CN113361989A - Cargo identification method, computer equipment and cargo monitoring system - Google Patents
Cargo identification method, computer equipment and cargo monitoring system Download PDFInfo
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- CN113361989A CN113361989A CN202110496901.4A CN202110496901A CN113361989A CN 113361989 A CN113361989 A CN 113361989A CN 202110496901 A CN202110496901 A CN 202110496901A CN 113361989 A CN113361989 A CN 113361989A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0833—Tracking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
Abstract
The invention provides a cargo identification method, a computer readable storage medium, computer equipment and a cargo monitoring system, wherein the method is implemented for each vehicle and comprises the following steps: acquiring a basic image and a plurality of carriage images in different loading states as sample data, wherein the basic image is an empty carriage image; respectively carrying out differential feature extraction on a plurality of carriage images in different loading states and a basic image to obtain a plurality of difference graphs, and taking the plurality of difference graphs as training data; inputting training data into a convolutional neural network model for training to obtain a cargo identification model; acquiring a carriage image in real time; and carrying out differentiation feature extraction on the carriage image and the basic image acquired in real time to obtain a difference map, then transmitting the difference map into the cargo identification model for reasoning, comparing the difference maps obtained at adjacent moments to obtain cargo change information in the carriage in real time. And the carriage is monitored in real time without manual participation.
Description
Technical Field
The invention belongs to the field of logistics supervision, and particularly relates to a cargo identification method, a computer readable storage medium, computer equipment and a cargo monitoring system.
Background
In the field of freight transportation, unexpected events such as loss and damage of cargos can occur, and at present, the quantity of cargos in the vehicle is mainly judged by checking the cargos in a manual field or by using a camera to collect pictures. These solutions have limitations: firstly, manual on-site viewing; but is only suitable for the condition that the goods are difficult to judge when the vehicle stops running; secondly, images collected by a camera; the goods can be called and checked after transportation is finished, and huge loss can be caused because the condition that the goods are lost is not processed in time; thirdly, online video monitoring; but causes high flow cost and requires real-time monitoring by matching manpower.
Disclosure of Invention
The invention aims to provide a cargo identification method, a computer readable storage medium, computer equipment and a cargo monitoring system, and aims to solve the problem that the abnormity of cargo reduction and the like cannot be found in time in the cargo transportation process.
In a first aspect, the present invention provides a cargo identification method, which is a step performed for each vehicle, including:
acquiring a basic image and a plurality of carriage images in different loading states as sample data, wherein the basic image is an empty carriage image;
respectively carrying out differential feature extraction on a plurality of carriage images in different loading states and a basic image to obtain a plurality of difference graphs, and taking the plurality of difference graphs as training data;
inputting training data into a convolutional neural network model for training to obtain a cargo identification model;
acquiring a carriage image in real time;
and carrying out differentiation feature extraction on the carriage image and the basic image acquired in real time to obtain a difference map, then transmitting the difference map into the cargo identification model for reasoning, comparing the difference maps obtained at adjacent moments to obtain cargo change information in the carriage in real time.
Further, the differential feature extraction includes pixel differencing and vector differencing.
Further, when the differential feature extraction adopts a pixel difference, the cargo identification method further includes: and respectively converting the carriage image and the basic image into grayscale images with pixel values between 0 and 255.
In a second aspect, the invention provides a computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for identifying an item as described above.
In a third aspect, the present invention provides a computer device comprising: one or more processors, a memory, and one or more computer programs, the processors and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, which when executing the computer programs implement the steps of the item identification method.
In a fourth aspect, the invention provides a cargo monitoring system, which comprises the computer device, a camera module and a reminding module, wherein the camera module is connected with the computer device and is used for acquiring images and transmitting the images to the computer device; if the goods are lost, a prompt is sent by the prompt module.
According to the invention, differential characteristics of a carriage image and a basic image acquired in real time are extracted to obtain a difference image, the difference image is transmitted to the cargo identification model for reasoning, the difference images obtained at adjacent moments are compared, and cargo change information in a carriage is acquired in real time; can realize the real time monitoring to the goods in the carriage, in time discover that the goods reduces etc. unusually, reduce the goods and lose, can remind the driver through reminding the module when detecting the abnormal conditions, the system during operation does not need artifical the participation.
Drawings
Fig. 1 is a flowchart of a cargo identification method according to an embodiment of the present invention.
Fig. 2 is a block diagram of a specific structure of a computer device according to an embodiment of the present invention.
Fig. 3 is a block diagram of a cargo monitoring system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, a cargo identification method according to an embodiment of the present invention includes the following steps: it should be noted that the method is performed for each vehicle, and the cargo identification method of the present invention is not limited to the flow sequence shown in fig. 1 if the result is substantially the same.
S1, obtaining a basic image and a plurality of carriage images in different loading states as sample data, wherein the basic image is an empty carriage image;
s2, performing differential feature extraction on a plurality of carriage images in different loading states and a basic image respectively to obtain a plurality of differential graphs, and taking the plurality of differential graphs as training data;
s3, inputting training data into the convolutional neural network model for training to obtain a cargo identification model;
s4, acquiring a carriage image in real time;
and S5, carrying out differentiation feature extraction on the carriage image and the basic image acquired in real time to obtain a difference map, transmitting the difference map into the cargo recognition model for reasoning, comparing the difference maps obtained at adjacent moments to obtain cargo change information in the carriage in real time.
In an embodiment of the present invention, after obtaining the cargo change information in the compartment in real time, the cargo identification method further includes: and judging whether the goods are lost or not according to the goods change information, and if so, sending a prompt.
In an embodiment of the present invention, the differential feature extraction includes pixel differencing and vector differencing.
In an embodiment of the present invention, when the difference feature extraction adopts a pixel difference, the cargo identification method further includes: and respectively converting the carriage image and the basic image into grayscale images with pixel values between 0 and 255.
In an embodiment of the present invention, the convolutional neural network model is a Resnet model.
An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps of the cargo identification method provided by an embodiment of the present invention.
Fig. 2 is a block diagram showing a specific structure of a computer device according to an embodiment of the present invention, where a computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processors 101 and the memory 102 are connected by a bus, the one or more computer programs are stored in the memory 102 and configured to be executed by the one or more processors 101, and the steps of the cargo identification method as provided by an embodiment of the invention are implemented when the computer programs are executed by the processors 101.
Referring to fig. 3, an embodiment of the present invention provides a cargo monitoring system, including a computer device 100, a camera module 200 connected to the computer device 100, and a reminder module 300, where the camera module 200 collects images and transmits the images to the computer device 100; if the goods are lost, a reminder is issued by the reminder module 300.
According to the embodiment of the invention, differential characteristics of a carriage image and a basic image which are acquired in real time are extracted to obtain a difference image, the difference image is transmitted to the cargo identification model for reasoning, the difference images obtained at adjacent moments are compared, and cargo change information in a carriage is acquired in real time; can realize the real time monitoring to the goods in the carriage, in time discover that the goods reduces etc. unusually, reduce the goods and lose, can remind the driver through reminding the module when detecting the abnormal conditions, the system during operation does not need artifical the participation.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A cargo identification method, characterized in that the method is a step performed for each vehicle, comprising:
acquiring a basic image and a plurality of carriage images in different loading states as sample data, wherein the basic image is an empty carriage image;
respectively carrying out differential feature extraction on a plurality of carriage images in different loading states and a basic image to obtain a plurality of difference graphs, and taking the plurality of difference graphs as training data;
inputting training data into a convolutional neural network model for training to obtain a cargo identification model;
acquiring a carriage image in real time;
and carrying out differentiation feature extraction on the carriage image and the basic image acquired in real time to obtain a difference map, then transmitting the difference map into the cargo identification model for reasoning, comparing the difference maps obtained at adjacent moments to obtain cargo change information in the carriage in real time.
2. The cargo identification method according to claim 1, wherein after the real-time acquisition of the cargo change information in the vehicle compartment, the cargo identification method further comprises: and judging whether the goods are lost or not according to the goods change information, and if so, sending a prompt.
3. The cargo identification method of claim 1, wherein the differential feature extraction includes pixel differencing and vector differencing.
4. The cargo identification method according to claim 3, wherein when the differential feature extraction employs a pixel difference, the cargo identification method further comprises: and respectively converting the carriage image and the basic image into grayscale images with pixel values between 0 and 255.
5. The cargo identification method of claim 1, wherein the convolutional neural network model is a Resnet model.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the item identification method according to any one of claims 1 to 5.
7. A computer device, comprising: one or more processors, a memory and one or more computer programs, the processors and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, characterized in that the steps of the item identification method according to any of claims 1 to 5 are implemented when the computer programs are executed by the processors.
8. A cargo monitoring system, comprising a computer device according to claim 7, a camera module connected to the computer device, and a reminder module, wherein the camera module performs image acquisition and transmits to the computer device; if the goods are lost, a prompt is sent by the prompt module.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110496901.4A CN113361989A (en) | 2021-05-07 | 2021-05-07 | Cargo identification method, computer equipment and cargo monitoring system |
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| CN202110496901.4A CN113361989A (en) | 2021-05-07 | 2021-05-07 | Cargo identification method, computer equipment and cargo monitoring system |
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| CN115776610A (en) * | 2023-02-10 | 2023-03-10 | 北京徐工汉云技术有限公司 | Camera shooting control method and device for cargo monitoring of freight vehicle |
| CN116246233A (en) * | 2023-05-11 | 2023-06-09 | 深圳依时货拉拉科技有限公司 | Vehicle-mounted cargo monitoring method and device, computer equipment and storage medium |
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