CN204405065U - For the unmanned plane checkout equipment of urban road identification - Google Patents
For the unmanned plane checkout equipment of urban road identification Download PDFInfo
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- CN204405065U CN204405065U CN201520133593.9U CN201520133593U CN204405065U CN 204405065 U CN204405065 U CN 204405065U CN 201520133593 U CN201520133593 U CN 201520133593U CN 204405065 U CN204405065 U CN 204405065U
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Abstract
The utility model relates to a kind of unmanned plane checkout equipment for urban road identification, described checkout equipment comprises unmanned plane driving mechanism, CMOS ultra high-definition camera, fpga chip and flush bonding processor, flush bonding processor and unmanned plane driving mechanism, CMOS ultra high-definition camera is connected respectively with fpga chip, controlling unmanned plane driving mechanism flies to directly over urban road to be detected to drive unmanned plane, and after determining that unmanned plane is flown to directly over urban road to be detected, start CMOS ultra high-definition camera to urban road shooting to be detected to obtain urban road image, start fpga chip and image procossing is carried out to determine the road curve in urban road image to urban road image.By the utility model, can survey and draw the road curve information of urban road to be detected motor-driven, exactly, the mapping for Tactics of Urban Surveying department provides more valuable reference data.
Description
Technical field
The utility model relates to road survey field, particularly relates to a kind of unmanned plane checkout equipment for urban road identification.
Background technology
The relevant information of urban road comprises curvilinear path, the locator data and relative position etc. of urban road.The relevant information obtaining urban road is that one of necessary key element of city map is drawn by city management department.Correct urban road information can help city management department in a planned way to dispose city management work, also contribute to other functional departments of city and carry out relevant work, such as, city traffic control department is facilitated to carry out management area division, more successfully to realize the division of duty of control of traffic and road.
Two kinds are had to the detection scheme of urban road information in prior art: the first, use remote sensing satellite shooting urban remote sensing image, urban remote sensing image is analyzed, extracts the information of each bar traffic route in city; The second, is arranged staff to carry out field exploring on each bar urban road, is obtained the information such as the curvilinear path of each bar urban road, locator data and relative position by artificial mode.But above-mentioned two kinds of detection schemes all have self limitation: the former due to the urban remote sensing image resolution obtained inadequate, be difficult to obtain high-precision urban road information; The latter needs to arrange a large amount of man power and materials, and detection mode is too original simultaneously, and the time cost of at substantial, real-time is very poor.
Therefore, need a kind of new urban road identifying schemes, while the relevant information ensureing fast, obtain at low cost each road in city, not to reduce mapping precision for cost, good cost performance can be had.
Summary of the invention
In order to solve the problem, the utility model provides a kind of unmanned plane checkout equipment for urban road identification, employing unmanned plane is detection platform, utilize wireless control technology can arrive each bar at any time to need to carry out road information detection directly over the urban road of detection, thus reduction detection overhead, there is good maneuverability and real-time, simultaneously for the characteristic distributions of urban road, customize multiple image processing equipment with different images processing capacity to process in order the ultra high-definition image captured by the picture pick-up device on unmanned plane, and, based on the large data characteristics of ultra high-definition image, each image procossing all adopts parallel mode to carry out, while improving data precision, ensure that the high speed of data processing is carried out.
According to one side of the present utility model, provide a kind of unmanned plane checkout equipment for urban road identification, described checkout equipment comprises unmanned plane driving mechanism, CMOS ultra high-definition camera, fpga chip and flush bonding processor, described flush bonding processor and described unmanned plane driving mechanism, described CMOS ultra high-definition camera is connected respectively with described fpga chip, controlling described unmanned plane driving mechanism flies to directly over urban road to be detected to drive described unmanned plane, and after determining that described unmanned plane is flown to directly over urban road to be detected, start described CMOS ultra high-definition camera to urban road shooting to be detected to obtain urban road image, start described fpga chip and image procossing is carried out to determine the road curve in urban road image to urban road image.
More specifically, also comprising: Galileo navigation device in the unmanned plane checkout equipment of urban road identification described, connecting Galileo navigation satellite, for receiving the real time positioning data of unmanned plane position, ultrasonic height sensors, comprise ultrasound wave emitter, ultrasonic receiver and single-chip microcomputer, described single-chip microcomputer is connected respectively with described ultrasound wave emitter and described ultrasonic receiver, described ultrasound wave emitter launches ultrasound wave earthward, described ultrasonic receiver receives the ultrasound wave of ground return, and described single-chip microcomputer calculates the real-time ultrasound wave height of unmanned plane according to the launch time of described ultrasound wave emitter, the time of reception of described ultrasonic receiver and ultrasonic propagation velocity, portable hard drive, for prestoring the table of comparisons, the described table of comparisons with order of magnitude corresponding to image data amount for index, save the Iamge Segmentation quantity that each order of magnitude is corresponding respectively, described portable hard drive is also for prestoring urban road R channel range, urban road G channel range, urban road channel B scope, and described urban road R channel range, described urban road G channel range and described urban road channel B scope are used for the urban road in RGB image to be separated with RGB image background, transceiver, draw platform with the urban road of far-end and set up two-way wireless communication link, draw the flight steering order of platform transmission for receiving described urban road, described flight steering order comprises object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height, described CMOS ultra high-definition camera comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and cmos imaging electronic unit, and the resolution of the urban road image captured by described CMOS ultra high-definition camera is 3840 × 2160, described fpga chip is the EP1C6Q240C8 of the Cyclone series of altera corp, in described fpga chip, be integrated with image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit, n equals the largest amount grade in the described table of comparisons, described image data amount estimation unit is connected with described CMOS ultra high-definition camera, for assessment of the data volume of the compressed image obtained after compress described urban road image based on MPEG-4, described image division unit is estimated unit with described portable hard drive, described CMOS ultra high-definition camera and described image data amount and is connected respectively, based on the order of magnitude that the data volume of compressed image is corresponding, corresponding Iamge Segmentation quantity is inquired as target image dividing number m in the described table of comparisons, and described urban road image is longitudinally on average divided into m subimage, a described m subimage is distributed to respectively m graphics processing unit before in a described n graphics processing unit, each graphics processing unit in described front m graphics processing unit carries out image procossing to determine the road way curve in described corresponding subimage to the corresponding subimage be assigned to, described image uniting unit is connected respectively with a described n graphics processing unit, and m the road way curve exported by described front m graphics processing unit merges, to obtain the road curve in urban road image, described flush bonding processor and described unmanned plane driving mechanism, described CMOS ultra high-definition camera, described fpga chip, described transceiver, described Galileo navigation device is connected respectively with described ultrasonic height sensors, receive the flight steering order that described transceiver forwards, described flight steering order is resolved to obtain object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height, control described unmanned plane driving mechanism to fly to position directly over described urban road to be detected to drive described unmanned plane, when described real-time ultrasound wave height is mated with described object shooting height and described real time positioning data mates with described object Galileo locator data, enter urban road detecting pattern, when described real-time ultrasound wave height is not mated with described object shooting height or described real time positioning data does not mate with described object Galileo locator data, enter urban road searching modes, wherein, each graphics processing unit comprises color space conversion subelement, auto color enhanson, image division sub-unit, self-adaptation recursive filtering subelement and least square fitting subelement, described color space conversion subelement is connected with described image division unit, for performing rgb color space conversion to the corresponding subimage be assigned to, obtains the RGB image after conversion, described auto color enhanson and described color space are changed subelement and are connected, and the RGB image after described conversion are performed to the image enhancement processing strengthened based on auto color, to obtain the enhancing image that road and background contrasts strengthen, described separation of images subelement is connected respectively with described portable hard drive and described auto color enhanson, calculate the R channel value of each pixel in described enhancing image, G channel value and channel B value, when the R channel value of a certain pixel in described urban road R channel range, G channel value in described urban road G channel range and channel B value within the scope of described urban road channel B time, be defined as urban road pixel, by urban road combination of pixels all in described enhancing image to form road pattern, described self-adaptation recursive filtering subelement is connected with described separation of images subelement, performs the process of self-adaptation recursive filtering, to obtain the filtering road pattern of filtering noise pixel to described road pattern, described least square fitting subelement is connected with described self-adaptation recursive filtering subelement, perform process of fitting treatment to determine way curve based on least square fitting algorithm to described filtering road pattern, described road way curve is and the square distance of all pixels in described filtering road pattern and minimum curve, described flush bonding processor is in described urban road detecting pattern, start described super CMOS ultra high-definition camera and described fpga chip, receive described urban road image and described road curve, determine the relative position of described road curve in described urban road image, determine the locating information of described road curve based on described relative position, described real time positioning data and described real-time ultrasound wave height, the locating information of described road curve comprises the locating information of the locating information of the starting point of described road curve and the terminal of described road curve, described flush bonding processor, in described urban road searching modes, closes described CMOS ultra high-definition camera and described fpga chip.
More specifically, described in the unmanned plane checkout equipment of urban road identification: described transceiver receives the locating information of described road curve and described road curve, and the locating information of described road curve and described road curve is wirelessly sent to described urban road drafting platform.
More specifically, described in the unmanned plane checkout equipment of urban road identification: the integration realization mode substituting an EP1C6Q240C8FPGA chip, image data amount estimated unit, image division unit, an image uniting unit and n graphics processing unit and adopt fpga chip respectively to realize.
More specifically, described in the unmanned plane checkout equipment of urban road identification: the XC3S1000FT256 of the type selecting Dou Shi Xilinx company of the fpga chip that image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit adopts respectively.
More specifically, described in the unmanned plane checkout equipment of urban road identification: image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit adopts the fpga chip of different model to realize respectively.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present utility model is described, wherein:
Fig. 1 is the block diagram of the unmanned plane checkout equipment for urban road identification according to the utility model embodiment.
Embodiment
Below with reference to accompanying drawings the embodiment of the unmanned plane checkout equipment for urban road identification of the present utility model is described in detail.
Urban road, be a kind of can each department in sensible city, for communications and transportation in city and pedestrian, be convenient to resident living, work and cultural and recreational activity, and be connected with road outside city and bear the externally road of traffic.Urban road is generally broad compared with highway, for adapting to the complicated vehicles, divide car lane, bus priority lane, bicycle lane etc. more, the walkway and building construction that exceed road surface is had in urban road both sides, bury common line underground under walkway more, promising beautifying city, urban road both sides and arrange greenbelt, sculptures art work, the urban road planted on two sides woods, and be mounted with gutter so that municipal drainage.
Urban road is the motifs of city management.He is related to organic activity in whole city.Thus, the acquisition of the relevant information of incity, city each bar road is extremely important, the correctness of these relevant informations, directly affects planning efficiency and the planning effect of urban function department.
But, urban road information extraction technology mainly satellite remote sensing mode of the prior art and on-site land survey mode, although the former reduces testing cost, but under the pressure of the limitation of satellite remote sensing mechanism, the remotely-sensed data precision obtained is not high, cause the urban road information extracted very accurate, although the latter can obtain the urban road information of certain precision, cost of labor and time cost are too high.
The utility model has built a kind of unmanned plane checkout equipment for urban road identification, with the motor-driven and unmanned plane of sensing range broadness for detection platform, based on a set of urban road testing mechanism be made up of multiple image procossing subelement of urban road characteristic customization, to in the information extraction of urban road, take into account the high requirement to precision, efficiency and cost performance.
Fig. 1 is the block diagram of the unmanned plane checkout equipment for urban road identification according to the utility model embodiment, described checkout equipment comprises CMOS ultra high-definition camera 1, fpga chip 2, unmanned plane driving mechanism 3, portable hard drive 4, Galileo navigation device 5, ultrasonic height sensors 6, transceiver 7 and flush bonding processor 8, and flush bonding processor 8 is connected respectively with CMOS ultra high-definition camera 1, fpga chip 2, unmanned plane driving mechanism 3, portable hard drive 4, Galileo navigation device 5, ultrasonic height sensors 6, transceiver 7.
Wherein, described flush bonding processor 8 is flown to directly over urban road to be detected for controlling described unmanned plane driving mechanism 3 to drive described unmanned plane, and after determining that described unmanned plane is flown to directly over urban road to be detected, start described CMOS ultra high-definition camera 1 to take to obtain urban road image urban road to be detected, start described fpga chip 2 pairs of urban road images and carry out image procossing to determine the road curve in urban road image.
Then, continue to be further detailed the concrete structure of the unmanned plane checkout equipment for urban road identification of the present utility model.
In described unmanned plane checkout equipment, described Galileo navigation device 5 connects Galileo navigation satellite, for receiving the real time positioning data of unmanned plane position.
In described unmanned plane checkout equipment, described ultrasonic height sensors 6 comprises ultrasound wave emitter, ultrasonic receiver and single-chip microcomputer, described single-chip microcomputer is connected respectively with described ultrasound wave emitter and described ultrasonic receiver, described ultrasound wave emitter launches ultrasound wave earthward, described ultrasonic receiver receives the ultrasound wave of ground return, and described single-chip microcomputer calculates the real-time ultrasound wave height of unmanned plane according to the launch time of described ultrasound wave emitter, the time of reception of described ultrasonic receiver and ultrasonic propagation velocity.
In described unmanned plane checkout equipment, described portable hard drive 4 is for prestoring the table of comparisons, the described table of comparisons with order of magnitude corresponding to image data amount for index, save the Iamge Segmentation quantity that each order of magnitude is corresponding respectively, described portable hard drive 4 is also for prestoring urban road R channel range, urban road G channel range, urban road channel B scope, and described urban road R channel range, described urban road G channel range and described urban road channel B scope are used for the urban road in RGB image to be separated with RGB image background.
In described unmanned plane checkout equipment, described transceiver 7 is drawn platform with the urban road of far-end and is set up two-way wireless communication link, draw the flight steering order of platform transmission for receiving described urban road, described flight steering order comprises object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height.
In described unmanned plane checkout equipment, described CMOS ultra high-definition camera 1 comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and cmos imaging electronic unit, and the resolution of the urban road image captured by described CMOS ultra high-definition camera 1 is 3840 × 2160.
In described unmanned plane checkout equipment, described fpga chip 2 is the EP1C6Q240C8 of the Cyclone series of altera corp, in described fpga chip 2, be integrated with image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit, n equals the largest amount grade in the described table of comparisons.
In described fpga chip 2, described image data amount estimation unit is connected with described CMOS ultra high-definition camera 1, for assessment of the data volume of the compressed image obtained after compress described urban road image based on MPEG-4, described image division unit and described portable hard drive 4, described CMOS ultra high-definition camera 1 is connected respectively with described image data amount estimation unit, based on the order of magnitude that the data volume of compressed image is corresponding, corresponding Iamge Segmentation quantity is inquired as target image dividing number m in the described table of comparisons, and described urban road image is longitudinally on average divided into m subimage, a described m subimage is distributed to respectively m graphics processing unit before in a described n graphics processing unit,
In described fpga chip 2, each graphics processing unit in a described front m graphics processing unit carries out image procossing to determine the road way curve in described corresponding subimage to the corresponding subimage be assigned to; Described image uniting unit is connected respectively with a described n graphics processing unit, and m the road way curve exported by described front m graphics processing unit merges, to obtain the road curve in urban road image;
In described unmanned plane checkout equipment, described flush bonding processor 8 and described unmanned plane driving mechanism 3, described CMOS ultra high-definition camera 1, described fpga chip 2, described transceiver 7, described Galileo navigation device 5 is connected respectively with described ultrasonic height sensors 6, receive the flight steering order that described transceiver 7 forwards, described flight steering order is resolved to obtain object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height, control described unmanned plane driving mechanism 3 to fly to position directly over described urban road to be detected to drive described unmanned plane, when described real-time ultrasound wave height is mated with described object shooting height and described real time positioning data mates with described object Galileo locator data, enter urban road detecting pattern, when described real-time ultrasound wave height is not mated with described object shooting height or described real time positioning data does not mate with described object Galileo locator data, enter urban road searching modes.
Wherein, each graphics processing unit comprises color space conversion subelement, auto color enhanson, image division sub-unit, self-adaptation recursive filtering subelement and least square fitting subelement.
In each graphics processing unit, described color space conversion subelement is connected with described image division unit, for performing rgb color space conversion to the corresponding subimage be assigned to, obtains the RGB image after conversion.
In each graphics processing unit, described auto color enhanson and described color space are changed subelement and are connected, RGB image after described conversion is performed to the image enhancement processing strengthened based on auto color, to obtain the enhancing image that road and background contrasts strengthen.
In each graphics processing unit, described separation of images subelement is connected respectively with described portable hard drive 4 and described auto color enhanson, calculate the R channel value of each pixel in described enhancing image, G channel value and channel B value, when the R channel value of a certain pixel in described urban road R channel range, G channel value in described urban road G channel range and channel B value within the scope of described urban road channel B time, be defined as urban road pixel, by urban road combination of pixels all in described enhancing image to form road pattern.
In each graphics processing unit, described self-adaptation recursive filtering subelement is connected with described separation of images subelement, performs the process of self-adaptation recursive filtering, to obtain the filtering road pattern of filtering noise pixel to described road pattern.
In each graphics processing unit, described least square fitting subelement is connected with described self-adaptation recursive filtering subelement, perform process of fitting treatment to determine way curve based on least square fitting algorithm to described filtering road pattern, described road way curve is and the square distance of all pixels in described filtering road pattern and minimum curve.
Wherein, described flush bonding processor 8 is in described urban road detecting pattern, start described super CMOS ultra high-definition camera 1 and described fpga chip 2, receive described urban road image and described road curve, determine the relative position of described road curve in described urban road image, determine the locating information of described road curve based on described relative position, described real time positioning data and described real-time ultrasound wave height, the locating information of described road curve comprises the locating information of the locating information of the starting point of described road curve and the terminal of described road curve; And described flush bonding processor 8 is in described urban road searching modes, close described CMOS ultra high-definition camera 1 and described fpga chip 2.
Wherein, in described unmanned plane checkout equipment: described transceiver 7 can receive the locating information of described road curve and described road curve, and the locating information of described road curve and described road curve is wirelessly sent to described urban road drafting platform, alternatively, substitute the integration realization mode of an EP1C6Q240C8FPGA chip 2, image data amount is estimated unit, image division unit, image uniting unit and n graphics processing unit adopt fpga chip to realize respectively, and image data amount estimation unit, image division unit, the XC3S1000FT256 of the type selecting Dou Shi Xilinx company of the fpga chip that image uniting unit and n graphics processing unit adopt respectively, or, image data amount estimation unit, image division unit, image uniting unit and n graphics processing unit adopt the fpga chip of different model to realize respectively.
In addition, GPS navigation system, GLONASS navigational system, Galileo navigation system or Beidou satellite navigation system are four kinds of navigational system that the world today the most generally uses.
Beidou satellite navigation system be China implementing independent development capability, independent operating GPS (Global Position System).System Construction target is: build up independent, open and compatible, advanced technology, reliable and stable Beidou satellite navigation system covering the whole world, promote that satellite navigation industrial chain is formed, form perfect national satellite navigation application industry to support, promote and security system, promote the widespread use of satellite navigation in national economy society every profession and trade, Beidou satellite navigation system is by space segment, ground segment and user segment three part composition, space segment comprises 5 satellites and 30 non-geo satellites, ground segment comprises master station, several land stations such as injection plant and monitoring station, user segment comprises Big Dipper user terminal and the terminal with other satellite navigation system compatibilities.
GPS is the of new generation Aerospace Satellite navigation positioning systems of 20 century 70s by land, sea, and air of U.S. joint research and development, its fundamental purpose is for large field, land, sea, air three provides real-time, round-the-clock and global navigation Service, and for some military purposes such as information acquisition, Nuclear detonation monitoring and emergency communications, be the important composition that the U.S. dominates global strategy exclusively.Through the research experiment in more than 20 years, cost 30,000,000,000 dollars, in March, 1994, Global coverage rate up to 98% 24 gps satellite constellations oneself laid.GALILEO positioning system is one, European Union global position system just under construction, outside the existing gps system of the Ye Shiji U.S. and Muscovite GLONASS navigational system, 3rd can be for civilian use positioning system, the basic service of Galileo system has navigation, location, time service; Special service has search and rescue aid; Expanded application service system has application in aircraft navigation and landing system, safe railway operation scheduling, marine transportation system, land fleet transportation dispatching, precision agriculture, on January 7th, 2010, EU Committee claims, and the GALILEO positioning system of European Union will put into effect from 2014.Russian Glonass navigational system is made up of 24 satellites, and precision is at about 10 meters, dual-use, design 2009 the end of the year service range be extended to the whole world.
Adopt the unmanned plane checkout equipment for urban road identification of the present utility model, measuring accuracy cannot be met for existing urban road recognition method simultaneously, measure the technical matters of cost and measurement requirement of real-time, introduce unmanned aerial vehicle platform and wireless communications mode effectively to control and measure cost and meet the requirement of measuring real-time, and the image processing section coordinated manipulation having customized multiple difference in functionality according to the feature of urban road is to meet the requirement of measuring accuracy.
Be understandable that, although the utility model with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the utility model.For any those of ordinary skill in the art, do not departing under technical solutions of the utility model ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solutions of the utility model, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solutions of the utility model, according to technical spirit of the present utility model to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solutions of the utility model protection.
Claims (6)
1. the unmanned plane checkout equipment for urban road identification, it is characterized in that, described checkout equipment comprises unmanned plane driving mechanism, CMOS ultra high-definition camera, fpga chip and flush bonding processor, described flush bonding processor and described unmanned plane driving mechanism, described CMOS ultra high-definition camera is connected respectively with described fpga chip, controlling described unmanned plane driving mechanism flies to directly over urban road to be detected to drive described unmanned plane, and after determining that described unmanned plane is flown to directly over urban road to be detected, start described CMOS ultra high-definition camera to urban road shooting to be detected to obtain urban road image, start described fpga chip and image procossing is carried out to determine the road curve in urban road image to urban road image.
2., as claimed in claim 1 for the unmanned plane checkout equipment of urban road identification, it is characterized in that, described checkout equipment also comprises:
Galileo navigation device, connects Galileo navigation satellite, for receiving the real time positioning data of unmanned plane position;
Ultrasonic height sensors, comprise ultrasound wave emitter, ultrasonic receiver and single-chip microcomputer, described single-chip microcomputer is connected respectively with described ultrasound wave emitter and described ultrasonic receiver, described ultrasound wave emitter launches ultrasound wave earthward, described ultrasonic receiver receives the ultrasound wave of ground return, and described single-chip microcomputer calculates the real-time ultrasound wave height of unmanned plane according to the launch time of described ultrasound wave emitter, the time of reception of described ultrasonic receiver and ultrasonic propagation velocity;
Portable hard drive, for prestoring the table of comparisons, the described table of comparisons with order of magnitude corresponding to image data amount for index, save the Iamge Segmentation quantity that each order of magnitude is corresponding respectively, described portable hard drive is also for prestoring urban road R channel range, urban road G channel range, urban road channel B scope, and described urban road R channel range, described urban road G channel range and described urban road channel B scope are used for the urban road in RGB image to be separated with RGB image background;
Transceiver, draw platform with the urban road of far-end and set up two-way wireless communication link, draw the flight steering order of platform transmission for receiving described urban road, described flight steering order comprises object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height;
Described CMOS ultra high-definition camera comprises undercarriage having shock absorption function, front cover glass, camera lens, filter and cmos imaging electronic unit, and the resolution of the urban road image captured by described CMOS ultra high-definition camera is 3840 × 2160;
Described fpga chip is the EP1C6Q240C8 of the Cyclone series of altera corp, in described fpga chip, be integrated with image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit, n equals the largest amount grade in the described table of comparisons; Described image data amount estimation unit is connected with described CMOS ultra high-definition camera, for assessment of the data volume of the compressed image obtained after compress described urban road image based on MPEG-4; Described image division unit is estimated unit with described portable hard drive, described CMOS ultra high-definition camera and described image data amount and is connected respectively, based on the order of magnitude that the data volume of compressed image is corresponding, corresponding Iamge Segmentation quantity is inquired as target image dividing number m in the described table of comparisons, and described urban road image is longitudinally on average divided into m subimage, a described m subimage is distributed to respectively m graphics processing unit before in a described n graphics processing unit; Each graphics processing unit in described front m graphics processing unit carries out image procossing to determine the road way curve in described corresponding subimage to the corresponding subimage be assigned to; Described image uniting unit is connected respectively with a described n graphics processing unit, and m the road way curve exported by described front m graphics processing unit merges, to obtain the road curve in urban road image;
Described flush bonding processor and described unmanned plane driving mechanism, described CMOS ultra high-definition camera, described fpga chip, described transceiver, described Galileo navigation device is connected respectively with described ultrasonic height sensors, receive the flight steering order that described transceiver forwards, described flight steering order is resolved to obtain object Galileo locator data corresponding to position directly over urban road to be detected and object shooting height, control described unmanned plane driving mechanism to fly to position directly over described urban road to be detected to drive described unmanned plane, when described real-time ultrasound wave height is mated with described object shooting height and described real time positioning data mates with described object Galileo locator data, enter urban road detecting pattern, when described real-time ultrasound wave height is not mated with described object shooting height or described real time positioning data does not mate with described object Galileo locator data, enter urban road searching modes,
Wherein, each graphics processing unit comprises color space conversion subelement, auto color enhanson, image division sub-unit, self-adaptation recursive filtering subelement and least square fitting subelement; Described color space conversion subelement is connected with described image division unit, for performing rgb color space conversion to the corresponding subimage be assigned to, obtains the RGB image after conversion; Described auto color enhanson and described color space are changed subelement and are connected, and the RGB image after described conversion are performed to the image enhancement processing strengthened based on auto color, to obtain the enhancing image that road and background contrasts strengthen; Described separation of images subelement is connected respectively with described portable hard drive and described auto color enhanson, calculate the R channel value of each pixel in described enhancing image, G channel value and channel B value, when the R channel value of a certain pixel in described urban road R channel range, G channel value in described urban road G channel range and channel B value within the scope of described urban road channel B time, be defined as urban road pixel, by urban road combination of pixels all in described enhancing image to form road pattern; Described self-adaptation recursive filtering subelement is connected with described separation of images subelement, performs the process of self-adaptation recursive filtering, to obtain the filtering road pattern of filtering noise pixel to described road pattern; Described least square fitting subelement is connected with described self-adaptation recursive filtering subelement, perform process of fitting treatment to determine way curve based on least square fitting algorithm to described filtering road pattern, described road way curve is and the square distance of all pixels in described filtering road pattern and minimum curve;
Wherein, described flush bonding processor is in described urban road detecting pattern, start described super CMOS ultra high-definition camera and described fpga chip, receive described urban road image and described road curve, determine the relative position of described road curve in described urban road image, determine the locating information of described road curve based on described relative position, described real time positioning data and described real-time ultrasound wave height, the locating information of described road curve comprises the locating information of the locating information of the starting point of described road curve and the terminal of described road curve; Described flush bonding processor, in described urban road searching modes, closes described CMOS ultra high-definition camera and described fpga chip.
3., as claimed in claim 2 for the unmanned plane checkout equipment of urban road identification, it is characterized in that:
Described transceiver receives the locating information of described road curve and described road curve, and the locating information of described road curve and described road curve is wirelessly sent to described urban road drafting platform.
4., as claimed in claim 2 for the unmanned plane checkout equipment of urban road identification, it is characterized in that:
Substitute the integration realization mode of an EP1C6Q240C8FPGA chip, image data amount is estimated unit, image division unit, an image uniting unit and n graphics processing unit and adopt fpga chip respectively to realize.
5., as claimed in claim 4 for the unmanned plane checkout equipment of urban road identification, it is characterized in that:
The XC3S1000FT256 of the type selecting Dou Shi Xilinx company of the fpga chip that image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit adopts respectively.
6., as claimed in claim 4 for the unmanned plane checkout equipment of urban road identification, it is characterized in that:
Image data amount estimation unit, image division unit, an image uniting unit and n graphics processing unit adopts the fpga chip of different model to realize respectively.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104655107A (en) * | 2015-03-10 | 2015-05-27 | 无锡桑尼安科技有限公司 | Unmanned aerial vehicle detecting equipment for urban road identification |
| CN105389988A (en) * | 2015-12-07 | 2016-03-09 | 北京航空航天大学 | Multi-unmanned aerial vehicle cooperation highway intelligent inspection system |
| CN106951567A (en) * | 2017-04-07 | 2017-07-14 | 安徽建筑大学 | Water system space analysis method based on remote sensing technology for Huizhou traditional settlement |
| CN107065894A (en) * | 2016-01-28 | 2017-08-18 | 松下电器（美国）知识产权公司 | Unmanned vehicle, flight altitude control device, method and program |
| CN109282797A (en) * | 2018-03-16 | 2019-01-29 | 高艳云 | Unmanned plane target identification localization method |
| CN110969875A (en) * | 2019-12-19 | 2020-04-07 | 深圳市哈工大交通电子技术有限公司 | Method and system for road intersection traffic management |
| US11322033B2 (en) | 2019-08-27 | 2022-05-03 | International Business Machines Corporation | Remote surface condition assessment |
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2015
- 2015-03-10 CN CN201520133593.9U patent/CN204405065U/en not_active Withdrawn - After Issue
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104655107A (en) * | 2015-03-10 | 2015-05-27 | 无锡桑尼安科技有限公司 | Unmanned aerial vehicle detecting equipment for urban road identification |
| CN105157679A (en) * | 2015-03-10 | 2015-12-16 | 无锡桑尼安科技有限公司 | Unmanned aerial vehicle detection equipment for urban road identification |
| CN105180902A (en) * | 2015-03-10 | 2015-12-23 | 无锡桑尼安科技有限公司 | Unmanned aerial vehicle (UAV) detection equipment for identifying urban roads |
| CN105389988B (en) * | 2015-12-07 | 2018-03-06 | 北京航空航天大学 | A kind of express highway intelligent cruising inspection system of multiple no-manned plane collaboration |
| CN105389988A (en) * | 2015-12-07 | 2016-03-09 | 北京航空航天大学 | Multi-unmanned aerial vehicle cooperation highway intelligent inspection system |
| CN107065894A (en) * | 2016-01-28 | 2017-08-18 | 松下电器（美国）知识产权公司 | Unmanned vehicle, flight altitude control device, method and program |
| CN107065894B (en) * | 2016-01-28 | 2021-11-26 | 松下电器（美国）知识产权公司 | Unmanned aerial vehicle, flying height control device, method, and computer-readable recording medium |
| CN106951567A (en) * | 2017-04-07 | 2017-07-14 | 安徽建筑大学 | Water system space analysis method based on remote sensing technology for Huizhou traditional settlement |
| CN106951567B (en) * | 2017-04-07 | 2020-07-17 | 安徽建筑大学 | Water system space analysis method based on remote sensing technology for Huizhou traditional settlement |
| CN109282797A (en) * | 2018-03-16 | 2019-01-29 | 高艳云 | Unmanned plane target identification localization method |
| CN109282797B (en) * | 2018-03-16 | 2019-06-04 | 西安亿航白鹭传媒科技有限公司 | Unmanned plane target identification localization method |
| US11322033B2 (en) | 2019-08-27 | 2022-05-03 | International Business Machines Corporation | Remote surface condition assessment |
| CN110969875A (en) * | 2019-12-19 | 2020-04-07 | 深圳市哈工大交通电子技术有限公司 | Method and system for road intersection traffic management |
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