CN109613559A - The flood boundaries floating material discriminating gear and method of view-based access control model and laser radar - Google Patents
The flood boundaries floating material discriminating gear and method of view-based access control model and laser radar Download PDFInfo
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Abstract
The invention discloses the detection device of the floater and land boundary of a kind of view-based access control model and laser radar, described device includes data collection layer, processing diagnostic horizon and communication interface layer；The data collection layer: including a laser radar, a vision system and a visual processes SOC；The processing diagnostic horizon includes: MCU, human-computer interaction module, pose measurement module, image analysis processing module and deep neural network training module；Above-mentioned communication interface layer includes Ethernet SOC, Powerlink module and CAN module.The present invention provides the floater of a kind of view-based access control model and laser radar and the detection device and method of land boundary, the boundary on the water surface for having floating material and land or riverbank can accurately be differentiated by making unmanned boat both, the moment hull bottom and riverbed distance can be detected again, avoid stranded equal dangerous generation in time.
Description
Technical field
The present invention relates to the discriminating gear of the floater and land boundary of a kind of view-based access control model and laser radar and sides
Method belongs to the Condition Monitoring Technology field of industrial intelligent equipment.
Background technique
Refuse on water surface unmanned boat is that one kind does not need Manpower operating, by its independent navigation, detection, positioning, monitoring in
One, the special equipment of collecting refuse from open water prune job is carried out, due to Chinese Urbanization and industrialized development, to river
Pollution is produced, the floaters large area such as rubbish and floating algae covers the water surface, causes aquatic environment complicated, unmanned boat is in the water surface
On can not accurately differentiate flood boundaries, be possible to cause in this way unmanned boat in water operation when too close to riverbank, make nobody
Ship is stranded, or even causes unmanned boat hull damage.So this detection differentiation is to be badly in need of and necessary, in order to improve unmanned boat
Fluency and safety when operation.
Summary of the invention
To solve the above-mentioned problems, the present invention provides floater and the land side of a kind of view-based access control model and laser radar
The detection device and method on boundary, the boundary on the water surface for having floating material and land or riverbank can accurately be differentiated by making unmanned boat both,
The moment hull bottom and riverbed distance can be detected again, avoid stranded equal dangerous generation in time.Detection device of the present invention and nobody
Ship, come control flaps slurry, maintains easily maintenance and programming of the personnel to unmanned boat ontology and detection device by interface connection, and
Fault detection, analysis and replacement to laser radar and vision system and detection device.
Technical scheme is as follows:
The detection device of the floater and land boundary of a kind of view-based access control model and laser radar, described device includes that data are adopted
Collect layer, processing diagnostic horizon and communication interface layer；
The data collection layer: including a laser radar, a vision system and a visual processes SOC；The vision SOC
By broadcasting network absolute time and relative time, the image data of vision system and the point cloud data of laser radar scanning are realized
At the time of it is synchronous；The vision system and the laser radar simultaneously to same riverbank and water surface juncture area row scanning shoot,
Acquire riverbank, algae and rubbish point cloud data and visual pattern；The vision SOC Built-in Image preprocessor, passes through addition
Anisotropic diffusion filtering carries out smooth and image border to image and retains, this is existing ripe algorithm；
The processing diagnostic horizon includes: MCU, human-computer interaction module, pose measurement module, image analysis processing module and depth mind
Through network training module；By SPI communication bus communication, MCU passes through with human-computer interaction module for MCU and pose measurement module
RS485 is communicated, and the deep neural network training module and image analysis processing module directly pass through DMA in MCU memory
Carry out data exchange；
The human-computer interaction module is for providing manual remote control, display and audio output function, part when for initially installing
It is arranged, shows the harbor work's work that pulls in shore back of unmanned boat when work and work are completed；
The pose measurement module establishes station heart rectangular coordinate system by origin of laser radar, by the output for recording navigation software
Data record unmanned ship's head deflection information, the riverbank point cloud coordinate acquired by the space coordinates and laser radar,
The approximation space plane equation on the moment riverbank is fitted by principle of least square method, then range formula can obtain ship and arrive from point to surface
The vertical range on riverbank, it is stranded because of too close riverbank to avoid unmanned boat, and the data of the pose measurement module are also used for depth
Spend neural metwork training module training pattern；
Described image analysis and processing module can carry out characteristic point acquisition by image of the existing SIFT algorithm to data collection layer,
And feature point description vector is generated to all characteristic points, the above visual pattern, visual pattern characteristic point are transmitted to depth nerve
Network training model；
The deep neural network training module is by existing genetic algorithm, with visual pattern, visual pattern characteristic parameter
Input carries out evolutionary training to it to be output with the course deflection information of input data mutually in the same time；
The communication interface layer provides the communication interface of data transmission for device；Communication interface layer include Ethernet SOC,
Powerlink module and CAN module.
The detection method of the floater and land boundary of a kind of view-based access control model and laser radar, utilizes above-mentioned dress
It sets, it is characterised in that include the following steps:
(1) according to unmanned boat structure and draft, determine that laser radar irradiation area should be at a distance from fore holding, according to nothing
People's ship draft and river structure determine the early warning distance at ship and land and water boundary；
(2) by human-computer interaction module, Current vision system and laser radar direction of illumination and irradiating angle, radar illumination are set
Direction, vision system shooting direction, the direction of advance of fore direction and unmanned boat are answered coplanar；
(3) unmanned boat after the installation is completed, is placed in water by device, into device calibration phase；
(4) following special water surface visual signature image and radar scanning point cloud data: city are first acquired by manual operation unmanned boat
River riverbank texture, algae and rubbish waterborne and other floating materials；
(5) device obtains the data of vision system and laser radar by data collection layer, adds anisotropy to expand by vision SOC
The image that filtering shoots video camera is dissipated to pre-process, to image noise reduction and enhancing, meanwhile, pass through the communication of communication interface layer
Module is communicated with unmanned boat propeller control, and device receives the signal from propeller control, provides current spiral
Paddle direction of propulsion information；
(6) image analysis processing module utilizes the pretreatment image of data collection layer, establishes the time slip-window of image sequence simultaneously
Comparison front and back image changes, the characteristic parameter and invariant features parameter changed in real-time detection image, finally according to characteristic parameter
Establish floating material and riverbank characteristic model；
(7) pose measurement module real-time perfoming unmanned boat course deflection information acquires, and course deflection information is transmitted to depth
Neural training module；According to known river width and using radar as 3, any riverbank coordinate of space coordinates origin, nothing
People's ship calculates the depth of water and offshore distance of unmanned boat real time position, according to the early warning distance set, judges whether to occur
It is stranded；
(8) genetic algorithm is utilized, depth nerve training module is using image analysis processing modular character result as neural network mould
Type input, the course deflection information of pose measurement module is that output is trained, and stores network model, when the school of finishing device
Quasi- work；
(9) it if device does not complete calibration, repeats step (4) to step (8) and is calibrated；If device has been completed to calibrate, weight
Multiple step (4) and step (7), and enter step (10)；
(10) stored neural network model is utilized, image analysis processing module is recorded a demerit defeated as neural network model
Enter, the information of pose measurement module is the operation at sea that output instruct unmanned boat；
(11) step (9) are repeated and step (10) starts to carry out the online real-time working process of unmanned boat.
Advantageous effects of the invention:
The floater and land boundary method of discrimination of view-based access control model and laser radar provided by the invention, can judge land and water side
Boundary and floating material, the generation for the safety accidents such as avoid unmanned boat stranded, can also measure the real-time unmanned ship position depth of water and offshore
Distance.In addition, discriminating gear of the present invention can also be used directly as third party's discriminating gear, facilitate unmanned boat or unmanned boat
Other work.
Detailed description of the invention
Fig. 1 is the hardware architecture diagram of apparatus of the present invention；
Fig. 2 is the installation site schematic diagram of apparatus of the present invention；
Fig. 3 is the detection principle diagram of apparatus of the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, the detection device of the floater and land boundary of a kind of view-based access control model and laser radar, described
Device includes data collection layer, processing diagnostic horizon and communication interface layer；
The data collection layer: including a laser radar, a vision system and a visual processes SOC；The vision SOC
By broadcasting network absolute time and relative time, the image data of vision system and the point cloud data of laser radar scanning are realized
At the time of it is synchronous；The vision system and the laser radar simultaneously to same riverbank and water surface juncture area row scanning shoot,
Acquire riverbank, algae and rubbish point cloud data and visual pattern；The vision SOC Built-in Image preprocessor, i.e., by adding
Entering anisotropic filtering may be implemented vision system image real-time de-noising and enhancing；
The processing diagnostic horizon includes: MCU, human-computer interaction module, pose measurement module, image analysis processing module and depth mind
Through network training module；By SPI communication bus communication, MCU passes through with human-computer interaction module for MCU and pose measurement module
RS485 is communicated, and the deep neural network training module and image analysis processing module directly pass through DMA in MCU memory
Carry out data exchange；
The human-computer interaction module is for providing manual remote control, display and audio output function, part when for initially installing
It is arranged, shows the harbor work's work that pulls in shore back of unmanned boat when work and work are completed；
The pose measurement module is by establishing the dynamic space coordinate fixed as origin, three axes direction using laser radar
System, determines the real-time course deflection of unmanned boat, and acquires the information, passes through the space coordinates, it may be determined that laser radar acquisition
Riverbank point cloud coordinate can calculate the approximation space plane equation and Vertical Square on the moment riverbank using space geometry vector
To the unmanned boat real time position depth of water, unmanned boat is avoided because the depth of water is too shallow and stranded, the data of the pose measurement module are also used
In deep neural network training module training pattern；
Described image analysis and processing module carries out characteristic point acquisition by image of the SIFT algorithm to data collection layer, acquires city
River riverbank texture image, algae and rubbish floater waterborne as visual pattern characteristic parameter, and match synchronization,
The radar scanning point cloud chart of the same area, and the above visual pattern, visual pattern characteristic parameter and radar scanning point cloud chart are passed
Transport to deep neural network training pattern；
The deep neural network training module, with visual pattern, visual pattern characteristic parameter and matched radar points in the same time
Cloud atlas picture is input, to be output with the course deflection information of input data mutually in the same time, carries out evolutionary training；
The communication interface layer provides the communication interface of data transmission for device；Communication interface layer include Ethernet SOC,
Powerlink module and CAN module.
It is illustrated in figure 2 the installation site schematic diagram of apparatus of the present invention, laser radar and the vision system peace of apparatus of the present invention
Vision system and radar are mounted on the close ship in hull line of symmetry at unmanned boat ceiling, and it can be seen from top view
Head position guarantees unmanned boat balance when installation as far as possible.
It is illustrated in figure 3 the detection principle diagram of apparatus of the present invention, water surface information is acquired by vision system and radar, depending on
Feel SOC and handle the data processing of diagnostic horizon, vision system can be detected floater and riverbank, pass through deep neural network
The training result model of training module, can differentiate floating material and land boundary, in addition the early warning distance concurrently set, that is, can refer to
Lead work of the unmanned boat on the water surface.
The detection method of the floater and land boundary of a kind of view-based access control model and laser radar, utilizes above-mentioned dress
It sets, it is characterised in that include the following steps:
(1) according to unmanned boat structure and draft, determine that laser radar irradiation area should be at a distance from fore holding, according to nothing
People's ship draft and river structure determine the early warning distance at ship and land and water boundary；
(2) by human-computer interaction module, Current vision system and laser radar direction of illumination and irradiating angle, radar illumination are set
Direction, vision system shooting direction, the direction of advance of fore direction and unmanned boat are answered coplanar；
(3) unmanned boat after the installation is completed, is placed in water by device, into device calibration phase；
(4) following special water surface visual signature image and radar scanning point cloud data: city are first acquired by manual operation unmanned boat
River riverbank texture, algae and rubbish waterborne and other floating materials；
(5) device obtains the data of vision system and laser radar by data collection layer, adds anisotropy to filter by vision SOC
The image that wave shoots video camera pre-processes, to image noise reduction and enhancing, meanwhile, pass through the communication module of communication interface layer
It is communicated with unmanned boat propeller control, device receives the signal from propeller control, provides when front propeller pushes away
Into directional information；
(6) image analysis processing module utilizes the pretreatment image of data collection layer, establishes the time slip-window of image sequence simultaneously
Comparison front and back image changes, the characteristic parameter and invariant features parameter changed in real-time detection image, finally according to characteristic parameter
Establish floating material and riverbank characteristic model；
(7) pose measurement module real-time perfoming unmanned boat course deflection information acquires, and course deflection information is transmitted to depth
Neural training module；According to known river width and using radar as 3, any riverbank coordinate of space coordinates origin, nothing
People's ship calculates the depth of water and offshore distance of unmanned boat real time position, according to the early warning distance set, judges whether to occur
It is stranded；
(8) genetic algorithm is utilized, depth nerve training module is using image analysis processing modular character result as neural network mould
Type input, the course deflection information of pose measurement module is that output is trained, and stores network model, when the school of finishing device
Quasi- work；
(9) it if device does not complete calibration, repeats step (4) to step (8) and is calibrated；If device has been completed to calibrate, weight
Multiple step (4) and step (7), and enter step (10)；
(10) stored neural network model is utilized, image analysis processing module is recorded a demerit defeated as neural network model
Enter, the information of pose measurement module is the operation at sea that output instruct unmanned boat；
(11) step (9) are repeated and step (10) starts to carry out the online real-time working process of unmanned boat.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (2)
1. the discriminating gear of the floater and land boundary of a kind of view-based access control model and laser radar, it is characterised in that: described
Device includes data collection layer, processing diagnostic horizon and communication interface layer；
The data collection layer: including a laser radar, a vision system and a visual processes SOC；The vision SOC
By broadcasting network absolute time and relative time, the image data of vision system and the point cloud data of laser radar scanning are realized
At the time of it is synchronous；The vision system and the laser radar simultaneously to same riverbank and water surface juncture area row scanning shoot,
Acquire riverbank, algae and rubbish point cloud data and visual pattern；The vision SOC Built-in Image preprocessor, i.e., by adding
Entering anisotropic filtering may be implemented vision system image real-time de-noising and enhancing；
The processing diagnostic horizon includes: MCU, human-computer interaction module, pose measurement module, image analysis processing module and depth mind
Through network training module；By SPI communication bus communication, MCU passes through with human-computer interaction module for MCU and pose measurement module
RS485 is communicated, and the deep neural network training module and image analysis processing module directly pass through DMA in MCU memory
Carry out data exchange；
The human-computer interaction module is for providing manual remote control, display and audio output function, part when for initially installing
It is arranged, shows the harbor work's work that pulls in shore back of unmanned boat when work and work are completed；
The pose measurement module is by establishing the dynamic space coordinate fixed as origin, three axes direction using laser radar
System, determines the real-time course deflection of unmanned boat, and acquires the information, passes through the space coordinates, it may be determined that laser radar acquisition
Riverbank point cloud coordinate can calculate the approximation space plane equation and Vertical Square on the moment riverbank using space geometry vector
To the unmanned boat real time position depth of water, unmanned boat is avoided because the depth of water is too shallow and stranded, the data of the pose measurement module are also used
In deep neural network training module training pattern；
Described image analysis and processing module carries out characteristic point acquisition by image of the SIFT algorithm to data collection layer, acquires city
River riverbank texture image, algae and rubbish floater waterborne as visual pattern characteristic parameter, and match synchronization,
The radar scanning point cloud chart of the same area, and the above visual pattern, visual pattern characteristic parameter and radar scanning point cloud chart are passed
Transport to deep neural network training pattern；
The deep neural network training module, with visual pattern, visual pattern characteristic parameter and matched radar points in the same time
Cloud atlas picture is input, to be output with the course deflection information of input data mutually in the same time, carries out evolutionary training；
The communication interface layer provides the communication interface of data transmission for device；Communication interface layer include Ethernet SOC,
Powerlink module and CAN module.
2. the method for discrimination of the floater and land boundary of a kind of view-based access control model and laser radar utilizes claim 1 institute
The device stated, it is characterised in that include the following steps:
(1) according to unmanned boat structure and draft, determine that laser radar irradiation area should be at a distance from fore holding, according to nothing
People's ship draft and river structure determine the early warning distance at ship and land and water boundary；
(2) by human-computer interaction module, Current vision system and laser radar direction of illumination and irradiating angle, radar illumination are set
Direction, vision system shooting direction, the direction of advance of fore direction and unmanned boat are answered coplanar；
(3) unmanned boat after the installation is completed, is placed in water by device, into device calibration phase；
(4) following special water surface visual signature image and radar scanning point cloud data: city are first acquired by manual operation unmanned boat
River riverbank texture, algae and rubbish waterborne and other floating materials；
(5) device obtains the data of vision system and laser radar by data collection layer, adds anisotropy to filter by vision SOC
The image that wave shoots video camera pre-processes, to image noise reduction and enhancing, meanwhile, pass through the communication module of communication interface layer
It is communicated with unmanned boat propeller control, device receives the signal from propeller control, provides when front propeller pushes away
Into directional information；
(6) image analysis processing module utilizes the pretreatment image of data collection layer, establishes the time slip-window of image sequence simultaneously
Comparison front and back image changes, the characteristic parameter and invariant features parameter changed in real-time detection image, finally according to characteristic parameter
Establish floating material and riverbank characteristic model；
(7) pose measurement module real-time perfoming unmanned boat course deflection information acquires, and course deflection information is transmitted to depth
Neural training module；According to known river width and using radar as 3, any riverbank coordinate of space coordinates origin, nothing
People's ship calculates the depth of water and offshore distance of unmanned boat real time position, according to the early warning distance set, judges whether to occur
It is stranded；
(8) genetic algorithm is utilized, depth nerve training module is using image analysis processing modular character result as neural network mould
Type input, the course deflection information of pose measurement module is that output is trained, and stores network model, when the school of finishing device
Quasi- work；
(9) it if device does not complete calibration, repeats step (4) to step (8) and is calibrated；If device has been completed to calibrate, weight
Multiple step (4) and step (7), and enter step (10)；
(10) stored neural network model is utilized, image analysis processing module is recorded a demerit defeated as neural network model
Enter, the information of pose measurement module is the operation at sea that output instruct unmanned boat；
(11) step (9) are repeated and step (10) starts to carry out the online real-time working process of unmanned boat.
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| CN201811547930.3A CN109613559B (en) | 2018-12-18 | 2018-12-18 | Device and method for distinguishing water-land boundary floaters based on vision and laser radar |
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| CN201811547930.3A CN109613559B (en) | 2018-12-18 | 2018-12-18 | Device and method for distinguishing water-land boundary floaters based on vision and laser radar |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110371259A (en) * | 2019-07-29 | 2019-10-25 | 河海大学常州校区 | A kind of offshore colleting garbage floated on water device and method |
| CN110414396A (en) * | 2019-07-19 | 2019-11-05 | 中国人民解放军海军工程大学 | A kind of unmanned boat perception blending algorithm based on deep learning |
| CN110632920A (en) * | 2019-08-29 | 2019-12-31 | 上海海事大学 | Unmanned ship control method |
| CN115202370A (en) * | 2022-09-15 | 2022-10-18 | 泰山学院 | Navigation control method and system for unmanned ship and readable storage medium |
| CN116642536A (en) * | 2023-05-31 | 2023-08-25 | 交通运输部天津水运工程科学研究所 | Breakwater structure safety monitoring and early warning system based on multi-source data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110414396A (en) * | 2019-07-19 | 2019-11-05 | 中国人民解放军海军工程大学 | A kind of unmanned boat perception blending algorithm based on deep learning |
| CN110414396B (en) * | 2019-07-19 | 2021-07-16 | 中国人民解放军海军工程大学 | Unmanned ship perception fusion algorithm based on deep learning |
| CN110371259A (en) * | 2019-07-29 | 2019-10-25 | 河海大学常州校区 | A kind of offshore colleting garbage floated on water device and method |
| CN110371259B (en) * | 2019-07-29 | 2021-06-01 | 河海大学常州校区 | Near-shore water surface floater cleaning device and method |
| CN110632920A (en) * | 2019-08-29 | 2019-12-31 | 上海海事大学 | Unmanned ship control method |
| CN115202370A (en) * | 2022-09-15 | 2022-10-18 | 泰山学院 | Navigation control method and system for unmanned ship and readable storage medium |
| CN116642536A (en) * | 2023-05-31 | 2023-08-25 | 交通运输部天津水运工程科学研究所 | Breakwater structure safety monitoring and early warning system based on multi-source data |
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