Control system and control method for home page wallpaper of in-vehicle infotainment system

Abstract

The invention relates to the technical field of in-vehicle entertainment product homepage wallpaper, in particular to an in-vehicle entertainment product homepage wallpaper control system and a control method thereof.The control method comprises the following steps that 1, data collection is conducted, specifically, an automobile data recorder intercepts an image # imgabs0 # at a certain moment; 2, preprocessing: carrying out size normalization, color space conversion and noise suppression on the intercepted image # imgabs 1 # to obtain a preprocessed image # imgabs 2 #; 3, feature extraction: automatically partitioning the # imgabs3 # by using an image segmentation module, automatically dividing the image into a plurality of key regions # imgabs4 # based on a saliency detection and horizontal line detection algorithm, respectively extracting a local feature vector # imgabs5 # in each key region, and extracting a global feature vector # imgabs6 # from the whole image; according to the method and the system, the characteristic value in the image is extracted according to the image acquired by the automobile data recorder in real time, the current environment is identified, and the wallpaper of the automobile data recorder system is adjusted in different environments, so that the wallpaper of the system is linked with the real environment outside the automobile, the interestingness is enhanced, and better visual experience is brought to a user.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle homepage wallpaper, and in particular to a vehicle homepage wallpaper control system and a control method thereof. Background Art

[0002] The car homepage wallpaper control system is an intelligent system installed in the car that allows the driver to personalize and manage the wallpaper of the car's main interface. The driver can select different wallpapers through the car system, such as landscape, city, nature and other themes to meet personal aesthetic needs; Some high-end systems support automatic wallpaper switching based on time, weather, or driving mode to enhance the visual experience. This system is usually integrated with other functions of the car computer, such as navigation and media playback, to provide a unified interactive interface. Its operation is simple, and users only need to select and set it on the touch screen of the car computer. In actual use, if the car homepage wallpaper control system cannot automatically change the wallpaper according to the external environment of the car, the car homepage wallpaper control system will be less interesting and difficult to provide users with a good visual experience. Therefore, to address the above problems, a car homepage wallpaper control system and a control method are proposed. Summary of the Invention

[0003] The object of the present invention is to provide a vehicle homepage wallpaper control system and a control method thereof, so as to solve the problem that, during actual use, the vehicle homepage wallpaper control system cannot automatically change the wallpaper according to the external environment of the vehicle, which will lead to the low interest of the vehicle homepage wallpaper control system and the difficulty in providing users with a good visual experience.

[0004] To achieve the above object, the present invention provides the following technical solutions: A vehicle homepage wallpaper control system and a control method thereof include the following steps: Step 1: Data collection: The dashcam captures an image at a certain moment ; Step 2: Preprocessing: Capture the image Perform size normalization, color space conversion and noise suppression to obtain the preprocessed image ; Step 3: Feature extraction: Use image segmentation module to Automatically partition the image into several key areas based on saliency detection and horizontal line detection algorithms , and extract local feature vectors in each key area ,At the same time, extract the global feature vector of the entire image ; Step 4: Multi-scale feature fusion: Use the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is:

[0005] Where, and is a trainable weight matrix; is the fused feature vector; Step 5: Classification output: The fused feature vector The data is input into a multi-classification neural network, and the Softmax function is used to calculate the probability distribution of the seaside, forest, desert, and flower fields. The probability distribution formula for each scene category is:

[0006] According to the predicted probability The size of the image is 100, and the recognition results corresponding to the seaside, forest, desert and flower sea scenes are output; Step 6: The system sends the recognition result signal to the MCU through the serial port; Step 7: After receiving the data, the MCU sends the wallpaper adjustment instruction to the vehicle intelligent wallpaper control system through the CAN network; Step 8: After receiving the instruction, the car computer intelligent wallpaper control system adjusts the wallpaper.

[0007] As a further optimization of the present invention, the image segmentation module uses a combination of a saliency detection algorithm and a horizontal line detection algorithm to automatically determine the segmentation areas of the foreground, midground and background in the image, and divide the key areas accordingly. .

[0008] As a further optimized content of the present invention, wherein: the global feature Including the color histogram, texture statistics and edge features of the image, and the local features It combines local binary patterns, Gabor filters and high-level semantic features extracted by deep convolutional neural networks.

[0009] As a further optimization of the present invention, the scene classification module adopts an end-to-end trained multi-classification convolutional neural network and trains the fusion features with the cross entropy loss function as the optimization target.

[0010] As a further optimization of the present invention, in step seven, after the MCU completes verification after receiving the signal sent by the system, it stores and further processes the received data, and converts the signal into the corresponding wallpaper control instruction code according to the pre-set mapping relationship, and defines the change mode that the wallpaper should present through the code. Then, the MCU converts these control instructions into a standard CAN message format according to the communication protocol of the CAN network, including an identifier, a data field, and a control field. The identifier is used to ensure that the vehicle-mounted intelligent control system can correctly identify and receive the instruction, the data field contains detailed information on the wallpaper adjustment, and the control field contains control information on the data transmission. Finally, the MCU sends the generated CAN message to the CAN network bus so that the vehicle-mounted intelligent control system can obtain the corresponding instructions from the bus.

[0011] As a further optimization of the present invention, in step eight, after receiving the control instruction uploaded on the CAN network bus, the vehicle-mounted intelligent wallpaper control system will parse the received message, extract the control information related to the wallpaper adjustment, and drive the internal power drive circuit based on this information to change the brightness connected to the vehicle-mounted intelligent wallpaper control system to control the brightness of the wallpaper. During the adjustment process, the vehicle-mounted intelligent wallpaper control system will also monitor the working status in real time to ensure that it works normally according to the instructions, and can take corresponding protective measures or feedback error information to the upstream device when an abnormal situation occurs.

[0012] As a further optimized content of the present invention, it includes: an image acquisition module, a preprocessing module, a feature extraction module, a multi-scale feature fusion module, a scene classification module, a serial communication module, an MCU module and a vehicle intelligent wallpaper control system; The image acquisition module is used to collect images captured by the driving recorder at a certain moment in real time; The preprocessing module is used to perform size normalization, color space conversion and noise suppression on the collected image, and output a preprocessed image; The feature extraction module includes an image segmentation unit, a local feature extraction unit, and a global feature extraction unit, wherein: The image segmentation unit automatically divides the pre-processed image into foreground, midground and background by combining a saliency detection algorithm with a horizontal line detection algorithm, and determines a number of key areas accordingly; The global feature extraction unit extracts color histogram, texture statistics and edge features from the entire image to obtain a global feature vector ; The local feature extraction unit uses local binary patterns, Gabor filtering and deep convolutional neural networks in each key area to extract high-level semantic information and form a local feature vector ; The multi-scale feature fusion module uses the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is:

[0013] Where, and is a trainable weight matrix; is the fused feature vector; The scene classification module inputs the fused feature vector into the end-to-end trained multi-classification convolutional neural network and calculates the probability distribution of the four scene categories of seaside, forest, desert and flower field through the Softmax function. The probability distribution formula of each scene category is:

[0014] in is the predicted probability of the kth category (corresponding to the seaside, forest, desert and flower sea respectively); The serial communication module is used to send the identification result signal output by the classification to the vehicle microcontroller; After receiving the recognition result, the MCU module completes data verification, storage and further processing, converts the recognition signal into a wallpaper control instruction code according to a preset mapping relationship, and converts the instruction code into a standard CAN message (including identifier, data field, control field) according to the CAN network communication protocol, and sends the instruction to the vehicle intelligent wallpaper control system through the CAN network; After receiving the control command transmitted on the CAN network bus, the vehicle-mounted intelligent wallpaper control system parses the message, extracts the wallpaper adjustment control information, drives the internal power drive circuit to change the brightness of the wallpaper, and monitors the working status in real time to ensure the correct execution of the command, and takes protective measures or feedback error information when an abnormality occurs.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In this invention, based on the real-time image captured by the driving recorder, the feature values ​​in the image are extracted to identify the current environment. The wallpaper of the vehicle system is adjusted in different environments, so that the wallpaper of the system is linked with the real environment outside the vehicle, which enhances the fun and brings a better visual experience to the user. 2. This invention utilizes innovative image segmentation and multi-scale feature fusion technology to achieve high-precision recognition of seaside, forest, desert, and flower fields in dashcam images. A segmentation algorithm combining saliency detection with horizon detection automatically divides images into foreground, midground, and background. Local features are extracted from key areas, combined with global features and adaptively weighted fusion using an attention mechanism. This significantly improves the expressiveness of image features and classification accuracy. Even under complex lighting, angles, and noise interference, the system achieves stable and efficient scene recognition, providing a solid foundation for dynamic personalized control of vehicle homepage wallpapers. 3. In the present invention, the system transmits the recognition results to the on-board MCU through the standardized serial port and CAN network communication protocol. After verification, mapping conversion and standard CAN message generation, the MCU sends them to the vehicle-mounted intelligent wallpaper control system, realizing the full process automation of wallpaper control. This method not only ensures the accuracy and real-time performance of instructions during transmission, but also ensures the safe and stable operation of the system through real-time monitoring and protection measures, greatly improving the intelligent level of on-board human-computer interaction and user experience, so that the vehicle-mounted homepage wallpaper can be dynamically adjusted according to real-time scenarios to meet personalized and interactive needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system block diagram of a vehicle homepage wallpaper control system of the present invention; Figure 2 This is a flow chart of a control method for a vehicle homepage wallpaper control system according to the present invention. DETAILED DESCRIPTION

[0017] See also Figure 1-2 , the present invention provides a technical solution: A vehicle homepage wallpaper control system and a control method thereof include the following steps: Step 1: Data collection: The dashcam captures an image at a certain moment ; Step 2: Preprocessing: Capture the image Perform size normalization, color space conversion and noise suppression to obtain the preprocessed image ; Step 3: Feature extraction: Use image segmentation module to Automatically partition the image into several key areas based on saliency detection and horizontal line detection algorithms , and extract local feature vectors in each key area ,At the same time, extract the global feature vector of the entire image ; Step 4: Multi-scale feature fusion: Use the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is:

[0018] Where, and is a trainable weight matrix; is the fused feature vector; Step 5: Classification output: The fused feature vector The data is input into a multi-classification neural network, and the Softmax function is used to calculate the probability distribution of the seaside, forest, desert, and flower fields. The probability distribution formula for each scene category is:

[0019] According to the predicted probability The size of the image is 100, and the recognition results corresponding to the seaside, forest, desert and flower sea scenes are output; Step 6: The system sends the recognition result signal to the MCU through the serial port; Step 7: After receiving the data, the MCU sends the wallpaper adjustment instruction to the vehicle intelligent wallpaper control system through the CAN network; Step 8: After receiving the command, the vehicle-mounted intelligent wallpaper control system adjusts the wallpaper, realizing full process automation from image acquisition, preprocessing, feature extraction and fusion, to classification output and control command transmission. By introducing a complete multi-scale feature fusion formula and standardized CAN message conversion, it achieves precise and intelligent adjustment of wallpaper in different scenarios, improving the personalization and interactive experience of the vehicle user interface.

[0020] As a technical solution for further implementation of this plan, the image segmentation module uses a combination of saliency detection algorithm and horizontal line detection algorithm to automatically determine the segmentation areas of the foreground, midground and background in the image, and divide the key areas accordingly , combined with saliency detection and horizontal line detection technology, it can adaptively and accurately segment image areas and effectively extract the most representative key areas, thereby providing more accurate regional information for subsequent feature extraction and improving recognition accuracy; As a technical solution for further implementation of this plan, the global feature Including the color histogram, texture statistics and edge features of the image, and local features It combines high-level semantic features extracted from local binary patterns, Gabor filters, and deep convolutional neural networks. The joint extraction of global and local features can take into account both the overall statistical characteristics of the image and the semantic information of local details, ensuring that subtle differences are captured when distinguishing scenes such as the seaside, forest, desert, and flower fields, thereby improving the robustness and accuracy of the classification model. As a technical solution to further implement this plan, the scene classification module uses an end-to-end trained multi-class convolutional neural network and uses the cross-entropy loss function as the optimization target to train the fusion features. The end-to-end deep learning classification method can automatically optimize the model parameters and effectively reduce the classification error through the cross-entropy loss function, thereby achieving high-precision recognition of various scenes and adapting to the real-time control requirements in complex vehicle environments. As a technical solution for further implementation of this solution, in step seven, after the MCU completes verification after receiving the signal sent by the system, it stores and further processes the received data, and converts the signal into the corresponding wallpaper control instruction code according to the pre-set mapping relationship. The code defines the change mode that the wallpaper should present. Then, the MCU converts these control instructions into a standard CAN message format according to the communication protocol of the CAN network, including an identifier, a data field, and a control field. The identifier is used to ensure that the vehicle-mounted intelligent control system can correctly identify and receive the instruction. The data field contains detailed information on the wallpaper adjustment, and the control field contains control information for data transmission. Finally, the MCU sends the generated CAN message to the CAN network bus so that the vehicle-mounted intelligent control system can obtain the corresponding instructions from the bus. The MCU's signal verification and standardized conversion ensure the accurate transmission and execution of the control instructions. By using a message format that complies with the CAN protocol, it effectively avoids mistransmission and delay problems in the communication process, ensuring the real-time and stability of the vehicle-mounted wallpaper adjustment. As a technical solution for further implementation of this solution, in step eight, after receiving the control command uploaded on the CAN network bus, the vehicle-mounted intelligent wallpaper control system will parse the received message, extract the control information related to the wallpaper adjustment, and drive the internal power drive circuit based on this information to change the brightness of the wallpaper connected to the vehicle-mounted intelligent wallpaper control system. During the adjustment process, the vehicle-mounted intelligent wallpaper control system will also monitor the working status in real time to ensure that it works normally according to the command, and can take corresponding protection measures or feedback error information to the upstream device when an abnormal situation occurs, ensuring that the wallpaper can adjust the brightness quickly and accurately after receiving the command, and ensure the safe and stable operation of the system through real-time monitoring and protection measures, thereby improving the reliability and user experience of the vehicle-mounted intelligent control system. As a technical solution for further implementation of this plan, it includes image acquisition module, preprocessing module, feature extraction module, multi-scale feature fusion module, scene classification module, serial communication module, MCU module and vehicle intelligent wallpaper control system; The image acquisition module is used to collect images captured by the driving recorder at a certain moment in real time; The preprocessing module is used to perform size normalization, color space conversion and noise suppression on the collected images and output preprocessed images; The feature extraction module includes an image segmentation unit, a local feature extraction unit, and a global feature extraction unit, wherein: The image segmentation unit uses a combination of saliency detection algorithm and horizontal line detection algorithm to automatically divide the pre-processed image into foreground, midground and background, and determine several key areas based on this; The global feature extraction unit extracts color histogram, texture statistics and edge features from the entire image to obtain the global feature vector ; The local feature extraction unit uses local binary patterns, Gabor filtering and deep convolutional neural networks in each key area to extract high-level semantic information and form local feature vectors. ; The multi-scale feature fusion module uses the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is:

[0021] Where, and is a trainable weight matrix; is the fused feature vector; The scene classification module inputs the fused feature vector into an end-to-end trained multi-classification convolutional neural network and calculates the probability distribution of the four scene categories of seaside, forest, desert, and flower field using the Softmax function. The probability distribution formula for each scene category is:

[0022] in is the predicted probability of the kth category (corresponding to the seaside, forest, desert and flower sea respectively); The serial communication module is used to send the recognition result signal output by the classification to the on-board microcontroller; After receiving the recognition result, the MCU module completes data verification, storage and further processing, converts the recognition signal into a wallpaper control instruction code based on the preset mapping relationship, and converts the instruction code into a standard CAN message (including identifier, data field, control field) according to the CAN network communication protocol, and sends the instruction to the vehicle intelligent wallpaper control system through the CAN network; After receiving the control command transmitted on the CAN network bus, the vehicle-mounted intelligent wallpaper control system parses the message, extracts the wallpaper adjustment control information, drives the internal power drive circuit to change the brightness of the wallpaper, and monitors the working status in real time to ensure the correct execution of the command, and takes protective measures or feedback error information when an abnormality occurs. The system adopts a modular design, and the modules work closely together through standardized interfaces and communication protocols to achieve full-process intelligent control from image acquisition to wallpaper adjustment; the innovative feature extraction and multi-scale fusion technology ensures accurate scene recognition, thereby realizing real-time dynamic adjustment of personalized wallpaper, providing a highly intelligent and customized solution for in-vehicle human-computer interaction.

[0023] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A control method for a vehicle homepage wallpaper control system, characterized in that: The following steps are involved: Step 1: Data collection: The dashcam captures an image at a certain moment ; Step 2: Preprocessing: Capture the image Perform size normalization, color space conversion and noise suppression to obtain the preprocessed image ; Step 3: Feature extraction: Use image segmentation module to Automatically partition the image into several key areas based on saliency detection and horizontal line detection algorithms , and extract local feature vectors in each key area ,At the same time, extract the global feature vector of the entire image ; Step 4: Multi-scale feature fusion: Use the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is: ， Where, and is a trainable weight matrix; is the fused feature vector; Step 5: Classification output: The fused feature vector The data is input into a multi-classification neural network, and the Softmax function is used to calculate the probability distribution of the seaside, forest, desert, and flower fields. The probability distribution formula for each scene category is: ， According to the predicted probability The size of the image is 100, and the recognition results corresponding to the seaside, forest, desert and flower sea scenes are output; Step 6: The system sends the recognition result signal to the MCU through the serial port; Step 7: After receiving the data, the MCU sends the wallpaper adjustment instruction to the vehicle intelligent wallpaper control system through the CAN network; Step 8: After receiving the instruction, the car computer intelligent wallpaper control system adjusts the wallpaper.

2. The control method of the vehicle homepage wallpaper control system according to claim 1, characterized in that: The image segmentation module uses a combination of saliency detection algorithm and horizontal line detection algorithm to automatically determine the segmentation areas of the foreground, midground and background in the image, and divide the key areas accordingly. .

3. The control method of the vehicle homepage wallpaper control system according to claim 1, characterized in that: The global features Including the color histogram, texture statistics and edge features of the image, and the local features It combines local binary patterns, Gabor filters and high-level semantic features extracted by deep convolutional neural networks.

4. The control method of the vehicle homepage wallpaper control system according to claim 1, characterized in that: The scene classification module adopts an end-to-end trained multi-classification convolutional neural network and trains the fusion features with the cross entropy loss function as the optimization target.

5. The control method of the vehicle homepage wallpaper control system according to claim 1, characterized in that: In step seven, after the MCU completes verification after receiving the signal sent by the system, it stores and further processes the received data, and converts the signal into the corresponding wallpaper control instruction code according to the pre-set mapping relationship. The code defines the change mode that the wallpaper should present. Then, the MCU converts these control instructions into a standard CAN message format according to the communication protocol of the CAN network, including an identifier, a data field, and a control field. The identifier is used to ensure that the vehicle-mounted intelligent control system can correctly identify and receive the instruction. The data field contains detailed information on the wallpaper adjustment, and the control field contains control information for data transmission. Finally, the MCU sends the generated CAN message to the CAN network bus so that the vehicle-mounted intelligent control system can obtain the corresponding instruction from the bus.

6. The control method of the vehicle homepage wallpaper control system according to claim 1, characterized in that: In step eight, after receiving the control instruction uploaded on the CAN network bus, the vehicle-mounted intelligent wallpaper control system will parse the received message, extract the control information related to the wallpaper adjustment, and drive the internal power drive circuit based on this information to change the brightness connected to the vehicle-mounted intelligent wallpaper control system to control the brightness of the wallpaper. During the adjustment process, the vehicle-mounted intelligent wallpaper control system will also monitor the working status in real time to ensure that it works normally according to the instructions, and can take corresponding protective measures or feedback error information to the upstream equipment when an abnormal situation occurs.

7. The vehicle homepage wallpaper control system according to claim 1, characterized in that: It includes image acquisition module, preprocessing module, feature extraction module, multi-scale feature fusion module, scene classification module, serial communication module, MCU module and vehicle intelligent wallpaper control system; The image acquisition module is used to collect images captured by the driving recorder at a certain moment in real time; The preprocessing module is used to perform size normalization, color space conversion and noise suppression on the collected image, and output a preprocessed image; The feature extraction module is provided with an image segmentation unit, a local feature extraction unit and a global feature extraction unit, wherein: The image segmentation unit automatically divides the pre-processed image into foreground, midground and background by combining a saliency detection algorithm with a horizontal line detection algorithm, and determines a number of key areas accordingly; The global feature extraction unit extracts color histogram, texture statistics and edge features from the entire image to obtain a global feature vector ; The local feature extraction unit uses local binary patterns, Gabor filtering and deep convolutional neural networks in each key area to extract high-level semantic information and form a local feature vector ; The multi-scale feature fusion module uses the attention mechanism to perform adaptive weighted fusion of global features and local features. The adaptive weighted fusion formula is: ， Where, and is a trainable weight matrix; is the fused feature vector; The scene classification module inputs the fused feature vector into the end-to-end trained multi-classification convolutional neural network and calculates the probability distribution of the four scene categories of seaside, forest, desert and flower field through the Softmax function. The probability distribution formula of each scene category is: ， in is the predicted probability of the kth category (corresponding to the seaside, forest, desert and flower sea respectively); The serial communication module is used to send the identification result signal output by the classification to the vehicle microcontroller; After receiving the recognition result, the MCU module completes data verification, storage and further processing, converts the recognition signal into a wallpaper control instruction code according to a preset mapping relationship, and converts the instruction code into a standard CAN message (including identifier, data field, control field) according to the CAN network communication protocol, and sends the instruction to the vehicle intelligent wallpaper control system through the CAN network; After receiving the control command transmitted on the CAN network bus, the vehicle-mounted intelligent wallpaper control system parses the message, extracts the wallpaper adjustment control information, drives the internal power drive circuit to change the brightness of the wallpaper, and monitors the working status in real time to ensure the correct execution of the command, and takes protective measures or feedback error information when an abnormality occurs.

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