiments of the present invention, and are not intended to limit the present invention in any way. Any person skilled in the art can make many more possible variations and modifications of the technical solution of the present invention or modify equivalent embodiments without departing from the scope of the technical solution of the present invention by using the technical content disclosed above. Therefore, all equivalent changes according to the inventive concept are covered by the protection scope of the invention without departing from the technical scheme of the invention.
Claims (7)
1. The utility model provides a on-vehicle system of patrolling and examining of gas line based on dynamic AI vision, includes equipment, terminal equipment, its characterized in that patrols and examines: the inspection equipment comprises a camera and a relay processing device, wherein the camera is used for collecting image information of a construction area, the relay processing device comprises a processing module used for processing the image information, an alarm module used for alarming a monitoring area, an AI (analog to digital) identification unit used for identifying and classifying the construction area, a serial port communication module used for communicating with the camera, a wireless communication module used for communicating with terminal equipment and a power supply module used for supplying power; the terminal equipment is used for receiving information sent by the inspection equipment and remotely controlling the inspection equipment; after the AI identification unit identifies the monitoring area information, the inspection equipment sends the identified video and image to the terminal equipment.
2. The dynamic AI vision-based gas line vehicle-mounted inspection system of claim 1, wherein: a plurality of sensors are arranged in the camera.
3. The dynamic AI vision-based gas line vehicle-mounted inspection system of claim 2, wherein: the information comprises one or more of a video and an image of an engineering vehicle, a construction article, a surrounding construction site, a constructor, a small construction site and a construction font.
4. The fuel gas line vehicle-mounted inspection system based on dynamic AI vision according to claim 3, wherein: the AI identification unit comprises a first identification module, a second identification module, a third identification module, a fourth identification module, a fifth identification module and a sixth identification module;
the first identification module is used for identifying the engineering vehicle;
the second identification module is used for identifying construction articles;
the third identification module is used for identifying the enclosing construction site;
the fourth identification module is used for identifying constructors;
the fifth identification module is used for identifying a small-sized worksite;
and the sixth identification module is used for identifying the construction fonts.
5. The dynamic AI vision-based on-board inspection system for gas pipelines of claim 4, wherein: the AI recognition unit comprises a target detection algorithm, wherein the target detection algorithm is used for controlling a first recognition module, a second recognition module, a third recognition module, a fourth recognition module, a fifth recognition module and a sixth recognition module to recognize images, the target detection algorithm comprises an input end module, a reference network module, a Neck network module and a Head output layer module, the input end modules are respectively basic network modules, the reference network module is connected with the Neck network module, and the Neck network module is connected with the Head output layer module.
6. The dynamic AI vision-based on-board inspection system for gas pipelines of claim 5, wherein: the relay processing device further comprises a GPS module, wherein the GPS module is used for positioning the inspection equipment.
7. The dynamic AI vision-based on-board inspection system for gas pipelines of claim 6, wherein: the method for identifying the construction area by the inspection system comprises the following steps:
s1, setting a construction area to be monitored in the background, setting coordinates, and monitoring the construction area in real time;
s2, extracting a high-definition image from a video frame sequence shot in a construction area through a camera, and then sending the high-definition image to an AI identification unit through a serial port communication module for identification;
s3, presetting an information comparison image in the background, and then enabling a processing module to operate a first recognition module, a second recognition module, a third recognition module, a fourth recognition module, a fifth recognition module and a sixth recognition module in the AI recognition unit in parallel;
s4, the AI recognition unit performs comparison judgment on the high-definition image and the preset information comparison image based on a target detection algorithm of the depth convolution network, so that dynamic target recognition is completed at the edge end;
s5, sending the identified result to the terminal equipment through the wireless communication module, and controlling the alarm module to alarm by a background personnel according to the result.
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| CN202310161320.4A CN116758465A (en) | 2023-02-24 | 2023-02-24 | Gas pipeline vehicle-mounted inspection system based on dynamic AI vision |
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| CN202310161320.4A CN116758465A (en) | 2023-02-24 | 2023-02-24 | Gas pipeline vehicle-mounted inspection system based on dynamic AI vision |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372919A (en) * | 2023-09-22 | 2024-01-09 | 北京市燃气集团有限责任公司 | Third party construction threat detection method and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372919A (en) * | 2023-09-22 | 2024-01-09 | 北京市燃气集团有限责任公司 | Third party construction threat detection method and device |
| CN117372919B (en) * | 2023-09-22 | 2024-07-19 | 北京市燃气集团有限责任公司 | Third party construction threat detection method and device |
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