Car insurance premium evaluation method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the technical field of premium evaluation, and provides a vehicle insurance premium evaluation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a driving image of a driver based on a preset in-vehicle camera; performing safety belt detection processing on the driver driving image to obtain safety belt detection information; performing head recognition processing on the driving image of the driver to obtain head recognition information; performing composite emotion recognition processing on the driver driving image to obtain composite emotion recognition information; performing mobile phone detection processing on the driving image of the driver to obtain mobile phone detection information; performing score calculation processing according to the safety belt detection information, the head recognition information, the emotion recognition information, the mobile phone detection information and a preset weight score value set to obtain a driving behavior score value; and determining a vehicle insurance premium value according to the driving behavior score value and a preset premium score table. According to the technical scheme, the evaluation of the vehicle insurance premium can be more reasonable.

Description

Technical Field

[0001] The embodiments of the present application relate to but are not limited to the field of premium assessment technology, and in particular to a method, device, electronic device and computer-readable storage medium for assessing automobile insurance premiums. Background Art

[0002] At present, the penetration rate of cars in urban households is getting higher and higher. With the innovation and development of the automotive industry, car cameras have also developed rapidly. Cameras are increasingly widely used in the automotive field, mainly focusing on reversing images, 360 panoramic images, driving recorders and automatic driving; with the development of the industry, more and more vehicles are also equipped with in-car cameras. The popularity of cars has also given rise to car insurance. In the car insurance industry, the annual premium is only determined by whether there was an accident in the previous year. The judgment standard is single and not rational enough. Summary of the invention

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] In order to solve the problems mentioned in the above background technology, the embodiments of the present application provide a method, device, electronic device and computer-readable storage medium for evaluating automobile insurance premiums, which can make the evaluation of automobile insurance premiums more reasonable.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating a motor vehicle insurance premium, the method comprising:

[0006] Acquire the driver's driving image based on a preset in-vehicle camera;

[0007] Performing seat belt detection processing on the driver's driving image to obtain seat belt detection information; and performing head recognition processing on the driver's driving image to obtain head recognition information; and performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; and performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information;

[0008] Performing scoring calculation processing according to the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value;

[0009] The motor vehicle insurance premium value is determined according to the driving behavior score value and a preset premium score table.

[0010] According to some embodiments of the present application, performing seat belt detection processing on the driver driving image to obtain seat belt detection information includes:

[0011] Performing a first preprocessing on the driver driving image to obtain a first preprocessed image;

[0012] Performing a first feature extraction on the first preprocessed image to obtain a first image feature;

[0013] The first image feature is subjected to seat belt detection and classification processing based on a pre-trained first binary classification model to obtain the seat belt detection information.

[0014] According to some embodiments of the present application, performing head recognition processing on the driver's driving image to obtain head recognition information includes:

[0015] Performing head detection processing on the driver's driving image based on a pre-trained lightweight deep neural network to obtain head detection information;

[0016] In a case where the head detection information indicates that the driver's head is not detected, determining the driver's head lowering information as the head recognition information;

[0017] In the case where the head detection information indicates that the driver's head is detected, face detection processing is performed on the driver's driving image based on a pre-trained face detector to obtain face detection information; and the head recognition information is determined based on the face detection information.

[0018] According to some embodiments of the present application, determining the head recognition information according to the face detection information includes:

[0019] In a case where the facial detection information indicates that a complete face of the driver is detected, determining the driver's non-head-down information as the head recognition information;

[0020] In a case where the facial detection information indicates that the complete face of the driver is not detected, information that the driver lowers his head is determined as the head recognition information.

[0021] According to some embodiments of the present application, the performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information includes:

[0022] Performing face detection processing on the driver's driving image to obtain a driver's face image;

[0023] Extracting facial features from the driver's facial image to obtain facial features of the driver;

[0024] Performing key point location extraction processing on the driver's facial features to obtain facial key point features;

[0025] Identify and classify the key features of the face to obtain multiple expression information;

[0026] The plurality of expression information are matched with a preset basic expression combination to obtain the composite emotion recognition information.

[0027] According to some embodiments of the present application, performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information includes:

[0028] Performing a second preprocessing on the driver driving image to obtain a second preprocessed image;

[0029] Performing second feature extraction on the second preprocessed image to obtain second image features;

[0030] Perform mobile phone detection on the second image feature based on a pre-trained second binary classification model to obtain the mobile phone detection information.

[0031] According to some embodiments of the present application, the scoring calculation process is performed based on the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted score value set to obtain a driving behavior score value, including:

[0032] Selecting corresponding weighted scoring values ​​from the weighted scoring value set according to the seat belt detection information, the head recognition information, the composite emotion recognition information and the mobile phone detection information;

[0033] The seat belt detection information, the head recognition information, the composite emotion recognition information and the mobile phone detection information are respectively weighted and summed with the corresponding weighted scoring values ​​to obtain the driving behavior scoring value.

[0034] In a second aspect, the embodiment of the present application further provides a vehicle insurance premium assessment device, comprising:

[0035] A first processing module, used for acquiring a driving image of a driver based on a preset in-vehicle camera;

[0036] The second processing module is used to perform seat belt detection processing on the driver's driving image to obtain seat belt detection information; and perform head recognition processing on the driver's driving image to obtain head recognition information; and perform composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; and perform mobile phone detection processing on the driver's driving image to obtain mobile phone detection information;

[0037] A third processing module is used to perform scoring calculation processing based on the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value;

[0038] The fourth processing module is used to determine the car insurance premium value according to the driving behavior score value and a preset premium score table.

[0039] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for evaluating motor vehicle insurance premiums as described in the first aspect above is implemented.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the vehicle insurance premium assessment method as described in the first aspect above.

[0041] The method for evaluating automobile insurance premium according to the embodiment provided by the present application has at least the following beneficial effects: in the process of evaluating automobile insurance premium, firstly, a driving image of the driver is acquired based on a preset in-car camera; then, the driver's driving image is subjected to seat belt detection processing to obtain seat belt detection information; and, the driver's driving image is subjected to head recognition processing to obtain head recognition information; and, the driver's driving image is subjected to composite emotion recognition processing to obtain composite emotion recognition information; and, the driver's driving image is subjected to mobile phone detection processing to obtain mobile phone detection information; and then, a driving behavior score value is obtained by performing score calculation processing based on the seat belt detection information, head recognition information, composite emotion recognition information, mobile phone detection information and a preset weight score value set; and finally, the automobile insurance premium value is determined based on the driving behavior score value and a preset premium score table; through the above technical scheme, the driver's driving behavior is scored, and the automobile insurance premium is evaluated based on the score, so that the determination of the automobile insurance premium can be more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0043] Figure 1 is a flow chart of a method for evaluating automobile insurance premiums provided by an embodiment of the present application;

[0044] Figure 2 This is a specific flow chart of performing seat belt detection on a driver's driving image provided by an embodiment of the present application;

[0045] Figure 3 This is a specific flow chart of performing head recognition on a driver's driving image provided by an embodiment of the present application;

[0046] Figure 4This is a specific flow chart for determining head identification information provided by an embodiment of the present application;

[0047] Figure 5 This is a specific flow chart of performing composite emotion recognition on a driver's driving image provided by an embodiment of the present application;

[0048] Figure 6 This is a specific flow chart of performing mobile phone detection on a driver's driving image provided by an embodiment of the present application;

[0049] Figure 7 is a specific flow chart of step S300 provided in one embodiment of the present application;

[0050] Figure 8 is a schematic diagram of a vehicle insurance premium assessment device provided by an embodiment of the present application;

[0051] Fig. 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0054] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0055] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0056] AI is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0057] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0058] Artificial intelligence is AI. AI is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0059] The servers involved in artificial intelligence technology can be independent servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms.

[0060] The present application provides a method, device, electronic device and computer-readable storage medium for evaluating automobile insurance premiums. In the process of evaluating automobile insurance premiums, first, a driving image of a driver is acquired based on a preset in-car camera; then, a seat belt detection process is performed on the driver's driving image to obtain seat belt detection information; and, head recognition process is performed on the driver's driving image to obtain head recognition information; and, composite emotion recognition process is performed on the driver's driving image to obtain composite emotion recognition information; and, mobile phone detection process is performed on the driver's driving image to obtain mobile phone detection information; then, a scoring calculation process is performed based on the seat belt detection information, head recognition information, composite emotion recognition information, mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value; finally, the automobile insurance premium value is determined based on the driving behavior scoring value and a preset premium scoring table; through the above technical solution, the driver's driving behavior is scored, and the automobile insurance premium is evaluated based on the score, so that the determination of the automobile insurance premium can be more reasonable.

[0061] The motor vehicle insurance premium assessment method provided in the embodiment of the present application relates to the field of premium assessment technology. The motor vehicle insurance premium assessment method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the motor vehicle insurance premium assessment method, etc., but is not limited to the above forms.

[0062] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0063] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0064] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0065] like Figure 1 As shown, Figure 1 It is a flowchart of a method for evaluating automobile insurance premiums provided by an embodiment of the present application. The method for evaluating automobile insurance premiums includes but is not limited to steps S100 to S400.

[0066] Step S100, obtaining a driving image of the driver based on a preset in-vehicle camera;

[0067] Step S200, performing seat belt detection processing on the driver's driving image to obtain seat belt detection information; and performing head recognition processing on the driver's driving image to obtain head recognition information; and performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; and performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information;

[0068] Step S300, performing score calculation processing based on the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted score value set to obtain a driving behavior score value;

[0069] Step S400, determining the car insurance premium value according to the driving behavior score value and a preset premium score table.

[0070] It should be noted that in the process of evaluating the motor vehicle insurance premium, first, the driver's driving image is obtained based on the preset in-vehicle camera; then, the driver's driving image is subjected to seat belt detection processing to obtain seat belt detection information; and, the driver's driving image is subjected to head recognition processing to obtain head recognition information; and, the driver's driving image is subjected to composite emotion recognition processing to obtain composite emotion recognition information; and, the driver's driving image is subjected to mobile phone detection processing to obtain mobile phone detection information; then, a scoring calculation is performed based on the seat belt detection information, head recognition information, composite emotion recognition information, mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value; finally, the motor vehicle insurance premium value is determined based on the driving behavior scoring value and a preset premium scoring table; through the above technical scheme, the driver's driving behavior is scored, and the motor vehicle insurance premium is evaluated based on the score, so that the determination of the motor vehicle insurance premium can be more reasonable.

[0071] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining the driver's driving image, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or jump to a confirmation page. After the user's separate permission or consent is clearly obtained, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0072] It is worth noting that in the process of evaluating the motor vehicle insurance premium in the embodiment of the present application, after obtaining the driver's driving image, seat belt detection processing, head recognition processing, complex emotion recognition processing and mobile phone detection processing can be performed based on the driver's driving image; then, the driver's driving can be scored based on the above four aspects of detection processing, and the corresponding motor vehicle insurance premium value can be determined based on the calculated driving behavior score value; through the above technical solution, premium estimation can be performed based on the driver's driving behavior habits, which can not only make the determination of motor vehicle insurance premiums more reasonable, but also can well urge drivers to drive in a standardized manner.

[0073] It is worth noting that the driver's driving image is obtained based on the in-car camera, and the in-car camera can be a camera equipped by the vehicle itself or a camera installed later, which is not limited here. After obtaining the driver's driving image, the driver's driving image can be recognized and processed. The recognition process includes seat belt recognition, head recognition, composite emotion recognition and mobile phone detection recognition. Through the above four aspects of recognition, the driver's driving habits can be well detected, and then the premium can be evaluated based on the driver's driving habits, making the premium evaluation more reasonable.

[0074] It is worth noting that the seat belt detection processing of the driver's driving image is to detect whether the driver is wearing a seat belt while driving; the head recognition processing of the driver's driving image is to detect whether the driver lowers his head while driving; the composite emotion recognition processing of the driver's driving image is to detect the driver's facial expression and then infer the driver's emotional state; the mobile phone detection processing of the driver's driving image is to detect whether the driver makes or receives a call while driving.

[0075] It is worth noting that seat belt detection information, head recognition information, composite emotion recognition information and mobile phone detection information are all assigned different weighted scoring values; in the subsequent scoring process, the four different information are multiplied by the corresponding weighted scoring values, and then the four products are summed to obtain the driving behavior scoring value; the weighted scoring value can be flexibly set according to actual conditions, thereby making the determination of the driving behavior scoring value more reasonable.

[0076] In some embodiments, Figure 2 As shown, the above-mentioned seat belt detection processing of the driver's driving image to obtain the seat belt detection information may include but is not limited to steps S210 to S230.

[0077] Step S210, performing a first preprocessing on the driver's driving image to obtain a first preprocessed image;

[0078] Step S220, performing first feature extraction on the first preprocessed image to obtain a first image feature;

[0079] Step S230: Perform seat belt detection and classification processing on the first image feature based on the pre-trained first binary classification model to obtain seat belt detection information.

[0080] It should be noted that in the process of performing seat belt detection processing on the driver's driving image to obtain seat belt detection information, the driver's driving image is first preprocessed to obtain a first preprocessed image; then the first image feature can be obtained by performing first feature extraction on the first preprocessed image; subsequently, the first image feature can be subjected to seat belt detection classification processing based on the pre-trained first binary classification model to obtain seat belt detection information. Through the above technical solution, the binary classification model is used to detect and process the seat belt, making the seat belt detection more simple and quick.

[0081] It is worth noting that the first preprocessed image can be obtained by performing a first preprocessing on the driver's driving image, and the first preprocessing of the driver's driving image includes denoising and envelope extraction processing on the driver's driving image; the first binary classification model in the embodiment of the present application has an intuitive structure and interpretability, so that non-technical users can also understand the decision-making process of the model; the prediction of the model can provide insights to help understand the key features in the data and their relationship with the results; the binary classification model can adapt to various types of data, including numerical, categorical and textual data; a good binary classification model can learn from training data and make accurate predictions on new and unseen data; the stability and robustness of the prediction are improved by integrating multiple models; models such as support vector machines and neural networks can capture nonlinear relationships in data and are suitable for complex problems.

[0082] In some embodiments, Figure 3 As shown, the above-mentioned head recognition processing of the driver's driving image to obtain head recognition information may include but is not limited to steps S240 to S260.

[0083] Step S240, performing head detection processing on the driver's driving image based on a pre-trained lightweight deep neural network to obtain head detection information;

[0084] Step S250, when the head detection information indicates that the driver's head is not detected, determining the driver's head lowering information as the head recognition information;

[0085] Step S260, when the head detection information indicates that the driver's head is detected, face detection processing is performed on the driver's driving image based on a pre-trained face detector to obtain face detection information; and head recognition information is determined based on the face detection information.

[0086] It should be noted that in the process of performing head recognition processing on the driver's driving image to obtain head recognition information, the driver's driving image can first be subjected to head detection processing based on a pre-trained lightweight deep neural network to obtain head detection information; when the head detection information indicates that the driver's head is not detected, the driver's head-down information can be determined as head recognition information; when the head detection information indicates that the driver's head is detected, the driver's driving image can be subjected to face processing based on a pre-trained face detector to obtain face detection information, and subsequently the head recognition information can be determined based on the face detection information. In the above manner, whether the driver lowers his head can be well detected and processed, and the detection method is simple, fast and reliable.

[0087] It is worth noting that when the head detection information indicates that the driver's head is not detected, it is determined that the driver is in a state of lowering his head; when the head detection information indicates that the driver's head is detected, the face detector will be used to perform face detection processing on the driver's driving image. When the obtained face detection information indicates that the driver's complete face can be detected, it is determined that the driver is not lowering his head; when the obtained face detection information indicates that the driver's complete face cannot be detected, it is determined that the driver is lowering his head; through the above technical solution, it is possible to more accurately detect and process whether the driver is lowering his head.

[0088] In some embodiments, Figure 4 As shown, the above step S260 may include but is not limited to steps S261 to S262.

[0089] Step S261, when the face detection information indicates that the complete face of the driver is detected, determining the driver's non-head-down information as head recognition information;

[0090] Step S262: When the facial detection information indicates that the complete face of the driver is not detected, the driver's head-lowering information is determined as head recognition information.

[0091] It should be noted that, in the process of determining the head recognition information based on the face detection information, when the face detection information indicates that the driver's complete face is detected, the driver's non-head-down information can be determined as the head recognition information; when the face detection information indicates that the driver's complete face is not detected, the driver's head-down information can be determined as the head recognition information. Through the above technical solution, it is possible to more accurately detect whether the driver is lowering his head, making the detection process more reliable.

[0092] In some embodiments, Figure 5As shown, performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information may include but is not limited to steps S271 to S275.

[0093] Step S271, performing face detection processing on the driver's driving image to obtain the driver's face image;

[0094] Step S272, extracting facial features from the driver's facial image to obtain the driver's facial features;

[0095] Step S273, performing key point location extraction processing on the driver's facial features to obtain facial key point features;

[0096] Step S274, identifying and classifying the key features of the face to obtain a plurality of expression information;

[0097] Step S275, matching the multiple expression information with the preset basic expression combination to obtain composite emotion recognition information.

[0098] It should be noted that in the process of performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information, first, face detection processing is performed on the driver's driving image to obtain the driver's face image; then, facial feature extraction processing is performed on the driver's face image to obtain the driver's facial features; then, key point positioning and extraction processing is performed on the driver's facial features to obtain facial key point features; then, recognition and classification processing is performed on the facial key point features to obtain multiple expression information; finally, multiple expression information is matched with preset basic expression combinations to obtain composite emotion recognition information. Through the above technical scheme, the driver's emotions can be recognized and processed more finely, so that the subsequent scoring processing can be more reasonable and accurate.

[0099] It is worth noting that by matching multiple expression information with preset basic expression combinations, the corresponding composite emotion recognition information can be obtained, so that subsequent emotion recognition can be accurate and the driver's emotional state can be better assessed. It is important for the driver to be emotionally stable. If the driver is in a state of tension or irritability for a long time, accidents are more likely to occur. Composite emotion recognition processing of the driver improves the diversity of emotion recognition and makes the detection of emotions more accurate. For example, if the driver includes expressions of anger and surprise, the corresponding composite emotion recognition information can be obtained through the composite emotion recognition processing of the embodiment of the present application, so that the recognition of emotions can be more detailed and accurate.

[0100] In some embodiments, Figure 6As shown, performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information may include but is not limited to steps S281 to S283.

[0101] Step S281, performing a second preprocessing on the driver's driving image to obtain a second preprocessed image;

[0102] Step S282, performing second feature extraction on the second preprocessed image to obtain a second image feature;

[0103] Step S283: Perform mobile phone detection on the second image feature based on the pre-trained second binary classification model to obtain mobile phone detection information.

[0104] It should be noted that in the process of performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information, firstly, the driver's driving image is subjected to a second preprocessing to obtain a second preprocessed image; then, the second preprocessed image is subjected to a second feature extraction processing to obtain a second image feature; finally, based on the pre-trained second binary classification model, the second image feature is subjected to mobile phone detection processing to obtain the corresponding mobile phone detection information. Through the above technical solution, it is possible to simply and quickly detect whether the driver answers a call.

[0105] In some embodiments, Figure 7 As shown, the above step S300 may include but is not limited to step S310 to step S320.

[0106] Step S310, selecting corresponding weighted score values ​​from a weighted score value set according to the seat belt detection information, the head recognition information, the composite emotion recognition information, and the mobile phone detection information;

[0107] Step S320 , weighted summing the seat belt detection information, the head recognition information, the composite emotion recognition information and the mobile phone detection information with the corresponding weighted score values ​​is performed to obtain a driving behavior score value.

[0108] It should be noted that in the process of scoring and calculating the driving behavior score value based on the seat belt detection information, head recognition information, compound emotion recognition information, mobile phone detection information and a preset weight score value set, first, the corresponding weight score value is selected from the weight score value set according to the seat belt detection information, head recognition information, compound emotion recognition information and mobile phone detection information; finally, the seat belt detection information, head recognition information, compound emotion recognition information and mobile phone detection information are weighted and summed with the corresponding weight score values, and the corresponding driving behavior score value can be obtained, thereby preparing for the subsequent motor insurance premium assessment.

[0109] It is worth noting that different information corresponds to different weighted scoring values. For example, seat belt detection information is more important for driving safety, so the weighted scoring value corresponding to the seat belt detection information can be set to a larger value. During the calculation process, the seat belt detection information indicates that the driver has worn a seat belt, and the calculation value corresponding to the seat belt detection information is "0", while the seat belt detection information indicates that the driver has not worn a seat belt, and the calculation value corresponding to the seat belt detection information is "1".

[0110] In some embodiments of the present application, after obtaining the driving behavior score value, the obtained driving behavior score value can be matched with the pre-set premium score table, so that the motor vehicle insurance premium value corresponding to the driving behavior score value can be determined from the premium score table. Among them, the driving behavior score value in the premium score table can be set to multiple different intervals, and each interval corresponds to a motor vehicle insurance premium value; illustratively, the driving behavior score value in the premium score table is set to 10-20 intervals, 20-30 intervals, 30-40 intervals, etc., and each interval corresponds to a motor vehicle insurance premium value. And when the driving behavior score value belongs to the interval division value, a smaller motor vehicle insurance premium value can be selected as the motor vehicle insurance premium value.

[0111] In some embodiments of the present application, in the process of using the in-vehicle camera to obtain the driver's driving image, the driver's usual driving process can be firstly filmed and processed based on the in-vehicle camera, so as to obtain the driver's video image information, and then multiple video frames are captured from the video image information as the driver's driving image, and then the multiple driver's driving images are scored and processed respectively to obtain multiple driving behavior score values, and finally an average value can be calculated based on the multiple driving behavior score values ​​obtained, and finally the motor vehicle insurance premium value is determined according to the average value, so that the determination of the motor vehicle insurance premium can be more reasonable and accurate.

[0112] In addition, if Figure 8 As shown, an embodiment of the present application further provides a vehicle insurance premium evaluation device 10, comprising:

[0113] The first processing module 100 is used to obtain a driving image of the driver based on a preset in-vehicle camera;

[0114] The second processing module 200 is used to perform seat belt detection processing on the driver's driving image to obtain seat belt detection information; and perform head recognition processing on the driver's driving image to obtain head recognition information; and perform composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; and perform mobile phone detection processing on the driver's driving image to obtain mobile phone detection information;

[0115] The third processing module 300 is used to perform scoring calculation processing based on the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value;

[0116] The fourth processing module 400 is used to determine the car insurance premium value according to the driving behavior score value and a preset premium score table.

[0117] It should be noted that in the process of evaluating the motor vehicle insurance premium, first, the driver's driving image is obtained based on the preset in-vehicle camera; then, the driver's driving image is subjected to seat belt detection processing to obtain seat belt detection information; and, the driver's driving image is subjected to head recognition processing to obtain head recognition information; and, the driver's driving image is subjected to composite emotion recognition processing to obtain composite emotion recognition information; and, the driver's driving image is subjected to mobile phone detection processing to obtain mobile phone detection information; then, a scoring calculation is performed based on the seat belt detection information, head recognition information, composite emotion recognition information, mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value; finally, the motor vehicle insurance premium value is determined based on the driving behavior scoring value and a preset premium scoring table; through the above technical scheme, the driver's driving behavior is scored, and the motor vehicle insurance premium is evaluated based on the score, so that the determination of the motor vehicle insurance premium can be more reasonable.

[0118] The specific implementation of the vehicle insurance premium evaluation device 10 is substantially the same as the specific implementation of the above-mentioned vehicle insurance premium evaluation method, and will not be described in detail herein.

[0119] In addition, if Fig. 9 As shown, an embodiment of the present application further provides an electronic device 700 , which includes: a memory 720 , a processor 710 , and a computer program stored in the memory 720 and executable on the processor 710 .

[0120] The processor 710 and the memory 720 may be connected via a bus or in other ways.

[0121] The non-transitory software programs and instructions required to implement the vehicle insurance premium evaluation methods of the above embodiments are stored in the memory 720 , and when executed by the processor 710 , the vehicle insurance premium evaluation methods of the above embodiments are executed.

[0122] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor 710 or a controller, for example, by a processor 710 in the above-mentioned device embodiment, so that the above-mentioned processor 710 can execute the motor vehicle insurance premium assessment method in the above-mentioned embodiment.

[0124] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0125] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0127] The apparatus, device, computer-readable storage medium and method provided in the embodiments of the present application correspond to each other. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be repeated here.

[0128] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0129] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0130] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0131] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0133] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0137] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0138] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0140] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0141] Embodiments of the present application may be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0142] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0143] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating automobile insurance premium, characterized in that: The method comprises: Acquire the driver's driving image based on a preset in-vehicle camera; Performing seat belt detection processing on the driver's driving image to obtain seat belt detection information; and performing head recognition processing on the driver's driving image to obtain head recognition information; and performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; and performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information; Performing scoring calculation processing according to the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value; The motor vehicle insurance premium value is determined according to the driving behavior score value and a preset premium score table.

2. The method for evaluating automobile insurance premium according to claim 1, characterized in that: The performing seat belt detection processing on the driver driving image to obtain seat belt detection information includes: Performing a first preprocessing on the driver driving image to obtain a first preprocessed image; Performing a first feature extraction on the first preprocessed image to obtain a first image feature; The first image feature is subjected to seat belt detection and classification processing based on a pre-trained first binary classification model to obtain the seat belt detection information.

3. The method for evaluating automobile insurance premium according to claim 1, characterized in that: The step of performing head recognition processing on the driver's driving image to obtain head recognition information includes: Performing head detection processing on the driver's driving image based on a pre-trained lightweight deep neural network to obtain head detection information; In a case where the head detection information indicates that the driver's head is not detected, determining the driver's head lowering information as the head recognition information; In the case where the head detection information indicates that the driver's head is detected, face detection processing is performed on the driver's driving image based on a pre-trained face detector to obtain face detection information; and the head recognition information is determined based on the face detection information.

4. The method for evaluating automobile insurance premium according to claim 3, characterized in that: The determining the head recognition information according to the face detection information includes: In a case where the facial detection information indicates that a complete face of the driver is detected, determining the driver's non-head-down information as the head recognition information; In a case where the facial detection information indicates that the complete face of the driver is not detected, information that the driver lowers his head is determined as the head recognition information.

5. The method for evaluating automobile insurance premium according to claim 1, characterized in that: The performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information includes: Performing face detection processing on the driver's driving image to obtain a driver's face image; Extracting facial features from the driver's facial image to obtain facial features of the driver; Performing key point location extraction processing on the driver's facial features to obtain facial key point features; Identify and classify the key features of the face to obtain multiple expression information; The plurality of expression information are matched with a preset basic expression combination to obtain the composite emotion recognition information.

6. The method for evaluating automobile insurance premium according to claim 1, characterized in that: The performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information includes: Performing a second preprocessing on the driver driving image to obtain a second preprocessed image; Performing second feature extraction on the second preprocessed image to obtain second image features; Perform mobile phone detection on the second image feature based on a pre-trained second binary classification model to obtain the mobile phone detection information.

7. The method for evaluating automobile insurance premium according to claim 1, characterized in that: The step of performing score calculation and processing according to the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weight score value set to obtain a driving behavior score value includes: Selecting corresponding weighted scoring values ​​from the weighted scoring value set according to the seat belt detection information, the head recognition information, the composite emotion recognition information and the mobile phone detection information; The seat belt detection information, the head recognition information, the composite emotion recognition information and the mobile phone detection information are respectively weighted and summed with the corresponding weighted scoring values ​​to obtain the driving behavior scoring value.

8. A vehicle insurance premium assessment device, characterized in that: include: A first processing module, used for acquiring a driving image of a driver based on a preset in-vehicle camera; A second processing module is used to perform seat belt detection processing on the driver's driving image to obtain seat belt detection information; And, performing head recognition processing on the driving image of the driver to obtain head recognition information; and performing composite emotion recognition processing on the driver's driving image to obtain composite emotion recognition information; And, performing mobile phone detection processing on the driver's driving image to obtain mobile phone detection information; A third processing module is used to perform scoring calculation processing based on the seat belt detection information, the head recognition information, the composite emotion recognition information, the mobile phone detection information and a preset weighted scoring value set to obtain a driving behavior scoring value; The fourth processing module is used to determine the car insurance premium value according to the driving behavior score value and a preset premium score table.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the motor vehicle insurance premium assessment method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the motor vehicle insurance premium assessment method described in any one of claims 1 to 7.

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