CN209744069U - Be used for online inspection device of oil gas pipeline - Google Patents
Be used for online inspection device of oil gas pipeline Download PDFInfo
- Publication number
- CN209744069U CN209744069U CN201920424195.0U CN201920424195U CN209744069U CN 209744069 U CN209744069 U CN 209744069U CN 201920424195 U CN201920424195 U CN 201920424195U CN 209744069 U CN209744069 U CN 209744069U
- Authority
- CN
- China
- Prior art keywords
- module
- video
- nvidia
- unmanned aerial
- aerial vehicle
- 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.)
- Active
Links
Landscapes
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The utility model provides a pair of be used for online inspection device of oil gas pipeline, including unmanned aerial vehicle end and ground end, the unmanned aerial vehicle end is the unmanned aerial vehicle body, the unmanned aerial vehicle body includes video acquisition module, NVIDIA TX2 module and picture pass emission module, wherein, video acquisition module is used for gathering the video image of oil gas pipeline, and transmit the data message who gathers to NVIDIA TX2 module, NVIDIA TX2 module is used for handling the video that receives, obtain the video stream of marking unusual target, and transmit the video stream after will handling to the picture pass emission module; the map transmission module is used for transmitting the received data information to the ground terminal; the ground terminal comprises a picture transmission receiving module and a video display, wherein the picture transmission receiving module is used for receiving the data information transmitted by the picture transmission transmitting module and transmitting the received data information to the video display; the device can reduce the labor intensity of line patrol personnel and reduce the labor cost; and the problems of timely repairing of the oil and gas pipeline and peripheral abnormality can be found in time, the line inspection efficiency is greatly improved, and the accuracy of inspection work is improved.
Description
Technical Field
The utility model belongs to unmanned aerial vehicle technique, machine vision field, concretely relates to be used for online inspection device of oil gas pipeline.
Background
With the rapid development of national economy, the national demand for energy is increasing. Oil and gas pipelines are used as very important components in energy systems in China, and oil and gas pipelines in China have the characteristics of long total mileage, large span of construction years, multiple safety accidents and the like, so that the safety patrol management of the oil and gas pipelines needs to be continuously enhanced. And many oil and gas pipelines pass through the regions with dangerous terrain such as deserts, gobi, mountains and the like, so that the difficulty of pipeline inspection work is increased. For the traditional manual inspection, the labor cost is high and the efficiency is low due to large workload and hard conditions, and the inspection task cannot be executed safely in time under many conditions, so that a more scientific technical means is required. The unmanned aerial vehicle inspection technology is adopted, and the application trend of the petroleum industry at home and abroad is achieved.
However, in the existing drone technology, due to the limitation of the computational performance of the onboard processor, image or video processing needs to be done off-line: the unmanned aerial vehicle carries a camera to shoot images or videos in the air, and the images or videos are transmitted to a computer at the ground end in real time through a wireless communication module to be processed. In such a way, in a long-distance or high-shielding scene, the situation of losing images can occur, and thus the computer at the front end cannot acquire complete images, which is not beneficial to finding abnormal information of the oil-gas pipeline and the periphery of the oil-gas pipeline. Therefore, there is a need for improvement of the prior art to solve the above technical problems.
Disclosure of Invention
An object of the utility model is to provide a be used for online inspection device of oil gas pipeline has solved current unmanned aerial vehicle technique, uses wireless communication module remote transmission to ground end computer to handle, and this kind of processing mode has the problem that ground end computer can't acquire complete image.
In order to achieve the above purpose, the utility model discloses a technical scheme is:
the utility model provides an online inspection device for oil gas pipelines, which comprises an unmanned aerial vehicle end and a ground end, wherein the unmanned aerial vehicle end is used for collecting and marking abnormal information of the oil gas pipelines and transmitting the abnormal information to the ground end;
The unmanned aerial vehicle comprises an unmanned aerial vehicle body, wherein the unmanned aerial vehicle body comprises a video acquisition module, an NVIDIA TX2 module and an image transmission and emission module, the video acquisition module is used for acquiring video images of an oil and gas pipeline and transmitting acquired data information to the NVIDIA TX2 module, and the NVIDIA TX2 module is used for processing received videos to obtain video streams for marking abnormal targets and transmitting the processed video streams to the image transmission and emission module; the map transmission module is used for transmitting the received data information to the ground terminal;
the ground terminal comprises a picture transmission receiving module and a video display, wherein the picture transmission receiving module is used for receiving the data information transmitted by the picture transmission transmitting module and transmitting the received data information to the video display; the video display is used for displaying the video detection picture of the unmanned aerial vehicle end in real time.
preferably, the NVIDIA TX2 module is connected with the video acquisition module through a USB interface; the NVIDIA TX2 module is connected with the image transmission module through an HDMI interface.
Preferably, the NVIDIA TX2 module comprises an NVIDIA TX2 core board and an NVIDIA TX2 carrier board, wherein the NVIDIA TX2 core board and the NVIDIA TX2 carrier board are connected by connectors.
Preferably, the NVIDIA TX2 core board is an artificial intelligence single-module super computer integrating a GPU with 256 CUDA cores, a 4-core ARM Cortex-A57 and a 64-bit CPU with dual cores Denver2, and the computer runs an Ubuntu16.04Linux 64-bit operating system.
preferably, the video acquisition module comprises a high-definition camera and a high-definition image acquisition card, wherein the high-definition camera and the high-definition image acquisition card are connected through an HDMI (high-definition multimedia interface) wire; the high-definition camera is used for shooting a video of the oil and gas pipeline; the high-definition image acquisition card is used for receiving the video read by the HDMI interface and converting the received video stream into a USB interface video stream which can be identified by the NVIDIA TX2 module.
Preferably, the high definition camera is fixed on the unmanned aerial vehicle body from increasing steady cloud platform through the diaxon.
Preferably, the ground terminal further comprises a real-time image transmission terminal, and the real-time image transmission terminal is connected with the image transmission and reception module through an HDMI line and is used for enabling the patrolman to check the video detection picture in real time.
Compared with the prior art, the beneficial effects of the utility model are that:
The utility model provides a pair of be used for online inspection device of oil gas pipeline gathers the video image of oil gas pipeline through the video acquisition module to transmit the video image who gathers to NVIDIA TX2 module, handle the video image who receives through NVIDIA TX2 module, and transmit the data transmission after handling to the picture and pass emission module, pass the emission module with the data message transmission that receives to the ground end by the picture; the process avoids the problem of data information loss caused by the fact that the existing video image is transmitted to the ground end for processing; the utility model discloses utilize the unmanned aerial vehicle body to carry out oil gas pipeline and patrol the line work, and utilize NVIDIA TX2 module to carry out automatic, quick on-line measuring and accurate positioning to the unusual target (such as engineering vehicle, construction board house, crack, pot hole) that has the potential threat oil gas pipeline safety, and return target GPS positional information in real time, be convenient for patrol the accurate processing of line personnel; the device can reduce the labor intensity of line patrol personnel and reduce the labor cost; the abnormal problems of the oil gas pipeline and the periphery thereof can be timely found and repaired, the line inspection efficiency is greatly improved, and the accuracy of inspection work is improved; simultaneously, utilize NVIDIA TX2 high performance in the aspect of computer vision, the characteristics that the low energy consumption calculated reduce video processing's time, improve the utility model discloses real-time and the accuracy nature that unmanned aerial vehicle system oil gas pipeline patrolled and examined.
Further, the video acquisition module converts the video image acquired by the high-definition camera through the high-definition image acquisition card, and transmits the converted data information to the NVIDIA TX2 module to realize stable transmission of the video image.
Drawings
FIG. 1 is a block diagram of the structure of the unmanned aerial vehicle oil and gas pipeline online inspection system based on the convolutional neural network;
FIG. 2 is a flow chart of the utility model discloses unmanned aerial vehicle oil gas pipeline on-line inspection method based on convolution neural network;
Fig. 3 is the utility model discloses to the peripheral unusual target of oil gas pipeline make model training flow chart based on convolutional neural network.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
as shown in fig. 1, the utility model provides a pair of be used for online inspection device of oil gas pipeline, including unmanned aerial vehicle end and ground end, wherein, unmanned aerial vehicle end is used for gathering oil gas pipeline information to transmit the data information who gathers to the ground end.
The unmanned aerial vehicle end comprises a six-rotor unmanned aerial vehicle, two-blade paddles, a motor module, a power supply module, a flight control module, a GPS positioning module, a gyroscope, an altimeter, a two-shaft self-stability-increasing cradle head, a video acquisition module, an NVIDIA TX2 module, an airborne data transmission module and an image transmission and emission module,
Wherein, two oar are provided with six, drive through the motor module.
the motor module comprises six direct current brushless motors, and an output shaft of each direct current brushless motor is connected with one two-blade paddle and used for driving the two-blade paddles to rotate; the no-load speed of the dc brushless motor at a voltage of 48V is 1200/3200 revolutions per minute.
The power module is arranged in the unmanned aerial vehicle body, adopts a 48V lithium polymer battery and is used for providing power required by the operation of the modules, namely the unmanned aerial vehicle end flight control module, the GPS positioning module, the gyroscope, the altimeter, the motor module, the holder and the NVIDIA TX2 module.
The flight control module is used for receiving data information transmitted by the GPS positioning module, the gyroscope and the altimeter, controlling the motor module according to the received data information, adjusting the flight state of the unmanned aerial vehicle through the motor module, and further controlling the unmanned aerial vehicle to fly according to a specified air route; simultaneously, send unmanned aerial vehicle flight information data for ground data transmission module through airborne data transmission module, show on the ground station in real time.
the GPS positioning module is used for acquiring GPS position information of the unmanned aerial vehicle in real time.
The gyroscope is used for acquiring attitude data of the unmanned aerial vehicle.
The altimeter is used for measuring the flying height of the unmanned aerial vehicle.
The two-axis self-stabilization tripod head is used for stabilizing the camera, and the attitude (pitch angle and roll angle) of the camera can be adjusted through a ground remote controller.
the video acquisition module comprises a high-definition camera and a high-definition image acquisition card, wherein the high-definition camera and the high-definition image acquisition card are connected through an HDMI (high-definition multimedia interface) wire; the high-definition camera is used for shooting a video of the oil and gas pipeline; the high-definition image acquisition card is used for receiving the video read by the HDMI interface and converting the received video stream into a USB interface video stream which can be identified by NVIDIA TX 2.
The NVIDIA TX2 module consists of an NVIDIA TX2 core board and an NVIDIA TX2 carrier board: NVIDIA TX2 core board is used for realizing the unusual target detection locate function of system's oil gas pipeline based on convolutional neural network, be one the NVIDIA Pascal GPU of 256 CUDA cores of having integrateed, 4 nuclear ARM Cortex-A57 and the 64 bit CPU's of dual-core Denver2 artificial intelligence single module supercomputer, operation Ubuntu16.04Linux 64 bit operating system, powerful, the appearance is small and exquisite, energy-conserving high efficiency, powerful video processing ability has, support to operate large-scale degree of depth neural network, utilize these characteristics to reduce video processing's time, improve the utility model discloses unmanned aerial vehicle system target detection's real-time and accuracy nature.
The NVIDIA TX2 carrier board is used to implement data interface expansion of the NVIDIA TX2 core board and power output facing the system, and is connected with the NVIDIA TX2 core board by using a connector, the video acquisition module is connected with the USB interface of the NVIDIA TX2 carrier board, the flight control module is connected with the serial interface of the NVIDIA TX2 carrier board, and the image transmission module is connected with the HDMI interface of the NVIDIA TX2 carrier board.
The NVIDIA TX2 carrier board is provided with two USB interfaces, a W/OTG functional interface, an HDMI video output interface, a fan control interface for preventing the core board from overheating during the system operation, an RTC battery interface) and 24 serial interfaces, wherein the power supply voltage: + 7V- + 19V.
the airborne data transmission module is connected with the flight control module through the asynchronous receiving and transmitting transmitter, receives the ground station control instruction transmitted by the ground data transmission module on one hand, and transmits the data of the flight control module, the gyroscope, the altimeter and the GPS positioning module on the other hand.
And the image transmission module is connected with the NVIDIA TX2 module through an HDMI line and transmits an online detection picture of the NVIDIA TX2 module.
The ground terminal comprises a ground station, a remote controller, a ground data transmission module, a picture transmission receiving module, a video display and a real-time picture transmission terminal.
The ground station sends a control command through communication between the ground data transmission module and the airborne data transmission module on the one hand, issues an inspection task, plans the flight route of the unmanned aerial vehicle, guides the unmanned aerial vehicle to fly autonomously, and receives and displays flight state information of the unmanned aerial vehicle in real time on the one hand, wherein the flight state information comprises flight tracks, flight heights and GPS position information.
the remote controller is operated by the flying hand, can switch unmanned aerial vehicle flight module: the autonomous flight and the manual remote control flight can also control the tripod head to adjust the shooting angle of the camera.
The ground data transmission module is connected with the ground station through a USB line, on one hand, receives data transmitted by the airborne data transmission module, and on the other hand, transmits a control instruction of the ground station.
and the picture transmission receiving module is used for receiving the video detection picture transmitted by the picture transmission transmitting module.
And the video display is connected with the image transmission and receiving module through an HDMI line and displays a video detection image of the unmanned aerial vehicle end in real time.
the real-time image transmission terminal is connected with the image transmission and receiving module through an HDMI line, and the received video detection picture of the unmanned aerial vehicle end is published on the Internet in real time through a 3G/4G network, so that a patrol worker can check the video detection picture in real time by opening a specific webpage at any place.
As shown in fig. 2, the utility model discloses an unmanned aerial vehicle oil gas pipeline on-line inspection method based on convolution neural network, include following step:
Step 1, preparation work before takeoff:
s11, pressing the high-definition camera switch button to set the high-definition camera to be in a video mode, setting the resolution to be 1080P, setting the frame rate to be 30 frames and setting the view field to be a narrow view field, because the wide view field can distort the image and influence the detection effect;
S12, the unmanned aerial vehicle is powered on, power is supplied to the motor module, the flight control module, the GPS positioning module, the gyroscope, the altimeter, the airborne data transmission module, the image transmission and emission module, the two-axis self-stability-increasing cradle head and the NVIDIA TX2 module, and all modules at the unmanned aerial vehicle end start to work;
And S13, opening the ground station, and setting the flight line and the flight height of the unmanned aerial vehicle according to the oil and gas pipeline track information.
and 2, starting inspection:
s21, setting the ground remote controller to be in a manual mode, sending a flight command signal to unlock a motor command, sending a starting signal to six direct current brushless motors after the flight control module receives the command, and enabling the motors to enter an idle rotation state; then, the remote controller sends a takeoff command to the flight control module to enter a takeoff state, and the unmanned aerial vehicle takes off and enters a hovering state when reaching a set height;
s22, setting the ground remote controller into an automatic mode, and enabling the unmanned aerial vehicle to start stable flight according to a planned route in advance of the ground station;
s23, the video acquisition module starts to acquire images to form a frame-by-frame video stream;
s24, acquiring and processing the video stream output by the image acquisition module in real time by the NVIDIA TX2 module, detecting and positioning abnormal targets such as engineering vehicles, construction board houses, cracks, pot holes and the like existing around the oil and gas pipeline on line based on a convolutional neural network, extracting image coordinates and actual geographic GPS coordinates of the targets, and displaying the coordinates on an output detection picture in real time;
Meanwhile, the NVIDIA TX2 module acquires the GPS information and the time information sent by the flight control module in real time and displays the GPS information and the time information on an output detection picture in real time;
S25, the image transmission module receives the detection video output information of NVIDIA TX2 in real time, and the ground-end image transmission receiving module receives the detection video information and displays the detection video information on a video display, so that a user can check the abnormal information around the air route in the unmanned aerial vehicle operation process in real time, and timely discovery and timely processing can be realized; meanwhile, the real-time image transmission terminal issues the received detection video information to a specific webpage, and a user can also check abnormal information around the air route in the unmanned aerial vehicle operation process at any place in real time, so that timely discovery and timely processing can be achieved.
In S24, the online detection method based on the convolutional neural network of the unmanned aerial vehicle system is:
The method comprises the steps of directly detecting a current frame image of an input video by using a pre-trained detection model file based on a convolutional neural network so as to detect and identify abnormal targets such as engineering vehicles, construction board houses, cracks and pot holes and the like in the image, which potentially threaten the safety of an oil and gas pipeline, and outputting and displaying the abnormal targets by using rectangular frame marks.
As shown in fig. 3, the training method of the detection model file based on the convolutional neural network is as follows:
firstly, acquiring a large amount of aerial videos or pictures about the oil and gas pipeline and the periphery of the oil and gas pipeline;
Then, manually labeling or automatically labeling by using a trained model file to obtain an abnormal target sample set: the method comprises the steps of collecting engineering vehicle samples, collecting construction board room samples, collecting crack samples and collecting pit hole samples, and ensuring that the data volume among the sample sets is maintained within one order of magnitude;
and then, training by adopting the convolutional neural network to obtain a model file.
the model file training method can also be used for repeatedly training by expanding the abnormal target sample set in the later-stage line patrol work, so that the recall ratio and precision ratio of the model file can be further improved.
in S24, the specific process of the online positioning method of the unmanned aerial vehicle system is as follows:
Firstly, establishing a transformation relation between an image pixel coordinate system and a geographic coordinate system by inputting a current frame image of a video, camera internal parameters, a corresponding POS position and GPS information;
then, distortion correction is carried out on the current frame image;
Finally, the image pixel coordinates of the detected target are converted into the actual corresponding GPS coordinate position through the transformation relation, so that accurate positioning is realized, the inspection time is further shortened, and the inspection efficiency is improved.
The above-mentioned embodiments are intended to illustrate, not limit, the invention, and any modifications and changes made to the invention within the spirit and scope of the claims fall within the scope of the invention.
Claims (7)
1. The online inspection device for the oil-gas pipeline is characterized by comprising an unmanned aerial vehicle end and a ground end, wherein the unmanned aerial vehicle end is used for collecting and marking abnormal information of the oil-gas pipeline and transmitting the abnormal information to the ground end;
the unmanned aerial vehicle comprises an unmanned aerial vehicle body, wherein the unmanned aerial vehicle body comprises a video acquisition module, an NVIDIA TX2 module and an image transmission and emission module, the video acquisition module is used for acquiring video images of an oil and gas pipeline and transmitting acquired data information to the NVIDIA TX2 module, and the NVIDIA TX2 module is used for processing received videos to obtain video streams for marking abnormal targets and transmitting the processed video streams to the image transmission and emission module; the map transmission module is used for transmitting the received data information to the ground terminal;
The ground terminal comprises a picture transmission receiving module and a video display, wherein the picture transmission receiving module is used for receiving the data information transmitted by the picture transmission transmitting module and transmitting the received data information to the video display; the video display is used for displaying the video detection picture of the unmanned aerial vehicle end in real time.
2. the online inspection device for the oil and gas pipelines according to claim 1, wherein the NVIDIA TX2 module is connected with the video acquisition module through a USB interface; the NVIDIA TX2 module is connected with the image transmission module through an HDMI interface.
3. The online inspection device for oil and gas pipelines according to claim 2, wherein the NVIDIA TX2 module comprises an NVIDIA TX2 core board and an NVIDIA TX2 carrier board, wherein the NVIDIA TX2 core board and the NVIDIA TX2 carrier board are connected through a connector.
4. The on-line inspection device for oil and gas pipelines according to claim 3, wherein the NVIDIA TX2 core board is an artificial intelligence single-module super computer integrating a GPU with 256 CUDA cores, a 4-core ARM Cortex-A57 and a 64-bit CPU with dual cores Denver2, and the computer runs an Ubuntu16.04Linux 64-bit operating system.
5. The on-line inspection device for the oil and gas pipeline according to claim 1, wherein the video acquisition module comprises a high-definition camera and a high-definition image acquisition card, wherein the high-definition camera and the high-definition image acquisition card are connected through an HDMI (high-definition multimedia interface) line; the high-definition camera is used for shooting a video of the oil and gas pipeline; the high-definition image acquisition card is used for receiving the video read by the HDMI interface and converting the received video stream into a USB interface video stream which can be identified by the NVIDIA TX2 module.
6. The on-line inspection device for the oil and gas pipelines according to claim 5, wherein the high-definition camera is fixed on the unmanned aerial vehicle body through a two-axis self-stability-increasing cradle head.
7. the on-line inspection device for the oil and gas pipelines according to claim 1, wherein the ground end further comprises a real-time image transmission terminal, and the real-time image transmission terminal is connected with the image transmission and reception module through an HDMI line and is used for enabling an inspector to check a video detection image in real time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201920424195.0U CN209744069U (en) | 2019-03-29 | 2019-03-29 | Be used for online inspection device of oil gas pipeline |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201920424195.0U CN209744069U (en) | 2019-03-29 | 2019-03-29 | Be used for online inspection device of oil gas pipeline |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN209744069U true CN209744069U (en) | 2019-12-06 |
Family
ID=68719212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201920424195.0U Active CN209744069U (en) | 2019-03-29 | 2019-03-29 | Be used for online inspection device of oil gas pipeline |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN209744069U (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111614969A (en) * | 2020-05-14 | 2020-09-01 | 深圳供电局有限公司 | Unmanned aerial vehicle tour video live broadcast method and system |
| CN112378924A (en) * | 2020-09-24 | 2021-02-19 | 宁波市鄞州世纪耀达市政建设有限公司 | Pipeline crack positioning method and system, storage medium and intelligent terminal |
| CN112923242A (en) * | 2021-01-22 | 2021-06-08 | 西安万飞控制科技有限公司 | GIS (gas insulated switchgear) system and method for inspecting oil and gas pipelines |
| CN114582037A (en) * | 2022-02-28 | 2022-06-03 | 成都商汤科技有限公司 | Inspection method and device, electronic equipment and computer readable storage medium |
-
2019
- 2019-03-29 CN CN201920424195.0U patent/CN209744069U/en active Active
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111614969A (en) * | 2020-05-14 | 2020-09-01 | 深圳供电局有限公司 | Unmanned aerial vehicle tour video live broadcast method and system |
| CN112378924A (en) * | 2020-09-24 | 2021-02-19 | 宁波市鄞州世纪耀达市政建设有限公司 | Pipeline crack positioning method and system, storage medium and intelligent terminal |
| CN112923242A (en) * | 2021-01-22 | 2021-06-08 | 西安万飞控制科技有限公司 | GIS (gas insulated switchgear) system and method for inspecting oil and gas pipelines |
| CN112923242B (en) * | 2021-01-22 | 2023-11-14 | 西安万飞控制科技有限公司 | GIS system and method for inspecting oil and gas pipeline |
| CN114582037A (en) * | 2022-02-28 | 2022-06-03 | 成都商汤科技有限公司 | Inspection method and device, electronic equipment and computer readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN209744069U (en) | Be used for online inspection device of oil gas pipeline | |
| CN202043246U (en) | Remote data collector for fixed wing unmanned plane line inspection | |
| CN107664491B (en) | Method, device and system for measuring downward inclination angle of base station antenna | |
| CN102570345B (en) | UAV (unmanned aerial vehicle) transmission-line patrolling system | |
| CN103455036B (en) | A kind of scene aerial patrol method and aircraft | |
| CN108334104A (en) | A kind of autonomous cruising inspection system of unmanned plane and method based on RTK positioning | |
| CN202068445U (en) | Mobile monitoring system | |
| CN105511495A (en) | Control method and system for intelligent unmanned aerial vehicle patrol for power line | |
| CN108257145A (en) | A kind of UAV Intelligent based on AR technologies scouts processing system and method | |
| CN112379627B (en) | Positioning system and positioning method for assembled building | |
| CN104076820A (en) | Unmanned aerial vehicle electric power line polling control system and method based on three-dimensional GIS | |
| CN111832379A (en) | Unmanned aerial vehicle real-time video detection system based on convolutional neural network | |
| CN202197057U (en) | UAV (unmanned aerial vehicle) aircraft patrol system for power transmission line | |
| CN112437118A (en) | Road street lamp inspection system based on unmanned aerial vehicle | |
| CN104085529A (en) | Eight-rotor-wing unmanned plane system | |
| CN104683759A (en) | Wireless video monitoring equipment and method based on aircraft for chimney inner wall corrosion condition | |
| CN206862937U (en) | Photovoltaic plant unmanned plane detector | |
| CN111427054A (en) | Accurate ranging system of power transmission and distribution line way hidden danger | |
| CN109346991B (en) | FPV technology and many rotor unmanned aerial vehicle combine together portable electric power inspection device | |
| CN114003058B (en) | Intelligent inspection system and method for wearing safety helmet | |
| CN208332046U (en) | It can start and monitor the multi-functional street lamp of unmanned plane during flying state | |
| CN112783204B (en) | Real-time tracking system and method for ground maneuvering target of four-rotor unmanned aerial vehicle | |
| CN204291244U (en) | Electric power depopulated helicopter visible ray accurate detection system | |
| CN209168187U (en) | Electric power line patrol system | |
| CN210199580U (en) | Unmanned aerial vehicle is patrolled and examined to distribution network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant |