CN116863439B - Method, device and system for predicting dead zone of aviation oil filling vehicle and aviation oil filling vehicle - Google Patents
Method, device and system for predicting dead zone of aviation oil filling vehicle and aviation oil filling vehicle Download PDFInfo
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- 239000000446 fuel Substances 0.000 claims description 38
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- G06V20/50—Context or environment of the image
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Abstract
The disclosure provides a dead zone prediction method, device and system for an aviation oil filling vehicle and the aviation oil filling vehicle. The method for predicting the dead zone of the aviation oil tanker comprises the steps of obtaining images shot by a plurality of cameras arranged on the same side of the vehicle body of the aviation oil tanker in a front-back mode, wherein the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with overlapping areas, and the overlapping areas are farther away from the vehicle body compared with the dead zone of the aviation oil tanker; determining an obstacle entering an overlapping area in an image through image identification; processing the image containing the obstacle to determine the position of the obstacle; and obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to the blind area.
Description
Technical Field
The disclosure relates to the technical field of aviation oil filling vehicles, in particular to a dead zone prediction method, device and system for an aviation oil filling vehicle and the aviation oil filling vehicle.
Background
In the operation process of the aviation fuel filling vehicle, the area where the visual field of a driver is blocked by the aviation fuel filling vehicle or the area where the sensor cannot detect is called as a blind area.
At present, a sensor is additionally arranged in a blind area to predict whether an obstacle reaches the blind area. Such as ultrasonic sensors or lidar sensors, to reduce accidents caused by dead zones.
However, the added sensors are expensive and increase the cost of the vehicle.
Disclosure of Invention
The present disclosure provides a method, apparatus, system, and system for predicting dead zone of a fuel truck, and a fuel truck using mathematical methods to replace expensive sensors, thereby reducing vehicle costs.
The disclosure provides a dead zone prediction method for an aviation oil tanker, comprising the following steps:
acquiring images shot by a plurality of cameras arranged on the same side of a vehicle body of the aviation oil filling vehicle in a front-back mode, wherein the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is far away from the vehicle body compared with a blind area of the aviation oil filling vehicle;
determining an obstacle entering the overlapping area in the image through image identification;
processing the image containing the obstacle to determine the position of the obstacle;
and obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to a dead zone.
Further, the processing the image including the obstacle, determining the position of the obstacle includes:
processing the image containing the obstacle according to a triangle parallax method to obtain the position of the obstacle;
the step of obtaining the movement information of the obstacle according to the position change of the obstacle comprises the following steps:
the position of the obstacle is changed in a preset time period, so that a plurality of positions of the obstacle in the preset time period are obtained;
projecting the plurality of positions of the obstacle movement into a target coordinate system to obtain a plurality of plane projection positions of the obstacle; the origin of coordinates of the target coordinate system is a preset point of the vehicle body of the aviation fuel filling vehicle;
and forming a motion track of the obstacle according to the plurality of plane projection positions of the obstacle.
Further, the forming the movement track of the obstacle according to the plurality of plane projection positions of the obstacle includes:
obtaining N plane coordinate sequences corresponding to a plurality of positions of the obstacle movement within the latest N moments of the preset time period;
performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the abscissa of the coordinate sequence points corresponding to each obstacle and time;
and performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the ordinate of the coordinate sequence point corresponding to each obstacle and time.
Further, the performing curve fitting on the abscissa of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences includes:
performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences by adopting 3 curves;
performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences, including:
and performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences by adopting 3 times of curves.
Further, the N is a multiple that squares the relative speed of the obstacle with respect to the movement of the aerial tanker.
Further, the relative speed of the obstacle to the movement of the aerial tanker is the ratio of the distance between two adjacent planar projection positions of the obstacle to the sampling period.
Further, the predicting whether the obstacle moves to a blind area includes:
predicting the movement of the obstacle to the blind area in the case that the movement track of the obstacle has a change trend of moving from the overlapping area toward the blind area;
and predicting that the obstacle does not move to the blind area when the movement track of the obstacle has a change trend of moving away from the blind area from the overlapped area.
The utility model provides a prediction of aviation oil tank wagon blind area device, include:
the image acquisition module is used for acquiring images shot by a plurality of cameras arranged on the front and back sides of the same side of the body of the aviation oil filling vehicle, the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is far away from the body compared with the blind area of the aviation oil filling vehicle;
the image recognition module is used for determining an obstacle entering the overlapped area in the image through image recognition;
a position determining module of an obstacle, which is used for processing the image containing the obstacle and determining the position of the obstacle;
and the obstacle prediction module is used for obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict the possibility that the obstacle moves to the blind area.
The present disclosure provides a dead zone prediction system for an aircraft fuel dispenser, comprising one or more processors configured to implement the method as described in any of the above.
The utility model provides an aviation oil tank truck, include as above-mentioned aviation oil tank truck blind area prediction system or as above-mentioned aviation oil tank truck blind area prediction device.
The present disclosure provides a computer readable storage medium having stored thereon a program which, when executed by a processor, implements a method as claimed in any one of the above.
In some embodiments, the dead zone prediction method of the aviation oil tanker obtains images shot by a plurality of cameras arranged on the same side of the aviation oil tanker in front and behind, wherein the plurality of cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera have overlapping areas, and the overlapping areas are farther away from the vehicle body than the dead zone of the aviation oil tanker; determining an obstacle entering an overlapping area in an image through image identification; processing the image containing the obstacle to determine the position of the obstacle; and obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to the blind area. In this way, the position of the obstacle is determined through a plurality of cameras which are arranged on the front and back of the same side of the body of the aviation oil filling vehicle; and according to the position change of the obstacle, the movement information of the obstacle is obtained so as to predict whether the obstacle moves to a dead zone, and the expensive sensor is replaced by logic control, so that the vehicle cost is reduced.
Drawings
Fig. 1 is a schematic flow chart of a method for predicting a dead zone of an oil tanker according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram showing an implementation of the method for predicting the dead zone of the fuel truck shown in FIG. 1;
fig. 3 is a schematic structural diagram of a dead zone prediction device of an oil tanker according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a dead zone prediction system for an oil tanker provided in an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with aspects of one or more embodiments of the present description as detailed in the accompanying claims.
It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than described in this specification. Furthermore, individual steps described in this specification, in other embodiments, may be described as being split into multiple steps; while various steps described in this specification may be combined into a single step in other embodiments.
In order to solve the technical problem that the additionally installed sensor is high in price and increases the cost of the vehicle, the embodiment of the disclosure provides a dead zone prediction method of an aviation oil tanker, which is applied to a processor or a host. Further, the method is applied to a dead zone prediction system of the aviation oil tanker. Further, the dead zone prediction method of the aviation oil filling vehicle is applied to the aviation oil filling vehicle.
The method comprises the steps that images shot by a plurality of cameras arranged on the same side of a vehicle body of the aviation oil filling vehicle in a front-back mode are obtained, the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is farther away from the vehicle body compared with a blind area of the aviation oil filling vehicle; determining an obstacle entering an overlapping area in an image through image identification; processing the image containing the obstacle to determine the position of the obstacle; and obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to the blind area.
In the embodiment of the disclosure, the position of an obstacle is determined through a plurality of cameras arranged on the front and back of the same side of the body of the aviation oil filling vehicle; and obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to the blind area. Thus, the expensive sensor is replaced mathematically, reducing the cost of the vehicle.
Fig. 1 is a schematic flow chart of a dead zone prediction method of an aviation oil tanker according to an embodiment of the disclosure.
As shown in fig. 1, the method for predicting the dead zone of the fuel truck may include, but is not limited to, the following steps 110 to 130:
step 110, obtaining images shot by a plurality of cameras arranged on the same side of the body of the aviation oil filling vehicle and front and back, wherein the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is farther away from the body compared with a blind area of the aviation oil filling vehicle.
Wherein, the driver sits on the driver's seat, the change of blind area is opposite with invariable. The absolute position of the blind area changes during running of the vehicle, but the position with respect to the driver does not change.
The blind zone may include, but is not limited to, one or more of a rear left vehicle triangle, a rear right vehicle triangle, a rear license plate area, and a chassis area. Illustratively, the vehicle rear license plate region is, for example, a vehicle rear license plate lower region, and the vehicle chassis region is, for example, a vehicle chassis lower region.
The plurality of cameras may include the following cameras. In the description of the blind area, for example, the right rear triangle of the vehicle, the first camera is disposed at the right front portion of the body head of the fuel truck, which may also be referred to as a front camera. The second camera is arranged at the rear part of the right side of the tail of the body of the aviation oil filling vehicle and is also called a rear camera.
In the description of the blind area, for example, the left rear triangle of the vehicle, the first camera is disposed at the left front portion of the body of the fuel truck, which may also be referred to as a front camera. The second camera is arranged at the left rear part of the body of the aviation oil filling vehicle and is also called a rear camera.
In response to the above description, the blind area is, for example, a license plate area at the tail of the vehicle, and the first camera is disposed at the left rear side of the body of the fuel truck, which may also be referred to as a left camera. The second camera is arranged at the right part of the rear side of the body of the aviation oil filling vehicle and is also called as a right camera.
Of course, the above-mentioned blind area is, for example, under the vehicle chassis, similar to the blind area is, for example, the right rear side triangular area of the vehicle, in which the first camera and the second camera are adaptively adjusted to be positioned in front of, behind, and/or to the left of, the same side under the vehicle chassis, and the like, as compared to the blind area is, for example, the right rear side triangular area of the vehicle.
Of course, the plurality of cameras may be monocular cameras or wide-angle cameras. The type of camera is not limited herein, as long as the above-mentioned step 110 of capturing an image can be achieved.
Between the above step 110 and the following step 120, the above method for predicting a dead zone of an oil tanker further includes performing distortion correction on the image to reduce errors, thereby improving the authenticity of the image display data.
Step 120, determining an obstacle entering the overlapping area in the image through image recognition.
The above step 120 may be implemented in a variety of implementations:
in one implementation, feature point detection is performed on a first image shot by a first camera and a second image shot by a second camera to obtain a first image feature point and a second image feature point; and matching the first image characteristic points with the second image characteristic points to identify the obstacle. In another implementation, the obstacle recognition model may be trained from obstacle image samples by using an obstacle recognition model to recognize a first image captured by the first camera and a second image captured by the second camera. Of course, the step 120 may be implemented in a related art, such as using an existing general method to identify an obstacle, which is not illustrated herein.
And 130, processing the image containing the obstacle to determine the position of the obstacle.
In some embodiments, the step 130 may further include processing the image including the obstacle, and calculating the position of the obstacle with respect to the vehicle body according to the parallax principle. Thus, the position change track of the obstacle relative to the vehicle body is fitted according to the position change. In other embodiments, the step 130 may further include processing the image including the obstacle to determine an absolute position of the obstacle. For example, the position of the obstacle is the absolute position of the obstacle, based on the geodetic coordinate system. Thus, the absolute position change track of the obstacle is fitted according to the position change. The plurality of cameras are provided at fixed positions relative to the vehicle body, and the vehicle is based on the geodetic coordinate system, and the vehicle position is also an absolute position. This vehicle position can be determined by a positioning system and each vehicle is currently provided with a positioning system, the accuracy of which can be in the order of meters, more even in the order of centimeters. The absolute position of the obstacle can be determined by the absolute positions of the cameras and the position of the obstacle relative to the vehicle body.
And 140, obtaining movement information of the obstacle according to the position change of the obstacle so as to predict whether the obstacle moves to the blind area.
Predicting whether the obstacle moves to the blind zone in the above step 140 may further include predicting that the obstacle moves to the blind zone in a case where a trend of a movement trajectory of the obstacle is moving from the overlapping region toward the blind zone; in the case where the trend of the movement locus of the obstacle is to move away from the blind area from the overlap area, the obstacle is predicted not to move to the blind area. Thus, the obstacle avoids the blind area.
The above-described step 110 to step 140 may be repeatedly performed at predetermined time intervals according to the embodiment of the present disclosure. Wherein the predetermined time interval may be, but is not limited to, 0.2-0.5 seconds.
Fig. 2 is a schematic diagram illustrating implementation of the dead zone prediction method of the aviation fuel filling vehicle shown in fig. 1.
As shown in fig. 2 in conjunction with fig. 1, the above step 130 may further include, but is not limited to, the following step 1. And the above step 140 may include, but is not limited to, the following steps 2, 3 and 4.
And 1, processing an image containing the obstacle according to a trigonometric parallax method to obtain the position of the obstacle.
In some embodiments of obtaining the movement information of the obstacle in step 140, step 2, the position of the obstacle is changed in the preset time period, so as to obtain a plurality of positions of the movement of the obstacle in the preset time period. The plurality of positions of the obstacle movement herein may include, but are not limited to, three-dimensional coordinates of the obstacle movement.
Step 3, projecting a plurality of positions of the obstacle movement into a target coordinate system to obtainA plurality of planar projection positions of the obstacle; the coordinate origin of the target coordinate system is a preset point of the body of the aviation oil filling vehicle; wherein, as shown in FIG. 2 but not limited to, a plurality of plane projection positions P respectively 0 、P 1 、P 2 ……P N-1 。
The origin of coordinates may be a reference point set according to the user's needs. The reference point may be the right rear corner of the marine tanker. Of course, the reference point can also be the left rear corner of the aviation fuel filling vehicle. Alternatively, the reference point may be a central location of the fuel dispenser. The examples herein are merely examples of origin of coordinates, and are not limiting herein, so long as they can be based on origin of coordinates, and determining a plurality of plane projection positions of the obstacle. Such that the plurality of planar projected positions of the obstacle are relative positions.
And 4, forming a movement track of the obstacle according to the plurality of plane projection positions of the obstacle. Therefore, the movement of the obstacle is detected from a dynamic view angle, the movement track of the obstacle is established, whether the obstacle moves to a dead zone is judged, expensive equipment is replaced by a mathematical method, and the cost is low. And the plane projection position of the object on the xOy plane is obtained, the three-dimensional analysis is reduced to two dimensions, the difficulty and the operand are reduced, the movement track of the obtained obstacle is more visual, more concise and more readable.
In other embodiments of obtaining the movement information of the obstacle in step 140, the movement track of the obstacle may be formed directly by using a plurality of positions of the movement of the obstacle within the preset time period. Thus, the number of intermediate processing steps is reduced, and the processing efficiency is improved.
The 4 th step may further include the steps of (1) to (3) below:
(1) And obtaining N plane coordinate sequences corresponding to a plurality of positions of the obstacle movement within the last N moments of the preset time period. The preset time period can be set according to user requirements. Illustratively, the predetermined period of time is greater than 1s and less than 5 minutes. One plane coordinate at one moment, and N plane coordinates at N moments. After the plane coordinates are obtained, the subsequent fitting is convenient. N can be set according to user's demand, N is the integer more than 1.
Wherein N is a multiple that squares the relative speed of the obstacle with respect to the movement of the marine oil tanker. The multiple may be set according to the user requirement, for example, the multiple is a positive integer greater than 1 and less than 8. Exemplary multiples are, but are not limited to, 4,5,6. Specifically, n=5×round (v), v is the relative velocity of the obstacle P, round (v) represents rounding the velocity v, and v is in m/s.
The relative speed of the obstacle relative to the movement of the aviation fuel filling vehicle is the ratio of the distance between two adjacent plane projection positions of the obstacle to the sampling period. By way of example, the relative velocity v of the obstacle P is calculated by: v=p N- 1 P N-2 Distance of (x) sample period.
(2) And performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the abscissa of the coordinate sequence points corresponding to each obstacle and time. The step further may include performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences by using 3 curves. Therefore, the 3-time curve is more in line with the actual running track, so that the 3-time curve is adopted for curve fitting, the fitted curve is conveniently obtained, the fitting accuracy can be improved, and the 3-time curve is in line with the actual obstacle running track for subsequent prediction.
(3) And performing curve fitting on the ordinate of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the ordinate of the coordinate sequence points corresponding to each obstacle and time. The step may further include performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences by using 3-time curves.
The above-mentioned relation includes a corresponding function relation or a corresponding table, and the like, and is not exemplified here.
In this embodiment, it is further advantageous to determine the correspondence between the ordinate and the abscissa of the coordinate sequence point corresponding to each obstacle and time.
As shown in connection with fig. 2, embodiments of the present disclosure are exemplified as follows;
1) Calculating the projection P of the coordinates of the obstacle P in an xOy coordinate system according to the parallax principle T The coordinate origin of the xOy coordinate system is at the right rear corner of the vehicle, the horizontal right direction is the positive direction of the x axis, and the horizontal forward direction is the positive direction of the y axis; t is time.
2) Periodically repeating steps 110, 120 and 130 (including step 1) to obtain a plane coordinate sequence { P } of the nearest N times of the obstacle P N The } is { P 0 、P 1 、P 2 ……P N-1 }。
3) Will sequence { P N Fitting the abscissa of each point by adopting 3-time curve to obtain the relation of the abscissa and time as f x (N)。
4) Will sequence { P N Fitting the ordinate of each point by adopting a 3-time curve to obtain the relation of the ordinate and time as f y (N)。
5) After the obstacle P moves outside the field of view of the first camera and the field of view of the second camera, the function relation f can be passed x (N)、f y And (N) predicting the coordinates of the obstacle P, and further judging whether the obstacle P moves to a dead zone.
Fig. 3 is a schematic structural diagram of a dead zone prediction device of an aviation fuel tank truck according to an embodiment of the disclosure.
As shown in fig. 3, the dead zone prediction device for the aviation oil tanker comprises:
the image acquisition module 21 is configured to acquire images captured by a plurality of cameras disposed on the same side of the body of the aviation oil tanker, where the plurality of cameras include a first camera and a second camera, and the view angle of the first camera and the view angle of the second camera have an overlapping area, and the overlapping area is farther away from the body than a blind area of the aviation oil tanker;
an image recognition module 22 for determining an obstacle entering the overlapping region in the image by image recognition;
a position determining module 23 of the obstacle, configured to process the image including the obstacle, and determine the position of the obstacle;
the obstacle predicting module 24 is configured to obtain movement information of the obstacle according to the position change of the obstacle, so as to predict the possibility that the obstacle moves to the blind area.
The implementation process of the functions and roles of each module in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
The dead zone prediction device of the aviation fuel filling vehicle can be applied to a processor but is not limited to the dead zone prediction device. Further, the dead zone prediction device of the aviation fuel filling vehicle can be applied to a dead zone prediction system of the aviation fuel filling vehicle without limitation.
The embodiment of the disclosure provides a dead zone prediction system of an aviation oil tanker, which comprises one or more processors and is used for realizing the dead zone prediction method of the aviation oil tanker.
The embodiment of the disclosure provides an aviation oil filling vehicle, which comprises an aviation oil filling vehicle blind area prediction system or an aviation oil filling vehicle blind area prediction device.
Fig. 4 is a block diagram illustrating a dead zone prediction system 30 for an oil tanker provided by an embodiment of the present disclosure.
As shown in fig. 4, the fuel dispenser dead zone prediction system 30 includes one or more processors 31 for implementing the fuel dispenser dead zone prediction method described above.
In some embodiments, the fuel dispenser dead zone prediction system 30 may include a computer readable storage medium 39, the computer readable storage medium 39 may store a program that may be invoked by the processor 31, and may include a non-volatile storage medium. In some embodiments, the fuel dispenser dead zone prediction system 30 may include a memory 38 and an interface 37. In some embodiments, the fuel dispenser dead zone prediction system 30 may also include other hardware depending on the application.
The computer readable storage medium 39 of the presently disclosed embodiment has stored thereon a program for implementing the method of predicting a dead zone of an aircraft fuel dispenser as described above when executed by the processor 31.
The present disclosure may take the form of a computer program product embodied on one or more computer-readable storage media 39 (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having program code embodied therein. Computer readable storage media 39, including both permanent and non-permanent, removable and non-removable media, may be any method or technology for information storage. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer readable storage media 39 include, but are not limited to: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by the computing device.
In the description of the present disclosure, it should be understood that the terms "middle," "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate an orientation or a positional relationship based on that shown in the drawings, merely to facilitate description of the present disclosure and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present disclosure.
In the description of the present disclosure, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "first," "second," etc. can include at least one such feature, either explicitly or implicitly. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.
In the present disclosure, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the terms in this disclosure will be understood by those of ordinary skill in the art as the case may be.
In this disclosure, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact through an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
It will be understood that when an element is referred to as being "mounted," "positioned," "secured" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. Further, when one element is considered as being "fixedly connected" to another element, the two elements may be fixed by a detachable connection manner, or may be fixed by a non-detachable connection manner, such as sleeving, clamping, integrally forming, or welding, which may be implemented in the conventional technology, which is not further described herein.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples merely represent several embodiments of the present disclosure, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that variations and modifications can be made by those skilled in the art without departing from the inventive concepts of the present disclosure, which are within the scope of the present disclosure.
Claims (7)
1. The dead zone prediction method for the aviation oil tanker is characterized by comprising the following steps of:
acquiring images shot by a plurality of cameras arranged on the same side of a vehicle body of the aviation oil filling vehicle in a front-back mode, wherein the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is far away from the vehicle body compared with a blind area of the aviation oil filling vehicle;
determining an obstacle entering the overlapping area in the image through image identification;
processing the image containing the obstacle to determine the position of the obstacle;
the position of the obstacle is changed in a preset time period, so that a plurality of positions of the obstacle in the preset time period are obtained;
projecting the plurality of positions of the obstacle movement into a target coordinate system to obtain a plurality of plane projection positions of the obstacle; the origin of coordinates of the target coordinate system is a preset point of the vehicle body of the aviation fuel filling vehicle;
obtaining N plane coordinate sequences corresponding to a plurality of positions of the obstacle movement within the latest N moments of the preset time period;
performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the abscissa of the coordinate sequence points corresponding to each obstacle and time;
performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences to obtain the relation between the ordinate of the coordinate sequence point corresponding to each obstacle and time; the N is a multiple of the relative speed of the obstacle relative to the movement of the aviation fuel filling vehicle; the relative speed of the obstacle relative to the movement of the aviation fuel truck is the ratio of the distance between two adjacent plane projection positions of the obstacle to the sampling period.
2. The method of predicting a dead zone of an aircraft fuel dispenser of claim 1, wherein said processing said image including an obstacle to determine a location of said obstacle comprises:
and processing the image containing the obstacle according to a trigonometric parallax method to obtain the position of the obstacle.
3. The method for predicting the dead zone of the aviation fuel truck according to claim 1, wherein the curve fitting the abscissa of the plane coordinate sequence points corresponding to the obstacles in the N plane coordinate sequences comprises:
performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences by adopting 3 curves;
performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences, including:
and performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences by adopting 3 times of curves.
4. The method of predicting a dead zone of an aircraft fuel dispenser of claim 1, wherein said predicting whether said obstacle moves to a dead zone comprises:
predicting the movement of the obstacle to the blind area in the case that the movement track of the obstacle has a change trend of moving from the overlapping area toward the blind area;
and predicting that the obstacle does not move to the blind area when the movement track of the obstacle has a change trend of moving away from the blind area from the overlapped area.
5. The utility model provides a navigation oil tank wagon blind area prediction unit which characterized in that includes:
the image acquisition module is used for acquiring images shot by a plurality of cameras arranged on the front and back sides of the same side of the body of the aviation oil filling vehicle, the cameras comprise a first camera and a second camera, the view angle of the first camera and the view angle of the second camera are provided with an overlapping area, and the overlapping area is far away from the body compared with the blind area of the aviation oil filling vehicle;
the image recognition module is used for determining an obstacle entering the overlapped area in the image through image recognition;
a position determining module of an obstacle, which is used for processing the image containing the obstacle and determining the position of the obstacle;
the obstacle prediction module is used for obtaining the movement information of the obstacle according to the position change of the obstacle so as to predict the possibility that the obstacle moves to a blind area; wherein, according to the position change of the obstacle, obtaining the movement information of the obstacle comprises: the position of the obstacle is changed in a preset time period, so that a plurality of positions of the obstacle in the preset time period are obtained; projecting the plurality of positions of the obstacle movement into a target coordinate system to obtain a plurality of plane projection positions of the obstacle; the origin of coordinates of the target coordinate system is a preset point of the vehicle body of the aviation fuel filling vehicle; obtaining N plane coordinate sequences corresponding to a plurality of positions of the obstacle movement within the latest N moments of the preset time period; performing curve fitting on the abscissa of the plane coordinate sequence points corresponding to each obstacle of the N plane coordinate sequences to obtain the relationship between the abscissa of the coordinate sequence points corresponding to each obstacle and time; performing curve fitting on the ordinate of the plane coordinate sequence point corresponding to each obstacle of the N plane coordinate sequences to obtain the relation between the ordinate of the coordinate sequence point corresponding to each obstacle and time; the N is a multiple of the relative speed of the obstacle relative to the movement of the aviation fuel filling vehicle; the relative speed of the obstacle relative to the movement of the aviation fuel truck is the ratio of the distance between two adjacent plane projection positions of the obstacle to the sampling period.
6. A fuel dispenser dead zone prediction system comprising one or more processors configured to implement the fuel dispenser dead zone prediction method of any one of claims 1-4.
7. A fuel dispenser comprising the fuel dispenser dead zone prediction system according to claim 6 or the fuel dispenser dead zone prediction apparatus according to claim 5.
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