CN111144264A - Intelligent transportation management system and method based on image recognition - Google Patents
Intelligent transportation management system and method based on image recognition Download PDFInfo
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- 238000006073 displacement reaction Methods 0.000 claims description 20
- 238000007405 data analysis Methods 0.000 claims description 16
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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Abstract
The invention relates to the technical field of transportation management, in particular to an intelligent transportation management system and method based on image recognition, wherein the system comprises: the cargo analysis subsystem is used for acquiring an image A of the vehicle outline with the reference mark, acquiring the reference length of a preset reference mark and acquiring the mark length of the reference mark according to the image A and the reference length; the system is also used for acquiring theoretical cargo information and calculating the actual cargo volume according to the mark length and the theoretical cargo information; the auxiliary analysis subsystem is used for obtaining the standard auxiliary distance, the actual auxiliary distance and the material of the carried object, and generating an auxiliary analysis conclusion according to the standard auxiliary distance, the actual auxiliary distance, the material of the carried object and the actual volume of the carried object. This scheme of adoption can effectively make statistics of the single actual carrier volume of vehicle according to the reference mark to moving down through the carriage checks actual carrier volume, thereby avoiding hiding the condition of another building material under building material, effectively supervising the transportation condition.
Description
Technical Field
The invention relates to the technical field of transportation management, in particular to an intelligent transportation management system and method based on image recognition.
Background
In all industries, the management of goods is extremely important, especially for the traditional building industry, so that a transportation management department is independently arranged to manage the transportation of building materials. The transportation management of the building materials needs to count the types, transportation times, single transportation amount, total transportation amount and other data of the building materials, a manual management mode is adopted, a large amount of manpower is needed, and the workload is large, so that a semi-intelligent transportation management system appears.
Through set up the platform scale at the place of carrying cargo, regard the weight that the platform scale shows as single transportation volume to through the mode record building material's that artificially looks over type, thereby realize the transportation management to building material. However, such an approach still has the following disadvantages: the same transport vehicle hides another building material in the same transport vehicle under the condition of transporting one building material, and the single transport volume measured by the platform scale is larger than the actual transport volume; and through the artifical identification building material type, still need the staff to get on the bus and look over, spend more time, and have the safety risk easily.
With the rise of image recognition, people are assisted in management through image recognition, for example, the image recognition is applied to transportation management, building materials in images are obtained through the image recognition, textures of the building materials are analyzed to obtain types of the building materials, the length, the width and the height of a transport vehicle in the images are obtained through the image recognition, and therefore the single transportation volume of the transport vehicle is calculated. However, the following disadvantages may exist when image recognition is used: firstly, need acquire the image of a plurality of angles, consequently need set up the camera on a plurality of angles, or adopt a camera, but need follow different angles and shoot the image to make the manpower and the expense of maintaining the camera increase. The second is that the single transportation amount may not fill the whole carriage, but the volume of the carriage is used as the single transportation volume in image recognition, and errors exist among data of the volume. Thirdly, the situation that another building material is hidden under the building material cannot be solved, and the transportation situation cannot be effectively supervised.
Disclosure of Invention
The invention aims to provide an intelligent transportation management system and method based on image recognition, which can effectively count the single actual carrying volume of a vehicle according to a reference mark and check the actual carrying volume through the downward movement of a carriage, thereby avoiding the situation that another building material is hidden under the building material and effectively supervising the transportation situation.
The basic scheme provided by the invention is as follows: intelligent transportation management system based on image recognition includes:
the cargo analysis subsystem is used for acquiring an image A of the vehicle outline with the reference mark, acquiring the reference length of a preset reference mark and acquiring the mark length of the reference mark in the image A according to the image A and the reference length; the system is also used for acquiring theoretical cargo information and calculating the actual cargo volume according to the mark length and the theoretical cargo information;
and the auxiliary analysis subsystem is used for acquiring the standard auxiliary distance, the actual auxiliary distance and the loading material, and generating an auxiliary analysis conclusion according to the standard auxiliary distance, the actual auxiliary distance, the loading material and the actual loading volume.
Description of the drawings: the reference mark is a figure artificially marked on the carriage; the standard auxiliary distance is the distance from the bottom of the carriage to the ground when the vehicle is unloaded; the actual auxiliary distance is the distance from the bottom of the carriage to the ground when carrying objects; the material of the object carrying is the material type of the object carrying; the theoretical cargo information includes car length, car width, and car height.
The basic scheme has the following working principle and beneficial effects: the cargo analysis subsystem carries out image recognition on the image A, obtains the mark length under the condition that the reference length is known, and calculates the actual cargo volume according to the theoretical cargo information which is input in advance or the theoretical cargo information which is recognized through the image, thereby obtaining the single transportation volume of the vehicle.
When the carriage is loaded with building materials, the carriage moves downwards under the action of gravity, namely, the distance from the bottom of the carriage to the ground is reduced along with the increase of the building materials. Obtain standard auxiliary distance, actual auxiliary distance, carry the thing material through the auxiliary analysis subsystem, wherein standard auxiliary distance, actual auxiliary distance, it can be for managers actual measurement or look over to carry the thing material, also can obtain through image recognition, according to standard auxiliary distance, actual auxiliary distance, carry the thing material, the supplementary analysis conclusion of actual year thing volume generation, move down through the carriage and check actual year thing volume, reflect the result of checking through supplementary analysis conclusion, thereby avoid hiding the condition of another building material under the building material, carry out effective supervision to the transportation condition.
Further, the cargo analysis subsystem comprises:
the system comprises a reference analysis module, a data analysis module and a data analysis module, wherein the reference analysis module is used for acquiring an image A of a vehicle outline with a reference mark, acquiring the mark length of the reference mark in the image A according to the preset reference length of the reference mark, and sending the mark length to the data analysis module;
the vehicle analysis module is used for acquiring the theoretical cargo information of the vehicle in the image A and sending the theoretical cargo information to the data analysis module;
and the data analysis module is used for calculating the actual carrying volume according to the mark length and the theoretical carrying information.
Description of the drawings: the mark length is the distance from the top of the car to the surface of the building material loaded in the car.
Has the advantages that: the reference length is fixedly set, and the mark length is obtained by the reference length, so that the condition that a length is measured in advance to serve as a known quantity every time image recognition is carried out is avoided.
Further, the auxiliary analysis subsystem comprises:
the comparison analysis module is used for acquiring the standard auxiliary distance and the actual auxiliary distance and generating a theoretical loading weight interval according to the standard auxiliary distance and the actual auxiliary distance;
the weight analysis module is used for acquiring the material of the carried object and generating the actual carried object weight according to the material of the carried object and the actual carried object volume;
the auxiliary analysis conclusion comprises actual cargo information and a reminding report;
and the auxiliary judgment module is used for judging whether the actual carrying weight is in the theoretical carrying weight interval or not, if so, generating actual carrying information according to the actual carrying weight, and if not, generating a reminding report according to the theoretical carrying weight interval and the actual carrying weight.
Has the advantages that: the downward movement of the carriage is related to the load capacity of the vehicle, and due to the fact that an error exists in image recognition, when the error is reflected on the downward movement distance of the carriage, a larger error occurs in data, so that a theoretical load capacity interval is generated according to the standard auxiliary distance and the actual auxiliary distance, and the current load capacity of the vehicle is reflected in an interval mode.
Further, the reference analysis module is further configured to obtain a spatial relationship between the reference length and the mark length, and obtain the mark length in the image a according to the spatial relationship and the reference length.
Description of the drawings: the spatial relationship is vertical, parallel, etc.
Has the advantages that: under the condition that the reference length and the spatial relation are known, the mark length can be identified through image identification, and the actual carrying volume can be conveniently calculated subsequently.
Further, the reference mark is rectangular, and the reference mark is located on the vehicle.
Has the advantages that: the rectangle is easy to draw, the spatial relationship is fixed, and the rectangle is drawn on the vehicle to be used as a reference mark, so that image recognition is facilitated.
Further, the vehicle analysis module is used for obtaining the vehicle model of the vehicle in the image A and obtaining theoretical cargo information of the vehicle according to the vehicle model, wherein the theoretical cargo information comprises the carriage length, the carriage width and the carriage height.
Has the advantages that: theoretical cargo information of the vehicle can be obtained through the vehicle model, different vehicle models have different marks on the vehicle body, and the corresponding vehicle model can also be obtained through the license plate number of the vehicle, so that the actual carrying volume of the vehicle can be conveniently calculated.
Further, the vehicle analysis module is used for obtaining a preset reference length and a spatial relationship between the reference length and the mark length, and obtaining theoretical cargo information of the vehicle according to the reference length, the spatial relationship and the vehicle contour in the image A, wherein the theoretical cargo information is the carriage length, the carriage width and the carriage height.
Has the advantages that: and acquiring theoretical cargo information by means of image recognition when the reference length and the spatial relationship are known and the carriage is known to be a cuboid. Compared with the method of acquiring the theoretical cargo information through the carriage model, the method of acquiring the theoretical cargo information through the image recognition reduces the related processes.
Further, the data analysis module is used for generating an actual height according to the mark length and the carriage height, and generating an actual carrying volume according to the carriage length, the carriage width and the actual height.
Description of the drawings: the actual height is the height of the building material in the carriage.
Has the advantages that: and obtaining the actual height through the mark length and the carriage height, thereby calculating the actual carrying volume.
Further, the system also comprises a database, wherein an auxiliary displacement and load capacity association table is preset in the database;
and the comparison analysis module is used for generating auxiliary displacement according to the standard auxiliary distance and the actual auxiliary distance and screening out a theoretical loading weight interval from the auxiliary displacement and loading weight association table according to the auxiliary displacement.
Description of the drawings: the assist displacement is the difference between the standard assist distance and the actual assist distance.
Has the advantages that: the theoretical loading weight interval is screened out from the auxiliary displacement and the loading weight correlation table through the auxiliary displacement, and the actual loading condition of the vehicle is checked through the theoretical loading weight interval, so that the transportation condition is effectively supervised.
The invention also provides an intelligent transportation management method based on image recognition, and the intelligent transportation management system based on image recognition is used.
Has the advantages that: and (3) carrying out image recognition on the image A, obtaining the mark length under the condition that the reference length is known, and calculating the actual carrying volume according to the theoretical carrying information recorded in advance or the theoretical carrying information recognized through the image so as to obtain the single transportation volume of the vehicle.
When the carriage is loaded with building materials, the carriage moves downwards under the action of gravity, namely, the distance from the bottom of the carriage to the ground is reduced along with the increase of the building materials. The method comprises the steps of obtaining a standard auxiliary distance, an actual auxiliary distance and a carrying material, wherein the standard auxiliary distance, the actual auxiliary distance and the carrying material can be actually measured or checked by managers and can also be obtained through image recognition, generating an auxiliary analysis conclusion according to the standard auxiliary distance, the actual auxiliary distance, the carrying material and the actual carrying volume, checking the actual carrying volume through downward movement of a carriage, reflecting a checking result through the auxiliary analysis conclusion, avoiding hiding the condition of another building material under the building material, and effectively supervising the transportation condition.
Drawings
Fig. 1 is a logic block diagram of a first embodiment of an intelligent transportation management system based on image recognition according to the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
An intelligent transportation management system based on image recognition is shown in figure 1 and comprises a cargo-carrying analysis subsystem, an auxiliary analysis subsystem and a database, wherein a reference length, a spatial relationship, a standard auxiliary distance, a plurality of auxiliary displacement and load capacity association tables and a cargo material and weight association table are preset in the database, and the standard auxiliary distance is the distance from the bottom of a carriage to the ground when a vehicle is not loaded; the auxiliary displacement and load capacity association table records the corresponding relation between the auxiliary displacement and the theoretical load capacity interval, and the auxiliary displacement and the theoretical load capacity interval are different under different vehicle types; the correlation table of the material and the weight of the carried object records the corresponding relation between the material and the unit weight of the carried object, and the unit weight of different material of the carried object is different. In this embodiment, for convenience of management, the vehicles are of the same type, that is, the distance from the bottom of the carriage to the ground is the same when the vehicles are unloaded.
When the vehicle is loaded with the building materials, the carriage is not full, and after the loader finishes loading, the loader draws a reference mark on the carriage. The required reference mark is rectangular, the width of the reference mark is X, the value of X can be unified into a fixed numerical value by workers, the length of the reference mark is the distance from the building material to the top of the carriage, the width of the reference mark is the reference length, the length of the reference mark is the mark length, namely the known reference length is X, and the spatial relationship between the reference length and the mark length is vertical.
A cargo analysis subsystem comprising:
the reference analysis module is used for acquiring an image A of the vehicle contour with a reference mark, wherein the vehicle contour in the image A at the moment comprises a carriage tail part, a carriage left side and a carriage top part; the image A is used for acquiring the reference length, the spatial relationship between the reference length and the mark length from the database, identifying the mark length of the reference mark in the image A according to the reference length and the spatial relationship through an AI technology, and sending the mark length to the data analysis module.
The vehicle analysis module is used for acquiring an image A of a vehicle contour with a reference mark, wherein the vehicle contour in the image A at the moment comprises a carriage tail part, a carriage left side and a carriage top part; and the system is also used for acquiring the spatial relationship among the reference length, the reference length and the mark length from the database, identifying theoretical cargo information of the vehicle in the image A according to the reference length and the spatial relationship through an AI technology, and sending the theoretical cargo information to the data analysis module and the auxiliary analysis subsystem. The theoretical cargo information is the car length, car width and car height.
The data analysis module is used for receiving the marked length and the theoretical cargo information and generating the actual height according to the marked length and the height of the carriage in the theoretical cargo information; specifically, the actual height is the height of the building material in the carriage, which is obtained by subtracting the mark length from the carriage height. And the system is also used for generating an actual carrying volume according to the length, the width and the actual height of the carriage and sending the actual carrying volume to the auxiliary analysis subsystem.
The auxiliary analysis subsystem includes:
the comparison analysis module is used for acquiring an image A of the vehicle contour with the reference mark, wherein the vehicle contour in the image A at the moment comprises a carriage tail part, a carriage left side and a carriage top part; the system is also used for acquiring the reference length, the spatial relationship between the reference length and the mark length and the standard auxiliary distance from the database, identifying the distance from the bottom of the carriage to the bottom surface in the image A as an actual auxiliary distance according to the reference length and the spatial relationship by an AI technology, and generating an auxiliary displacement according to the standard auxiliary distance and the actual auxiliary distance, wherein the auxiliary displacement is obtained by subtracting the actual auxiliary distance from the standard auxiliary distance; and the auxiliary judging module is also used for receiving theoretical cargo information, acquiring an auxiliary displacement and load capacity association table from the database according to the theoretical cargo information, screening a corresponding theoretical cargo capacity interval from the auxiliary displacement and load capacity association table according to the auxiliary displacement and sending the theoretical cargo capacity interval to the auxiliary judging module.
The weight analysis module is used for acquiring the image A, the vehicle contour in the image A at the moment comprises a carriage tail part, a carriage left side and a carriage top part, the material of a carrying object is identified through an AI technology, and material identification can be realized through the existing material identification software. The device is also used for acquiring the carrying material and weight correlation table from the database, screening out unit weight according to the carrying material, generating actual carrying weight according to the unit weight and the actual carrying volume and sending the actual carrying weight to the auxiliary judgment module.
And the auxiliary judgment module is used for judging whether the actual carrying weight is in the theoretical carrying weight interval or not and generating an auxiliary analysis conclusion, and the auxiliary analysis conclusion comprises actual carrying information and a reminding report. When the actual carrying weight is within the theoretical carrying weight interval, generating actual carrying information according to the actual carrying weight and recording the actual carrying information in a database; and when the actual carrying weight is outside the theoretical carrying weight interval, generating a reminding report according to the theoretical carrying weight interval and the actual carrying weight, and feeding the reminding report back to the transportation management personnel.
The embodiment also provides an intelligent transportation management method based on image recognition, and the intelligent transportation management system based on image recognition is used
Example two
The difference between the present embodiment and the first embodiment is: the theoretical cargo information is not directly recognized by the AI technique, but is obtained by recognizing the vehicle model thereof. In this embodiment, to match different transportation tasks, a plurality of vehicle models are used, i.e. the standard assistance distances of different vehicles are related to the vehicle model.
An information comparison table is also preset in the database, and the comparison relationship among the license plate, the model number, the theoretical cargo information and the standard auxiliary distance of the vehicle is recorded in the information comparison table.
The comparison and analysis module is also used for identifying the license plate of the vehicle according to the vehicle analysis module when the standard auxiliary distance is obtained, obtaining the information comparison table from the database and screening the standard auxiliary distance from the information comparison table according to the license plate.
The vehicle analysis module is further used for obtaining the vehicle model of the vehicle in the image A and obtaining theoretical cargo information of the vehicle according to the vehicle model, wherein the theoretical cargo information comprises the carriage length, the carriage width and the carriage height. Specifically, the vehicle analysis module is used for acquiring an image A of a vehicle contour with a reference mark, wherein the vehicle contour in the image A at the moment comprises a carriage tail part, a carriage left side and a carriage top part; the system is also used for identifying the license plate of the vehicle through an AI technology, acquiring the information comparison table from the database, screening out theoretical cargo information from the information comparison table according to the license plate, and sending the theoretical cargo information to the data analysis module and the auxiliary analysis subsystem.
The embodiment also provides an intelligent transportation management method based on image recognition, and the intelligent transportation management system based on image recognition is used
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. Intelligent transportation management system based on image recognition, its characterized in that includes:
the cargo analysis subsystem is used for acquiring an image A of the vehicle outline with the reference mark, acquiring the reference length of a preset reference mark and acquiring the mark length of the reference mark in the image A according to the image A and the reference length; the system is also used for acquiring theoretical cargo information and calculating the actual cargo volume according to the mark length and the theoretical cargo information;
and the auxiliary analysis subsystem is used for acquiring the standard auxiliary distance, the actual auxiliary distance and the loading material, and generating an auxiliary analysis conclusion according to the standard auxiliary distance, the actual auxiliary distance, the loading material and the actual loading volume.
2. The intelligent transportation management system based on image recognition of claim 1, wherein the cargo analysis subsystem comprises:
the system comprises a reference analysis module, a data analysis module and a data analysis module, wherein the reference analysis module is used for acquiring an image A of a vehicle outline with a reference mark, acquiring the mark length of the reference mark in the image A according to the preset reference length of the reference mark, and sending the mark length to the data analysis module;
the vehicle analysis module is used for acquiring the theoretical cargo information of the vehicle in the image A and sending the theoretical cargo information to the data analysis module;
and the data analysis module is used for calculating the actual carrying volume according to the mark length and the theoretical carrying information.
3. The intelligent image recognition-based transportation management system of claim 1, wherein the auxiliary analysis subsystem comprises:
the comparison analysis module is used for acquiring the standard auxiliary distance and the actual auxiliary distance and generating a theoretical loading weight interval according to the standard auxiliary distance and the actual auxiliary distance;
the weight analysis module is used for acquiring the material of the carried object and generating the actual carried object weight according to the material of the carried object and the actual carried object volume;
the auxiliary analysis conclusion comprises actual cargo information and a reminding report;
and the auxiliary judgment module is used for judging whether the actual carrying weight is in the theoretical carrying weight interval or not, if so, generating actual carrying information according to the actual carrying weight, and if not, generating a reminding report according to the theoretical carrying weight interval and the actual carrying weight.
4. The intelligent transportation management system based on image recognition as claimed in claim 2, wherein: the reference analysis module is further configured to obtain a spatial relationship between the reference length and the mark length, and obtain the mark length in the image a according to the spatial relationship and the reference length.
5. The intelligent transportation management system based on image recognition as claimed in claim 4, wherein: the reference mark is rectangular and is located on the vehicle.
6. The intelligent transportation management system based on image recognition as claimed in claim 2, wherein: the vehicle analysis module is used for obtaining the vehicle model of the vehicle in the image A and obtaining theoretical cargo information of the vehicle according to the vehicle model, wherein the theoretical cargo information comprises the carriage length, the carriage width and the carriage height.
7. The intelligent transportation management system based on image recognition as claimed in claim 2, wherein: the vehicle analysis module is used for acquiring a preset reference length, a spatial relation between the reference length and the mark length, and acquiring theoretical cargo information of the vehicle according to the reference length, the spatial relation and the vehicle contour in the image A, wherein the theoretical cargo information is the carriage length, the carriage width and the carriage height.
8. The intelligent transportation management system based on image recognition according to claim 6 or 7, wherein: and the data analysis module is used for generating an actual height according to the mark length and the carriage height and generating an actual carrying volume according to the carriage length, the carriage width and the actual height.
9. The intelligent transportation management system based on image recognition as claimed in claim 3, wherein: the system also comprises a database, wherein an auxiliary displacement and load capacity association table is preset in the database;
and the comparison analysis module is used for generating auxiliary displacement according to the standard auxiliary distance and the actual auxiliary distance and screening out a theoretical loading weight interval from the auxiliary displacement and loading weight association table according to the auxiliary displacement.
10. The intelligent transportation management method based on image recognition is characterized by comprising the following steps: use of the intelligent image recognition based transportation management system of any of the preceding claims 1-9.
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| CN201911328629.8A CN111144264A (en) | 2019-12-20 | 2019-12-20 | Intelligent transportation management system and method based on image recognition |
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- 2019-12-20 CN CN201911328629.8A patent/CN111144264A/en active Pending
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| CN109116362A (en) * | 2018-07-03 | 2019-01-01 | 四川驹马科技有限公司 | A kind of adaptive load-carrying detection system of lorry based on ultrasound and its method |
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Application publication date: 20200512 |