WO2024069215A1 - The unmanned aerial vehicles with a hybrid structure for road crack detection by using image processing techniques - Google Patents
The unmanned aerial vehicles with a hybrid structure for road crack detection by using image processing techniques Download PDFInfo
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- WO2024069215A1 WO2024069215A1 PCT/IB2022/059339 IB2022059339W WO2024069215A1 WO 2024069215 A1 WO2024069215 A1 WO 2024069215A1 IB 2022059339 W IB2022059339 W IB 2022059339W WO 2024069215 A1 WO2024069215 A1 WO 2024069215A1
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- WO
- WIPO (PCT)
- Prior art keywords
- balloon
- uav
- drone
- image processing
- road
- Prior art date
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 title claims abstract description 22
- 238000010801 machine learning Methods 0.000 claims abstract description 18
- 230000007547 defect Effects 0.000 claims abstract description 13
- 238000005516 engineering process Methods 0.000 claims abstract description 11
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 4
- 238000007667 floating Methods 0.000 claims abstract description 4
- 239000007789 gas Substances 0.000 claims description 13
- 229920000515 polycarbonate Polymers 0.000 claims description 8
- 239000004417 polycarbonate Substances 0.000 claims description 8
- 239000001307 helium Substances 0.000 claims description 5
- 229910052734 helium Inorganic materials 0.000 claims description 5
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 4
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000003384 imaging method Methods 0.000 claims description 2
- 230000000877 morphologic effect Effects 0.000 claims description 2
- 238000005325 percolation Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims description 2
- 230000008901 benefit Effects 0.000 description 5
- 230000006378 damage Effects 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 235000019687 Lamb Nutrition 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000011179 visual inspection Methods 0.000 description 2
- 238000006424 Flood reaction Methods 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009429 distress Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 230000009528 severe injury Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64B—LIGHTER-THAN AIR AIRCRAFT
- B64B1/00—Lighter-than-air aircraft
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Abstract
using UAV technology can be applied to road crack detection and using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. The UAV uses a hybrid structure including a quadcopter and a balloon. part of the required thrust force of the aircraft is provided through its balloon mechanism, but the propellers of the aircraft play a key role in keeping the UAV floating and guiding it to be properly placed on the road. this UAV uses two parts of hardware and software (artificial intelligence).
Description
using UAV technology can be applied to road crack detection and using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. The UAV uses a hybrid structure including a quadcopter and a balloon. part of the required thrust force of the aircraft is provided through its balloon mechanism, but the propellers of the aircraft play a key role in keeping the UAV floating and guiding it to be properly placed on the road. this UAV uses two parts of hardware and software (artificial intelligence).
G01B5/28 – B64D27/00 – B64C27/00
Automated defect detection of corrosion or cracks using SAFT processed Lamb wave images
United States Patent 7783433
A system, method, and computer program product are provided for automated defect detection of corrosion or cracks using synthetic aperture focusing technique (SAFT) processed Lamb wave images. The method comprises processing the first image using a synthetic aperture focusing technique (SAFT) to enhance the resolution and a signal to noise ratio of a first extracted ultrasonic image, applying a systemic background noise suppression algorithm to the first extracted ultrasonic image to render a second extracted ultrasonic image having reduced noise, and applying a deconvolution linear filtering process to the second extracted ultrasonic image to render a third extracted ultrasonic image.
Hybrid airship
United States Patent Application 20180022461
A hybrid airship (drone, UAV) capable of significantly extended flight times can use one of two technologies, or both together. The first technology uses a combination of lifting gas (such as hydrogen or helium) in a central volume or balloon and multirotor technology for lift and maneuvering. The second technology equips the airship with an onboard generator to charge the batteries during flight for extended flight operations, with an internal combustion engine (such as a high power-to-weight ratio gas turbine engine) driving the generator. A quadcopter or other multicopter configuration is desirable.
In manual road inspection, raters go all over the road measuring its distress elements, but these surveys become too laborious and slow to allow an extensive assessment. These inspections are costly and risky for the personnel, due to traffic hazards. They also have problems associated with variability and repeatability resulting in inconsistencies in details. Therefore, manual road surveys do not allow one to carry out proper road maintenance. An automated detection system to quantify the quality of road surfaces and assist in prioritizing and planning the maintenance of the road becomes essential. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve detection efficiency and produce huge economic benefits. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection.
Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Cracks are the most significant pre-disaster of a road and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the detection efficiency is low. A road detection vehicle can speed up the efficiency to a certain extent, but the automation level is low and it is easy to block the traffic. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve detection efficiency and produce huge economic benefits. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection.
Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve detection efficiency and produce huge economic benefits. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. The UAV uses a hybrid structure including a quadcopter and a balloon. This structure will have higher operational power. Also, the flight time and distance will be longer and the speed will be slower. In this way, part of the required thrust force of the aircraft is provided through its balloon mechanism, but the propellers of the aircraft play a key role in keeping the UAV floating and guiding it to be properly placed on the road.
Hardware section:
Our UAV uses an innovative hybrid structure of drones and balloons, which can therefore achieve both drone agility and balloon flight continuity, the aircraft has a UFO structure [1] with a balloon in the center [2]. The diameter of this balloon can be changed through the mechanical mechanism built into it [18,19], and as a result, the volume of gas inside it can be changed depending on the required weight and altitude. The balloon mechanism [2] carries between 60% and 100% of the drone's weight and the rotors [8] provide up to 40% of the required thrust force.
The drone uses flexible solar panels [10] that enable it to supply some of its energy needs from solar energy and increase its flight continuity. And in some cases, do not need to return to the base to recharge. 4 rotors [9] which are surrounded by a polycarbonate fan cover [7] are installed in a rectangular shape around the balloon [2] and inside the body of the drone, which provides the remaining required thrust force and determines its direction of movement by changing the speeds. 8 sensors installed around the body of the drone helps to prevent collisions with obstacles.
A gas relief valve [6] helps the balloon to empty the gas inside the balloon [2] if needed and thereby reducing the flying altitude of the drone. Also, a compressed helium tank [5] is also considered to inject the required gas into the balloon [2] in a controlled manner to increase the flying height of the drone.
UAV batteries [4] are placed in four parts inside the drone body [1], and these batteries [4] are charged during flight through flexible solar cells [10] installed on the upper part of the balloon.
The UAV uses a folding landing gear mechanism [15], these landing gear are connected to the body [1] by joints [14] and are folded into the UAV body [1] during flight.
The diameter of the flying balloon [2] can be reduced and increased through the actuator [18] and the ball-screw mechanism [19] built into the body of the balloon. two plates, one above the balloon [11] and the other below the balloon [3], changing the length of the vertical axis located in the center of the balloon will shrink or expand the balloon [2]. This mechanism is connected to the drone body [1] by three handles [17].
Main computers and flight control systems [13], imaging equipment, and sensors [12] are located under the balloon [2] and are attached to the body [1] by the mechanism inside the balloon [18,19] and connected to the body [1] by handles [17].
Artificial Intelligence Section:
Our methods involve capturing images of the target component (roads) and analyzing them programmatically to find and classify cracks. The method is fast, less expensive, and robust. We employ image processing and machine learning techniques. Image processing involves the use of filters, morphological analysis, statistical methods, and percolation techniques for the detection of cracks; the machine learning process involves the collection of a dataset of images, which are supplied to the selected machine learning model for training.
We designed a noble method of detecting road cracks to improve the output of our system and to have the best performance based on our requirements. Our method is fast, less expensive, and robust. It involves pre-processing the captured images to remove noise and improve data quality, clustering and detecting cracks using ROBs, and combining and growing the clusters using a network based on deep Q-learning that improves as we feed more input into it.
It is possible to detect road defects without the use of workers
It is economical
It uses the hybrid mode of balloon and drone
It uses an image processing mechanism
It is enhanced using machine learning
Examples
UAV technology can be applied to road crack detection, it will greatly improve detection efficiency and produce huge economic benefits. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection.
This design can be used on all roads and highways. Also, the road and urban development department as well as seismography can use this device to detect road defects and cracks.
Claims (19)
- using UAV technology can be applied to road crack detection and using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. The UAV uses a hybrid structure including a quadcopter and a balloon.
- according to claim 1, part of the required thrust force of the aircraft is provided through its balloon mechanism, but the propellers of the aircraft play a key role in keeping the UAV floating and guiding it to be properly placed on the road.
- According to claim 2, this UAV uses two parts of hardware and software (artificial intelligence).
- according to claim 3, This drone has the following components: the UAV body, ballon, A gas relief valve, A polycarbonate fan cover, Flexible solar cells, Plate above the ballon, Sensor, Plate below the ballon, Joints, Folding landing gear mechanism, Fans, Handles, The actuator, Ball-screw mechanism, Batteries, A compressed helium tank, Rotors and Control system
- according to claim 4, the aircraft has a UFO structure with a balloon in the center. The diameter of this balloon can be changed through the mechanical mechanism built into it.
- according to claim 4, the volume of gas inside it can be changed depending on the required weight and altitude. The balloon mechanism carries between 60% and 100% of the drone's weight and the rotors provide up to 40% of the required thrust force.
- according to claim 4, The drone uses flexible solar panels that enable it to supply some of its energy needs from solar energy and increase its flight continuity. And in some cases, do not need to return to the base to recharge.
- according to claim 4, 4 rotors which are surrounded by a polycarbonate fan cover are installed in a rectangular shape around the balloon and inside the body of the drone, which provides the remaining required thrust force and determines its direction of movement by changing the speeds.
- according to claim 4, 8 sensors installed around the body of the drone helps to prevent collisions with obstacles.
- according to claim 4, A gas relief valve helps the balloon to empty the gas inside the balloon, if needed and thereby reducing the flying altitude of the drone. Also, a compressed helium tank is also considered to inject the required gas into the balloon in a controlled manner to increase the flying height of the drone.
- according to claim 4, UAV batteries are placed in four parts inside the drone body, and these batteries are charged during flight through flexible solar cells installed on the upper part of the balloon.
- The UAV uses a folding landing gear mechanism, these landing gear are connected to the body by joints and are folded into the UAV body during flight.
- according to claim 12, The diameter of the flying balloon can be reduced and increased through the actuator and the ball-screw mechanism built into the body of the balloon.
- according to claim 13, two plates, one above the balloon and the other below the balloon, changing the length of the vertical axis located in the center of the balloon will shrink or expand the balloon. This mechanism is connected to the drone body by three handles.
- according to claim 3, Main computers and flight control systems, imaging equipment, and sensors are located under the balloon and are attached to the body by the mechanism inside the balloon and connected to the body by handles.
- Our methods involve capturing images of the target component (roads) and analyzing them programmatically to find and classify cracks. We employ image processing and machine learning techniques.
- according to claim 16, Image processing involves the use of filters, morphological analysis, statistical methods, and percolation techniques for the detection of cracks
- according to claim 17, the machine learning process involves the collection of a dataset of images, which are supplied to the selected machine learning model for training.
- according to claim 18, It involves pre-processing the captured images to remove noise and improve data quality, clustering and detecting cracks using ROBs, and combining and growing the clusters using a network based on deep Q-learning that improves as we feed more input into it.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2022/059339 WO2024069215A1 (en) | 2022-09-30 | 2022-09-30 | The unmanned aerial vehicles with a hybrid structure for road crack detection by using image processing techniques |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2022/059339 WO2024069215A1 (en) | 2022-09-30 | 2022-09-30 | The unmanned aerial vehicles with a hybrid structure for road crack detection by using image processing techniques |
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| Publication Number | Publication Date |
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| WO2024069215A1 true WO2024069215A1 (en) | 2024-04-04 |
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| PCT/IB2022/059339 WO2024069215A1 (en) | 2022-09-30 | 2022-09-30 | The unmanned aerial vehicles with a hybrid structure for road crack detection by using image processing techniques |
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| Country | Link |
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| WO (1) | WO2024069215A1 (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160307448A1 (en) * | 2013-03-24 | 2016-10-20 | Bee Robotics Corporation | Hybrid airship-drone farm robot system for crop dusting, planting, fertilizing and other field jobs |
| CN109319082A (en) * | 2018-09-07 | 2019-02-12 | 江苏航空职业技术学院 | Quadrotor morphing aircraft |
| KR20220037027A (en) * | 2020-09-16 | 2022-03-24 | 이민형 | System and method for monitoring the ground using hybrid unmanned airship |
-
2022
- 2022-09-30 WO PCT/IB2022/059339 patent/WO2024069215A1/en unknown
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160307448A1 (en) * | 2013-03-24 | 2016-10-20 | Bee Robotics Corporation | Hybrid airship-drone farm robot system for crop dusting, planting, fertilizing and other field jobs |
| CN109319082A (en) * | 2018-09-07 | 2019-02-12 | 江苏航空职业技术学院 | Quadrotor morphing aircraft |
| KR20220037027A (en) * | 2020-09-16 | 2022-03-24 | 이민형 | System and method for monitoring the ground using hybrid unmanned airship |
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