CN113838022A - Method, system and device for identifying abnormal working conditions of automobile and storage medium - Google Patents
Method, system and device for identifying abnormal working conditions of automobile and storage medium Download PDFInfo
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Abstract
The application discloses a method, a system and a device for identifying abnormal working conditions of an automobile and a storage medium. The method comprises the steps of obtaining a thermal imaging image and a real image of an automobile; inputting a real image of the automobile into an automobile model prediction model, and determining the type class of the automobile; determining abnormal temperature thresholds corresponding to various parts of the automobile according to the type class of the automobile; inputting the thermal imaging image into a temperature prediction model to obtain a temperature identification result of each part of the automobile; and comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and determining that the operation condition of the automobile is abnormal when the temperature identification result is greater than or equal to the abnormal temperature threshold value. The method can realize early warning of the fire accident of the automobile so as to reduce the occurrence of the condition of the interlinked accident; the fire disaster alarm is beneficial to quickly solving the possible fire disaster and reducing property loss and safety risk. The method can be widely applied to the technical field of automobiles.
Description
Technical Field
The application relates to the technical field of automobiles, in particular to a method, a system and a device for identifying abnormal working conditions of an automobile and a storage medium.
Background
The fire disaster is an accident type which is easy to occur and has great harm, and great property loss is easily caused, so that the fire disaster early detection and emergency treatment are very important works.
With the development of traffic technology, more and more families purchase vehicles as travel tools, and the vehicles provide convenience for daily work and life of people. However, in practical applications, the vehicle may be at risk of fire, especially in new energy vehicles powered by batteries. When a vehicle is on fire, if the targeted rapid emergency treatment is not timely performed, a greater safety accident is likely to be caused. In the related technology, the detection and processing of the fire is mostly post-processing, namely, the alarm and accident processing program is started after the fire occurs, no proper early warning means is provided, the loss is large, the interlinked accidents are very easy to occur, and the safety is low.
In view of the above, there is a need to solve the technical problems in the related art.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the embodiments of the present application is to provide a method for identifying an abnormal operating condition of an automobile, which can identify an abnormal operating condition of the automobile, facilitate early warning of a fire, reduce the occurrence of a linked accident, and reduce property loss and safety risk.
Another object of the embodiments of the present application is to provide a system for identifying an abnormal operating condition of an automobile.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in a first aspect, an embodiment of the present application provides a method for identifying an abnormal operating condition of an automobile, where the method includes the following steps:
acquiring a thermal imaging image and a real image of the automobile;
inputting the real image of the automobile into an automobile model prediction model, and determining the type class of the automobile;
determining abnormal temperature thresholds corresponding to all parts of the automobile according to the type class of the automobile;
inputting the thermal imaging image into a temperature prediction model to obtain a temperature identification result of each part of the automobile;
and comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and determining that the running condition of the automobile is abnormal when the temperature identification result is greater than or equal to the abnormal temperature threshold value.
In addition, according to the method for identifying the abnormal working condition of the automobile in the embodiment of the application, the following additional technical characteristics can be further provided:
further, in an embodiment of the present application, the acquiring a real image of the automobile includes:
photographing the automobile to obtain an original image;
converting the original image into a gray image;
carrying out binarization processing on the gray level image to obtain a black and white image;
carrying out contour segmentation on the black-and-white image according to a preset pixel threshold value to obtain an automobile body area of the automobile;
and drawing the real image from the original image according to the body area.
Further, in an embodiment of the present application, the acquiring a thermal imaging image of the automobile includes:
acquiring a thermal imaging image of the automobile through a dual-spectrum thermal imaging device; the dual-spectrum thermal imaging equipment is arranged at a road junction or a charging pile.
Further, in an embodiment of the present application, before the step of inputting the thermographic image into a temperature prediction model, the method further comprises:
cropping and/or scaling the thermographic image.
Further, in an embodiment of the present application, the determining, according to the type class of the automobile, an abnormal temperature threshold corresponding to each component of the automobile includes:
according to the type of the automobile, accessing and acquiring production information of the automobile;
and determining abnormal temperature thresholds corresponding to all parts of the automobile according to the production information.
Further, in one embodiment of the present application, the abnormal temperature threshold includes a plurality of different levels;
when the temperature identification result is greater than or equal to the abnormal temperature threshold value, determining that the operation condition of the automobile is abnormal, including:
and when the temperature identification result is greater than or equal to the abnormal temperature threshold, determining the abnormal level of the running working condition of the automobile according to the level corresponding to the abnormal temperature threshold.
Further, in an embodiment of the present application, before the step of inputting the thermal imaging image into a temperature prediction model to obtain the temperature identification result of each component of the automobile, the method further includes:
and carrying out region division on the thermal imaging image according to the real image of the automobile, and determining the corresponding position of each part of the automobile in the thermal imaging image.
In a second aspect, an embodiment of the present application provides an abnormal operating condition recognition system for an automobile, where the system includes:
the acquisition module is used for acquiring a thermal imaging image and a real image of the automobile;
the input module is used for inputting the real image of the automobile into an automobile type prediction model and determining the type class of the automobile;
the processing module is used for determining abnormal temperature thresholds corresponding to all parts of the automobile according to the type class of the automobile;
the prediction module is used for inputting the thermal imaging image into a temperature prediction model to obtain a temperature recognition result of each part of the automobile;
and the comparison module is used for comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and when the temperature identification result is greater than or equal to the abnormal temperature threshold value, determining that the running working condition of the automobile is abnormal.
In a third aspect, an embodiment of the present application provides an apparatus for identifying an abnormal operating condition of an automobile, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one program causes the at least one processor to implement the method for identifying an abnormality in an operating condition of a vehicle according to the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, the method for identifying an abnormal operating condition of a vehicle according to the first aspect is implemented.
Advantages and benefits of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application:
according to the method for identifying the abnormal working condition of the automobile, the thermal imaging image and the real image of the automobile are obtained; inputting the real image of the automobile into an automobile model prediction model, and determining the type class of the automobile; determining abnormal temperature thresholds corresponding to all parts of the automobile according to the type class of the automobile; inputting the thermal imaging image into a temperature prediction model to obtain a temperature identification result of each part of the automobile; and comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and determining that the running condition of the automobile is abnormal when the temperature identification result is greater than or equal to the abnormal temperature threshold value. The method can realize early warning of the fire accident of the automobile so as to reduce the occurrence of the condition of the interlinked accident; the fire disaster alarm is beneficial to quickly solving the possible fire disaster and reducing property loss and safety risk.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a specific embodiment of a method for identifying an abnormal condition of an automobile according to the present application;
FIG. 2 is a schematic structural diagram of an embodiment of an abnormal vehicle condition recognition system according to the present application;
fig. 3 is a schematic structural diagram of a specific embodiment of the device for identifying the abnormal operating condition of the vehicle according to the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the automotive field, particularly emerging electric vehicles, accidents such as spontaneous combustion and fire are prone to occur. And once an accident occurs, the accident is difficult to process and easily causes a chain accident. For example, after a vehicle on a road suddenly catches a fire accident, a road management department and a fire department need to be notified as soon as possible, so that on one hand, the vehicle which needs to drive into the road subsequently is notified to reduce the occurrence of a interlinked accident; on the other hand, the fire is quickly put out, and property loss and safety risk are reduced. Of course, the above implementation scenario is only used to illustrate the application of the embodiment of the present application, and is not meant to limit the actual application of the method for identifying the abnormal operating condition of the vehicle in the present application. For example, in other embodiments, the operating condition of the vehicle being charged may be monitored to reduce the occurrence of fire during charging.
In view of the technical problems in the related art, the present application provides a method for identifying an abnormal operating condition of an automobile, and the method in the present application may be applied to a terminal device, and specifically may be implemented by being stored in a memory of the terminal device in the form of a program code and executed by a related processor. The terminal device may be any one of a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, and the like, but is not limited thereto.
Referring to fig. 1, the method in the present application mainly includes the following steps:
in this step, the real image of the automobile is obtained, which may be obtained by directly photographing the automobile by using a related camera device. For example, in some embodiments, a digital video camera may be used to capture an automobile, and then the captured image is sent to the terminal device through wired or wireless transmission, so that the terminal device obtains a real image of the automobile. Specifically, in the embodiment of the present application, the collection positions of the thermal imaging image and the real image of the automobile can be flexibly set according to needs, for example, when the thermal imaging image of the automobile is collected, the thermal imaging image and the real image can be collected by a dual-spectrum thermal imaging device. The dual spectrum thermal imaging device may include a camera for displaying according to infrared rays emitted from the object to be received, and the temperature distribution of the surface of the object to be measured is displayed by a colored picture, thereby facilitating finding out an abnormal point of temperature according to a slight difference in temperature. The double-spectrum thermal imaging equipment solves the problem that a plurality of visible light equipment cannot solve through the characteristics of super-strong environmental adaptability and super-long detection distance, and shows a new visual field for users. Moreover, due to the temperature-sensitive characteristic of the dual-spectrum thermal imaging device, when a certain object in a picture exceeds a set temperature threshold, an abnormality is easily observed from the image. In some embodiments, a dual-spectrum thermal imaging device may be disposed at a road junction; in other embodiments, a dual-spectrum thermal imaging device may also be disposed at the charging post.
Generally, since the photographed image may include other contents unrelated to the automobile, such as the road or road signs, some pre-processing may be performed on the photographed image to obtain the real image of the automobile. Specifically, in the process of processing and acquiring the real image: first, a car may be photographed, an obtained image may be recorded as an original image, and then an image portion of the car may be obtained by segmenting from the original image, that is, the original image may be segmented, so as to obtain a real image of the car.
The process of the segmentation process can be expressed as: and converting the original image into a gray image, and then carrying out binarization processing on the gray image to obtain a black-and-white image. Specifically, when the binarization processing is performed, a gray threshold may be set, and the gray threshold may be set to 100 assuming that the gray value of a pixel in the gray image is between 0 and 255. Therefore, the pixel points with the gray value higher than 100 can be processed into black, and the pixel points with the gray value lower than 100 can be processed into white, so that the whole image presents an obvious black-and-white effect, and a black-and-white image is obtained. The black-and-white image can obviously highlight the outline of the target to be obtained, namely the outline of the automobile, the area of the automobile in the original image can be conveniently drawn based on the outline, and then the image of the corresponding area can be drawn from the original image, so that the real image of the automobile can be obtained.
Specifically, in the present application, the binarization processing for the grayscale image may be implemented by using a correlation function in OpenCV or Matlab. In some embodiments, the automobile part in the original image may also be obtained by segmentation using an edge detection method, the specific means is to implement segmentation by identifying points in the image where gray changes are obvious, and common algorithms may include a Sobel algorithm, a Canny algorithm, a Laplacian algorithm, and the like. When the above process is implemented, in order to eliminate unstable factors caused by the shooting environment as much as possible, it is better to unify the shooting background environment when shooting and taking images, for example, the position and angle of the image capturing device from the automobile can be fixed. Furthermore, the thermal imaging image and real image acquisition devices can be integrated into one device, so that the thermal imaging image and the real image which completely correspond to each other are obtained. For the acquired real image and the thermal imaging image of the car with different sizes, the real image and the thermal imaging image can be cut and/or scaled to be scaled to a scale suitable for subsequent processing.
In addition, in the embodiment of the application, when the automobile is identified, the real image of the automobile can be further divided, that is, the corresponding position of each part in the automobile can be determined, and the marking is performed in the thermal imaging image, so that the temperature of each part of the automobile can be identified in the following process conveniently. Here, the division of the position region of the automobile component may be performed according to an actual automobile structure, and a related image segmentation algorithm may also be employed, which is not described herein again.
in the embodiment of the application, the obtained real image can be input into an automobile type prediction model to obtain an automobile type class recognition result of an automobile. In the embodiment of the application, the vehicle type of the vehicle can be classified according to the power of the vehicle, such as a gasoline power type vehicle, a hybrid power type vehicle, a pure electric vehicle, and the like; of course, the categories may be classified according to the brand categories of the automobiles, and are not listed here.
Here, the automobile model prediction model may be any machine learning model. Specifically, the automobile model prediction model in the embodiment of the present application may be obtained through training by the following steps: firstly, sample images of automobiles are obtained in batches, automobile type labels corresponding to the sample images are marked, the automobile type labels are used for representing automobile type categories of the automobiles corresponding to the sample images, then the sample images are input into an initialized automobile type prediction model, a prediction result output by the model can be obtained, and the prediction result is used for representing the automobile type categories of the automobiles corresponding to the sample images input into the model, so that the accuracy of automobile type prediction model prediction can be evaluated according to the prediction result and the automobile type labels, and parameters of the model can be updated.
Specifically, for an automobile model prediction model, the accuracy of the prediction result thereof can be measured by a Loss Function (Loss Function), the Loss Function is defined on a single training data and is used for measuring the prediction error of a training data, and specifically, the Loss value of the training data is determined by the model label of the single training data and the prediction result of the model on the training data. In actual training, a training data set has many training data, so a Cost Function (Cost Function) is generally adopted to measure the overall error of the training data set, and the Cost Function is defined on the whole training data set and is used for calculating the average value of prediction errors of all the training data, so that the prediction effect of the model can be measured better. For a general machine learning model, based on the cost function, and a regularization term for measuring the complexity of the model, the regularization term can be used as a training objective function, and based on the objective function, the loss value of the whole training data set can be obtained. There are many kinds of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc. all can be used as the loss function of the machine learning model, and are not described one by one here. In the embodiment of the application, a loss function can be selected from the loss functions to determine the loss value of the training, namely the loss value between the prediction result and the vehicle model label. And updating the parameters of the model by adopting a back propagation algorithm based on the trained loss value, and iterating for several rounds to obtain the trained automobile model prediction model. The specific number of iteration rounds may be preset, or training may be considered complete when the test set meets the accuracy requirement.
The process is that the type of the automobile is directly predicted based on the automobile type prediction model, and the automobile type recognition result of the automobile is obtained. In some embodiments, the type category of the automobile can be determined by comparison, and specifically, a plurality of sample images and type labels corresponding to the sample images can be stored in an automobile type prediction model; the model label is also used for representing the model type of the automobile corresponding to the sample image, and extracting the feature data of the sample image through the automobile model prediction model, where the feature data is a data form used for representing the image, and for example, the feature data of the image can be determined based on the pixel values of each pixel point in the image. In the embodiment of the present application, the data structure of the feature data may be any one of a numerical value, a vector, a matrix, a tensor, or the like, and the feature data corresponding to the sample image is recorded as the second feature data. Then, inputting a real image to be identified into an automobile model prediction model, extracting feature data of the real image to be recorded as first feature data, determining similarity between the real image and the sample image according to the first feature data and the second feature data, for example, calculating similarity between the real image and each sample image based on the first feature data and the second feature data through algorithms such as cosine similarity calculation, pearson correlation coefficient method or Jackside similarity coefficient method, so as to determine a sample image most similar to the real image according to the size of the similarity, and determining the type of the automobile in the real image according to an automobile model label corresponding to the sample image with the maximum similarity.
in this step, after the type category of the automobile is determined, the abnormal temperature threshold corresponding to each component of the automobile can be determined. In particular, the various parts of the automobile here may include the motor part of the automobile and the battery part of the automobile, and these two places have more heat output, and the situation of high temperature is easy to appear, and there is a higher risk of fire. Of course, other components besides the above two components may be included, and the principle of the present application is described herein by taking a battery component and a motor component as examples, and the other components will not be described in detail. For the automobiles of the type of the vehicle, abnormal temperature threshold data corresponding to each component of the automobile can be established in advance, for example, in some embodiments, the safety bearing range of the temperature of the automobile can be determined by testing the automobiles of different types of the vehicle, so that the abnormal temperature threshold data can be established; in some embodiments, the production and test information of the vehicle may be accessed and obtained from the office network of the vehicle, so as to determine the abnormal temperature threshold, for example, if the factory information of the vehicle indicates that the safe operating temperature range of the vehicle battery is-15 degrees to 60 degrees, the abnormal temperature threshold of the vehicle type may be set to 60 degrees. Of course, the above numerical values are merely used to exemplarily describe the principles of the present application scheme, and are not meant to limit the present application. In some embodiments, a plurality of abnormal temperature thresholds may be set, for example, three levels of 50 degrees, 55 degrees and 60 degrees are set, so that the abnormal operating condition of the automobile is determined more finely, and safety precaution is facilitated to be performed in advance.
in the embodiment of the application, for automobiles, automobiles of different types have larger difference in body structures and component structures, so that each type of automobile has different temperature rise conditions and abnormal temperature thresholds. In the embodiment of the application, a temperature prediction model can be trained and used for identifying the temperature of each part of the automobile through the thermal imaging image. Specifically, the temperature prediction model can be trained based on a thermal imaging image of the automobile and a corresponding temperature label, and the detailed training principle and process are similar to those of the automobile model prediction model, and are not repeated here.
After the temperature prediction model is obtained through training, the thermal imaging image of the automobile can be input into the temperature prediction model, and the recognition result output by the temperature prediction model is obtained, so that the temperature condition of each part of the automobile can be accurately determined.
And 150, comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and determining that the running condition of the automobile is abnormal when the temperature identification result is greater than or equal to the abnormal temperature threshold value.
In the embodiment of the application, the thermal imaging image is input into the temperature prediction model, and the model can determine the temperature recognition result of each part of the automobile according to the thermal imaging image. Then, comparing the temperature identification result with an abnormal temperature threshold, wherein the abnormal temperature threshold is a higher temperature for maintaining the safety of the automobile, when the temperature identification result is greater than or equal to the abnormal temperature threshold, the current working condition of the automobile is relatively dangerous, and the identification result can be determined as that the running working condition of the automobile is abnormal; on the contrary, when the temperature identification result is smaller than the abnormal temperature threshold value, the current state of the automobile is still in a safe state, the fire risk is not caused temporarily, and the identification result can be determined that the running condition abnormality of the automobile does not exist. In some embodiments, the abnormal level of the operation condition of the automobile can be determined according to the level corresponding to the abnormal temperature threshold, so that the safety risk of the automobile can be judged in a refined manner.
In the embodiment of the application, when the abnormal operation condition of the automobile is determined according to the identification result, the early warning signal can be triggered. Specifically, in some possible implementation modes, the audible and visual alarm signal may be triggered to remind the owner of the vehicle or the manager of the road gate, so as to inform people that the vehicle may have a potential safety hazard of an excessively high temperature, and the vehicle should not continue to run and needs to be safely checked, so as to improve the safety of the vehicle.
The following describes an automobile working condition abnormality recognition system according to an embodiment of the application in detail with reference to the accompanying drawings.
Referring to fig. 2, the system for identifying an abnormal operating condition of an automobile provided in the embodiment of the present application includes:
an obtaining module 101, configured to obtain a thermal imaging image and a real image of an automobile;
the input module 102 is configured to input a real image of the automobile into an automobile model prediction model, and determine a model category of the automobile;
the processing module 103 is configured to determine, according to a type category of the automobile, an abnormal temperature threshold corresponding to each component of the automobile;
the prediction module 104 is configured to input the thermal imaging image into a temperature prediction model to obtain a temperature identification result of each component of the automobile;
and the comparison module 105 is used for comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and when the temperature identification result is greater than or equal to the abnormal temperature threshold value, determining that the operation condition of the automobile is abnormal.
It is to be understood that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
Referring to fig. 3, the embodiment of the present application provides an abnormal operating condition recognition apparatus for an automobile, including:
at least one processor 201;
at least one memory 202 for storing at least one program;
when the at least one program is executed by the at least one processor 201, the at least one processor 201 is caused to implement a method for identifying an abnormality in an operating condition of the vehicle.
Similarly, the contents in the above method embodiments are all applicable to the embodiment of the device for identifying the abnormal operating condition of the automobile, the functions specifically realized by the embodiment of the device for identifying the abnormal operating condition of the automobile are the same as those in the above method embodiments, and the beneficial effects reached by the embodiment of the method are also the same as those reached by the embodiment of the method.
The embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored, and when the program executable by the processor 201 is executed by the processor 201, the method for identifying an abnormal operating condition of a vehicle is performed.
Similarly, the contents in the above method embodiments are all applicable to the computer-readable storage medium embodiments, the functions specifically implemented by the computer-readable storage medium embodiments are the same as those in the above method embodiments, and the beneficial effects achieved by the computer-readable storage medium embodiments are also the same as those achieved by the above method embodiments.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion regarding the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the present application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the application, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
While the present application has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An automobile working condition abnormity identification method is characterized by comprising the following steps:
acquiring a thermal imaging image and a real image of the automobile;
inputting the real image of the automobile into an automobile model prediction model, and determining the type class of the automobile;
determining abnormal temperature thresholds corresponding to all parts of the automobile according to the type class of the automobile;
inputting the thermal imaging image into a temperature prediction model to obtain a temperature identification result of each part of the automobile;
and comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and determining that the running condition of the automobile is abnormal when the temperature identification result is greater than or equal to the abnormal temperature threshold value.
2. The method for identifying the abnormal working condition of the automobile as claimed in claim 1, wherein the step of obtaining the real image of the automobile comprises the following steps:
photographing the automobile to obtain an original image;
converting the original image into a gray image;
carrying out binarization processing on the gray level image to obtain a black and white image;
carrying out contour segmentation on the black-and-white image according to a preset pixel threshold value to obtain an automobile body area of the automobile;
and drawing the real image from the original image according to the body area.
3. The method for identifying the abnormal working condition of the automobile as claimed in claim 1, wherein the step of obtaining the thermal imaging image of the automobile comprises the following steps:
acquiring a thermal imaging image of the automobile through a dual-spectrum thermal imaging device; the dual-spectrum thermal imaging equipment is arranged at a road junction or a charging pile.
4. The method for identifying an abnormality in an operating condition of an automobile according to claim 3, wherein said step of inputting said thermographic image into a temperature prediction model is preceded by the step of:
cropping and/or scaling the thermographic image.
5. The method for identifying the abnormal working condition of the automobile as claimed in any one of claims 1 to 4, wherein the step of determining the abnormal temperature threshold value corresponding to each part of the automobile according to the type class of the automobile comprises the following steps:
according to the type of the automobile, accessing and acquiring production information of the automobile;
and determining abnormal temperature thresholds corresponding to all parts of the automobile according to the production information.
6. The method for identifying an abnormality in an operating condition of a vehicle according to claim 5, wherein said abnormal temperature threshold includes a plurality of different levels;
when the temperature identification result is greater than or equal to the abnormal temperature threshold value, determining that the operation condition of the automobile is abnormal, including:
and when the temperature identification result is greater than or equal to the abnormal temperature threshold, determining the abnormal level of the running working condition of the automobile according to the level corresponding to the abnormal temperature threshold.
7. The method for identifying the abnormal working condition of the automobile as claimed in claim 1, wherein before the step of inputting the thermal imaging image into a temperature prediction model to obtain the temperature identification results of the components of the automobile, the method further comprises:
and carrying out region division on the thermal imaging image according to the real image of the automobile, and determining the corresponding position of each part of the automobile in the thermal imaging image.
8. An automobile working condition abnormity identification system is characterized by comprising:
the acquisition module is used for acquiring a thermal imaging image and a real image of the automobile;
the input module is used for inputting the real image of the automobile into an automobile type prediction model and determining the type class of the automobile;
the processing module is used for determining abnormal temperature thresholds corresponding to all parts of the automobile according to the type class of the automobile;
the prediction module is used for inputting the thermal imaging image into a temperature prediction model to obtain a temperature recognition result of each part of the automobile;
and the comparison module is used for comparing the temperature identification result of each part of the automobile with the abnormal temperature threshold value, and when the temperature identification result is greater than or equal to the abnormal temperature threshold value, determining that the running working condition of the automobile is abnormal.
9. An unusual recognition device of car operating mode characterized by includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the method for identifying the abnormality in the operating condition of the vehicle according to any one of claims 1 to 7.
10. A computer-readable storage medium in which a program executable by a processor is stored, characterized in that: the processor-executable program is configured to implement the method for identifying an abnormality in an operating condition of a vehicle according to any one of claims 1 to 7 when executed by the processor.
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| CN202111104949.2A CN113838022A (en) | 2021-09-22 | 2021-09-22 | Method, system and device for identifying abnormal working conditions of automobile and storage medium |
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| CN202111104949.2A CN113838022A (en) | 2021-09-22 | 2021-09-22 | Method, system and device for identifying abnormal working conditions of automobile and storage medium |
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