CN116958130B - Vehicle detection system and method based on machine learning - Google Patents
Vehicle detection system and method based on machine learning Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
Abstract
The application discloses a vehicle detection system and method based on machine learning, and relates to the technical field of vehicle detection. The detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model. The detection method is suitable for the detection system. According to the method and the device, the first characteristic data, the second characteristic data and the third characteristic data are used as training data for machine learning, redundant data which are generated conventionally are removed, meanwhile, the fact that data possibly causing abnormal vehicles can be trained is guaranteed, and then accuracy and reliability of vehicle detection results are guaranteed.
Description
Technical Field
The application relates to the technical field of vehicle detection, in particular to a vehicle detection system and method based on machine learning.
Background
In the related art, a manual inspection method is generally used for inspecting safety of a vehicle, and commonly used functions of the vehicle, such as fuel, coolant, brake fluid, electric quantity, rearview mirror, etc., are inspected. However, during safety inspection of a vehicle, some functions may be omitted, such as inspection and maintenance of tires, so that a user cannot make a correct judgment on the overall health of the vehicle in time. In contrast, in the vehicle detection performed by the intelligent detection technique, for example, the vehicle detection performed by the machine learning, in the machine learning, when the number of training data is large, the learning accuracy becomes high. As a method of adding training data, however, if all of the data collected from various devices is used for machine learning, a decrease in learning accuracy may be incurred instead. Therefore, by optimizing the training data in the machine learning process, the accuracy of the vehicle detection result can be improved.
Disclosure of Invention
An objective of the present application is to provide a vehicle detection system and method based on machine learning, so as to solve the technical problem set forth in the background art.
In order to achieve the above purpose, the present application discloses the following technical solutions:
in a first aspect, the present application discloses a machine learning based vehicle detection system comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; and after the model training module completes the model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule.
Preferably, the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the first characteristic numberAccording to the corresponding characteristic data.
Preferably, the method further comprises: and the data acquisition module is used for acquiring the second characteristic data and the third characteristic data.
Preferably, the second feature data and the third feature data are obtained by averaging at least two instant data acquired by the data acquisition module at the same time.
Preferably, when the ratio of the instant data M to the instant data N exceeds a preset threshold ρ when the instant data is averaged, the instant data M and the instant data N are respectively used as the corresponding second feature data or the third feature data.
Preferably, the model training module performs iterative training based on the first feature data, and the step of obtaining a machine learning model after training includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
Preferably, the vehicle detection module inputs the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and takes the output result as a vehicle detection result.
In a second aspect, the present application discloses a machine learning based vehicle detection method comprising a model building section and a vehicle detection section;
the model building section includes the steps of:
generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
training through the first characteristic data to obtain a machine learning model;
acquiring second characteristic data associated with the first characteristic data;
acquiring third characteristic data which meets a difference rule with the first characteristic data;
performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after finishing machine learning, wherein the model updating comprises supplementing second feature data and/or third feature data used in the model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
the vehicle detecting section includes the steps of:
and detecting the instant data acquired on the vehicle based on the machine learning model, and acquiring a vehicle detection result.
Preferably, the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
Preferably, the step of obtaining the machine learning model includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain a machine learning model;
and, the vehicle detecting portion specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
The beneficial effects are that: according to the vehicle detection system and method based on machine learning, through the first feature data, the second feature data and the third feature data serving as training data of machine learning, redundant data which occur conventionally are removed, meanwhile, the fact that data possibly causing vehicle abnormality can be trained is ensured, and further accuracy and reliability of vehicle detection results are ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a machine learning based vehicle detection system in an embodiment of the present application;
fig. 2 is a flow chart of a vehicle detection method based on machine learning in an embodiment of the present application.
Description of the embodiments
The following description of the technical solutions in the embodiments of the present application will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The present embodiment discloses a vehicle detection system based on machine learning as shown in fig. 1, comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; and after the model training module completes the model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule. It is possible that the first characteristic data is vehicle data obtained by big data and/or vehicle data obtained by a vehicle delivery/maintenance manual, such data generally enabling normal data/data range of the vehicle; the second characteristic data is data of detected normal operation of the vehicle, and the third characteristic data is data generated when the vehicle is abnormal.
As a possible implementation manner of this embodiment, the feature difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the special corresponding to the third characteristic dataAnd the feature data A2 is the feature data corresponding to the first feature data.
In this embodiment, the result difference rule may be a number of hardware structural differences obtained through image acquisition and sensor acquisition, and may be, but not limited to, detection result data corresponding to breakage of a vehicle body component, loosening of a connecting member, and the like.
As a preferred implementation manner of the present embodiment, the vehicle detection system based on machine learning further includes: and the data acquisition module is used for acquiring the second characteristic data and the third characteristic data. And the second characteristic data and the third characteristic data are obtained by averaging at least two instant data acquired at the same time by the data acquisition module. The average may be an average calculation method between numerical values, or may be a result expressed as a compromise of the result expression according to the result difference rule. Further, for the average value calculation mode, when the ratio of the instant data M to the instant data N exceeds the preset threshold ρ during the average of the instant data, the instant data M and the instant data N are respectively used as the corresponding second characteristic data or the third characteristic data, so that the data with larger difference can be ensured not to be removed, and the accuracy of the final detection result can be ensured.
As a possible implementation manner of this embodiment, the step of performing iterative training by the model training module based on the first feature data, and obtaining a machine learning model after training includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
Further, the vehicle detection module inputs the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and the output result is used as a vehicle detection result.
The present embodiment discloses in a second aspect a machine learning-based vehicle detection method as shown in fig. 2, which includes a model building section and a vehicle detection section.
Specifically, the model building section includes the steps of:
s101, generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
s102, training through first characteristic data to obtain a machine learning model;
s103, acquiring second characteristic data associated with the first characteristic data;
s104, obtaining third characteristic data which meets a difference rule with the first characteristic data;
s105, performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after the machine learning is completed, wherein the model updating comprises supplementing second feature data and/or third feature data used in a model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
specifically, the vehicle detecting section includes the steps of:
s201-detecting the instant data acquired on the vehicle based on the machine learning model, and acquiring a vehicle detection result.
It should be noted that the vehicle detection method based on machine learning disclosed in the present embodiment is applicable to the vehicle detection system based on machine learning described above.
Thus, the feature difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
The step of obtaining the machine learning model includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain a machine learning model;
and, the vehicle detecting portion specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
By virtue of the above, the specific contents of other steps in the method can be compared with the functional descriptions of each module in the vehicle detection system based on machine learning, and the description is omitted herein.
In summary, according to the vehicle detection system and method based on machine learning of the present embodiment, the first feature data, the second feature data and the third feature data are used as training data for machine learning, so that redundant data conventionally occurring are removed, and meanwhile, data possibly causing abnormal vehicle is ensured to be trained, so that accuracy and reliability of a vehicle detection result are ensured.
In the embodiments provided herein, it should be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For a hardware implementation, the processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the flow of an embodiment may be accomplished by a computer program to instruct the associated hardware. When implemented, the above-described programs may be stored in or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. The computer-readable storage media may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present application, and although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof, and any modifications, equivalents, improvements or changes that fall within the spirit and principles of the present application are intended to be included in the scope of protection of the present application.
Claims (4)
1. A machine learning based vehicle detection system, comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; after the model training module completes a model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule;
the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data;
the second characteristic data are data of detected normal running of the vehicle; the result difference rule is a hardware structure difference acquired through image acquisition and sensor acquisition;
the machine learning-based vehicle detection system further includes: the data acquisition module is used for acquiring the second characteristic data and the third characteristic data; the second characteristic data and the third characteristic data are obtained by averaging at least two instant data acquired at the same time by the data acquisition module; when the ratio of the instant data M to the instant data N exceeds a preset threshold value rho during the average of the instant data, respectively taking the instant data M and the instant data N as the corresponding second characteristic data or the third characteristic data;
the third characteristic data is data generated when the vehicle is abnormal;
the model training module carries out iterative training based on the first characteristic data, and the step of obtaining a machine learning model after training comprises the following steps: acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data; generating a target data set from the vehicle data and data described in a factory/maintenance manual; and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
2. The machine learning based vehicle detection system of claim 1, wherein the vehicle detection module inputs instant data collected on the vehicle into the machine learning model to obtain an output result of the machine learning model, and uses the output result as a vehicle detection result.
3. A vehicle detection method based on machine learning, characterized in that the method comprises a model construction part and a vehicle detection part;
the model building section includes the steps of:
generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
training through the first characteristic data to obtain a machine learning model;
acquiring second characteristic data associated with the first characteristic data;
acquiring third characteristic data which meets a difference rule with the first characteristic data;
performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after finishing machine learning, wherein the model updating comprises supplementing second feature data and/or third feature data used in the model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
the vehicle detecting section includes the steps of:
detecting instant data acquired from a vehicle based on the machine learning model, and acquiring a vehicle detection result;
the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data;
the second characteristic data are data of detected normal running of the vehicle; the result difference rule is a hardware structure difference acquired through image acquisition and sensor acquisition;
the machine learning-based vehicle detection system further includes: the data acquisition module is used for acquiring the second characteristic data and the third characteristic data; the second characteristic data and the third characteristic data are obtained by averaging at least two instant data acquired at the same time by the data acquisition module; when the ratio of the instant data M to the instant data N exceeds a preset threshold value rho during the average of the instant data, respectively taking the instant data M and the instant data N as the corresponding second characteristic data or the third characteristic data;
the step of obtaining the machine learning model includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
4. The machine learning based vehicle detection method according to claim 3, characterized in that the vehicle detection section specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
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| CN115169580A (en) * | 2022-06-30 | 2022-10-11 | 章鱼博士智能技术(上海)有限公司 | Training method and device for voltage difference abnormity detection model of vehicle battery pack |
| CN116047164A (en) * | 2022-12-30 | 2023-05-02 | 章鱼博士智能技术(上海)有限公司 | Detection method and detection device for insulation resistance abnormality of electric automobile |
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