CN111323418A - Sea surface oil stain monitoring system based on unmanned ship - Google Patents
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Abstract
The invention discloses a sea surface oil stain monitoring system based on an unmanned ship, which is characterized in that: the unmanned ship comprises an unmanned ship, a navigation module, a main control module, an automatic obstacle avoidance module, a data transmission module, a data processing module, a power supply module, an oil stain analysis module and a camera module. The invention combines unmanned ship cruising which can navigate in shallower water areas and island water areas and has longer cruising mileage, mature image RGB decomposition technology and residual error neural network analysis technology, can realize automatic analysis and judgment of the existence of oil stains, is more convenient, long-acting, accurate and easy to realize compared with the existing monitoring method, and has important significance for protecting marine environment.
Description
Technical Field
The invention relates to the technical field of marine information acquisition, in particular to a sea surface oil stain monitoring system based on an unmanned ship.
Background
With the rapid development of economy, the demand of petroleum is rapidly increased in our production and life, namely, the global offshore petroleum exploration and petroleum transportation industry is vigorously developed. Meanwhile, many oil spillage accidents are caused when the oil is stored and transported. At present, oil spill accidents caused by the release of oil into the ocean become serious ocean pollution worldwide; in addition, some ships illegally discharge oil in sea areas where oil contamination is prohibited, such as entering and exiting channels near ports. Sea surface oil contamination can cause great harm to the following aspects: brings great harm to fishery and aquatic products; poses a huge threat to the ecology of coastal zones, etc. The method has important significance for monitoring spilled oil pollution on the sea surface in real time. At present, the technologies commonly used for sea surface monitoring include satellite remote sensing monitoring, aviation monitoring, patrol ship monitoring and the like. The aerial remote sensing technology has the advantages of wide monitoring range, all weather, easy processing and interpretation of image data, real-time and continuous monitoring, long repeated observation period, low spatial resolution, limited number of oil spill monitoring satellites and the like, and higher cost. The patrol ship has certain maneuverability, can realize the monitoring of oil spill at sea in rainy and foggy days, is influenced and limited by factors such as work and rest rules due to manual patrol, and cannot sail in an over-shallow sea area. The unmanned ship carries the high-definition camera module, and has the advantages of flexibility, long endurance, capability of detecting oil stains in a small-range sea area, and high spectrum and spatial resolution. Therefore, the existing common technologies have many defects though having advantages, and are inconvenient to be widely used. Therefore, a sea surface oil stain monitoring system which is convenient and fast in real time, low in cost, easy to realize and accurate in oil stain monitoring is urgently needed.
Disclosure of Invention
The invention aims to overcome the defects in the background art and provides a novel sea surface oil stain monitoring system based on an unmanned boat.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides a sea greasy dirt monitoring system based on unmanned ship which characterized in that: the system comprises an unmanned boat, a navigation module, a main control module, an automatic obstacle avoidance module, a data transmission module, a data processing module, a power supply module, an oil stain analysis module and a camera module;
the navigation module receives a signal from a shore-based reference position through the data transmission module and also acquires positioning data by utilizing a GNSS antenna carried by the unmanned ship;
the main control module is used for receiving an instruction of a shore-based control personnel and automatically controlling the unmanned ship according to the instruction;
the automatic obstacle avoidance module enables the unmanned ship to avoid obstacles including static obstacles and dynamic obstacles when the unmanned ship executes a monitoring task;
the camera module is arranged on the unmanned ship and used for shooting sea surface image information;
the data processing module processes the image information acquired by the camera module, establishes a sample for the acquired image information, and adopts a red, green and blue three-channel data set of an RGB (red, green and blue) separation picture;
the image oil stain analysis module adopts a prediction result of a residual error neural network as a judgment basis for judging whether oil stains exist on the sea surface, and establishes a sample library for the collected image information;
the power supply module is used for providing power for the propulsion of the unmanned boat and each module.
A sea surface oil stain monitoring method based on unmanned boats is characterized by comprising the following steps: the method comprises the following steps: the method comprises the following steps that firstly, an unmanned ship is adopted to carry a binocular high-definition camera to photograph sea areas which are known to have oil stains and are unknown to have oil stains, and sea surface image information is collected;
step two, RGB decomposition is carried out on the sea surface picture obtained in the step one, a red, green and blue three-channel data set of the picture is obtained, and a training sample library of a residual error neural network is established;
step three, constructing a residual error neural network, inputting a three-dimensional matrix training set of a training sample library into the residual error neural network for training, and obtaining a trained residual error neural network;
fourthly, carrying out cruise shooting on the unknown oil stain-free sea area by the unmanned ship to obtain an unknown oil stain-free sea surface image;
fifthly, carrying out RGB decomposition on the sea surface picture obtained in the fourth step to obtain a red, green and blue three-channel data set of the image, and sorting the obtained data set to serve as a learning set of a residual error neural network;
step six, learning the learning set by the trained residual error neural network obtained in the step three to obtain a learning result;
step seven, the result of residual error neural network learning in step six is used as the basis for judging whether the unknown sea area has oil stain or not;
and step eight, if oil contamination exists, transmitting the information back to a ground control center, and collecting the water sample according to the instruction of shore-based control personnel.
The invention has the beneficial effects that: the invention combines unmanned ship cruising which can navigate in shallower water areas and island water areas and has longer cruising mileage, mature image RGB decomposition technology and residual error neural network analysis technology, can realize automatic analysis and judgment of the existence of oil stains, is more convenient, long-acting, accurate and easy to realize compared with the existing monitoring method, and has important significance for protecting marine environment.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The following detailed description of the preferred embodiments will be made with reference to the accompanying drawings.
As shown in fig. 1, the sea surface oil contamination monitoring system based on the unmanned ship comprises a navigation module, a main control module, an automatic obstacle avoidance module, a data transmission module, a data processing module, a power supply module, an oil contamination analysis module and a camera module;
the camera module carries a high-definition camera through the unmanned ship to shoot and obtain sea surface image information. And the automatic obstacle avoidance module adopts a laser radar to carry out real-time avoidance decision. The main control module is mainly used for carrying out automatic route planning and sampling according to a planning instruction of a shore-based control center. The data processing module adopts RGB to decompose the red, green and blue three-channel data set of the picture. And the oil contamination analysis module adopts the learning result of the residual error neural network as a judgment basis for judging whether the oil contamination exists on the sea surface, and automatically transmits the information to the shore-based control center if the oil contamination exists on the photographed sea surface.
In order to distinguish oil stain areas from oil stain-free areas, the larger the data obtained by RGB separation of the image is, the higher the brightness is, and the oil stain is not generated at the position; smaller data represent lower brightness, where there is oil contamination. For the picture with oil pollution analysis and the picture without oil pollution analysis, the RGB decomposed data set is used as a training set of the residual error neural network, and for the picture without oil pollution unknown, the RGB decomposed data set is used as a learning set of the residual error neural network.
In order to reduce the prediction error of the residual error neural network, the residual error neural network trains RGB data sets, namely training sets, of known greasy pictures and known greasy-free pictures, so that the neural network output continuously approaches to expected classification output, and finally the trained residual error neural network is obtained.
The learning result can be used as the basis for judging whether the sea surface has oil stains, so that the automatic analysis and judgment of whether the oil stains exist are realized.
Referring to fig. 2, according to the method for monitoring sea surface oil contamination based on the unmanned surface vehicle, the unmanned surface vehicle carries a high-definition camera to obtain sea surface image information of sea areas under various conditions that oil contamination is known to occur and oil contamination is known not to occur, and an image data processing module is used for decomposing, extracting and analyzing data; the learning result of the residual error neural network can be used as a judgment basis for judging whether oil stains exist or not; performing oil stain analysis on the image according to the learning result of the residual error neural network, and if determining that the oil stain problem exists in the monitored sea surface, transmitting an acquisition analysis result back to a shore-based control center in real time; the method comprises the following specific steps:
the method comprises the following steps that firstly, an unmanned ship is adopted to carry a binocular high-definition camera to photograph sea areas which are known to have oil stains and are unknown to have oil stains, and sea surface image information is collected;
step two, RGB decomposition is carried out on the sea surface picture obtained in the step one, a red, green and blue three-channel data set of the picture is obtained, and a training sample library of a residual error neural network is established;
step three, constructing a residual error neural network, inputting a three-dimensional matrix training set of a training sample library into the residual error neural network for training, and obtaining a trained residual error neural network;
fourthly, carrying out cruise shooting on the unknown oil stain-free sea area by the unmanned ship to obtain an unknown oil stain-free sea surface image;
fifthly, carrying out RGB decomposition on the sea surface picture obtained in the fourth step to obtain a red, green and blue three-channel data set of the image, and sorting the obtained data set to serve as a learning set of a residual error neural network;
step six, learning the learning set by the trained residual error neural network obtained in the step three to obtain a learning result;
step seven, the result of residual error neural network learning in step six is used as the basis for judging whether the unknown sea area has oil stain or not;
and step eight, if oil contamination is judged to exist, the information is transmitted back to a ground control center for feedback, and water sample collection is carried out according to instructions of shore-based control personnel.
The sea surface image information acquisition module adopts an unmanned ship to carry a high-definition camera. Compared with a manual driving ship, the unmanned ship has flexible monitoring, is not limited by the work and rest of personnel, and is easier to realize; the carried high-definition camera can clearly obtain sea surface images.
The image decomposition and data processing module is realized by RGB decomposition. Inputting sea surface pictures obtained by cruising and photographing of the unmanned aerial vehicle into an RGB program, decomposing red, green and blue data sets of the images, wherein the RGB data sets of the known oil-contaminated pictures and the known oil-free pictures are training sets, and the RGB data three-dimensional matrix of the unknown oil-contaminated pictures is a learning set.
And the image oil stain analysis module adopts the learning result of the residual error neural network as a judgment basis. The trained residual error neural network can learn the picture RGB data set of unknown oil stain, and the picture RGB data set enters different sample libraries to automatically identify whether the oil stain exists or not.
If the image information of the oil stains is judged to exist, the image information is automatically transmitted back to the shore-based control center, and water sample collection is carried out according to instructions of shore-based control personnel.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (2)
1. The utility model provides a sea greasy dirt monitoring system based on unmanned ship which characterized in that: the system comprises an unmanned boat, a navigation module, a main control module, an automatic obstacle avoidance module, a data transmission module, a data processing module, a power supply module, an oil stain analysis module and a camera module;
the navigation module receives a signal from a shore-based reference position through the data transmission module and also acquires positioning data by utilizing a GNSS antenna carried by the unmanned ship;
the main control module is used for receiving an instruction of a shore-based control personnel and automatically controlling the unmanned ship according to the instruction;
the automatic obstacle avoidance module enables the unmanned ship to avoid obstacles including static obstacles and dynamic obstacles when the unmanned ship executes a monitoring task;
the camera module is arranged on the unmanned ship and used for shooting sea surface image information;
the data processing module processes the image information acquired by the camera module, establishes a sample for the acquired image information, and adopts a red, green and blue three-channel data set of an RGB (red, green and blue) separation picture;
the image oil stain analysis module adopts a prediction result of a residual error neural network as a judgment basis for judging whether oil stains exist on the sea surface, and establishes a sample library for the collected image information;
the power supply module is used for providing power for the propulsion of the unmanned boat and each module.
2. The monitoring method by using the unmanned-boat-based sea surface oil stain monitoring system of claim 1, is characterized in that: the method comprises the following steps: the method comprises the following steps that firstly, an unmanned ship is adopted to carry a binocular high-definition camera to photograph sea areas which are known to have oil stains and are unknown to have oil stains, and sea surface image information is collected;
step two, RGB decomposition is carried out on the sea surface picture obtained in the step one, a red, green and blue three-channel data set of the picture is obtained, and a training sample library of a residual error neural network is established;
step three, constructing a residual error neural network, inputting a three-dimensional matrix training set of a training sample library into the residual error neural network for training, and obtaining a trained residual error neural network;
fourthly, carrying out cruise shooting on the unknown oil stain-free sea area by the unmanned ship to obtain an unknown oil stain-free sea surface image;
fifthly, carrying out RGB decomposition on the sea surface picture obtained in the fourth step to obtain a red, green and blue three-channel data set of the image, and sorting the obtained data set to serve as a learning set of a residual error neural network;
step six, learning the learning set by the trained residual error neural network obtained in the step three to obtain a learning result;
step seven, the result of residual error neural network learning in step six is used as the basis for judging whether the unknown sea area has oil stain or not;
and step eight, if oil contamination exists, transmitting the information back to a ground control center, and collecting the water sample according to the instruction of shore-based control personnel.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113483742A (en) * | 2021-09-07 | 2021-10-08 | 大连理工江苏研究院有限公司 | System and method for monitoring sea surface pollutants |
| KR20230116234A (en) * | 2022-01-28 | 2023-08-04 | 한국해양대학교 산학협력단 | Ship oil leak detection system in low light using color sensor |
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