WO2022127307A1 - 车辆视觉辅助系统、车载图像显示方法及装置 - Google Patents
车辆视觉辅助系统、车载图像显示方法及装置 Download PDFInfo
- Publication number
- WO2022127307A1 WO2022127307A1 PCT/CN2021/122882 CN2021122882W WO2022127307A1 WO 2022127307 A1 WO2022127307 A1 WO 2022127307A1 CN 2021122882 W CN2021122882 W CN 2021122882W WO 2022127307 A1 WO2022127307 A1 WO 2022127307A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- image
- vehicle
- original image
- processing
- enhanced
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000004438 eyesight Effects 0.000 title abstract description 14
- 238000005286 illumination Methods 0.000 claims description 50
- 230000009466 transformation Effects 0.000 claims description 39
- 230000000007 visual effect Effects 0.000 claims description 29
- 230000008569 process Effects 0.000 claims description 23
- 238000003891 environmental analysis Methods 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 description 40
- 238000005516 engineering process Methods 0.000 description 20
- 238000010586 diagram Methods 0.000 description 8
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 4
- 230000005611 electricity Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 206010047513 Vision blurred Diseases 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004297 night vision Effects 0.000 description 1
- 230000009469 supplementation Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
Definitions
- the present disclosure belongs to the field of vehicle driving safety, and relates to a vehicle visual assistance system, a vehicle image display method and a device.
- visual assistance systems refer to systems that provide drivers with visual information to improve driving safety.
- Traditional visual aid systems include night vision systems, which improve visual effects through infrared cameras.
- the present disclosure provides a vehicle visual assistance system, a vehicle image display method and a device, which aim to improve the visual effect of the occupants in the vehicle and improve the driving safety under the condition of poor visual conditions.
- Some embodiments of the present disclosure provide a vehicle visual assistance system, including a high-definition camera; the vehicle visual assistance system is further provided with an AI image processing module using AI image processing technology, and a window display using transparent OLED display technology;
- the high-definition camera captures the environment around the vehicle, and transmits the captured original image to the AI image processing module; the AI image processing module sends the processed image signal to the window display.
- the AI image processing technology can convert the image of the road with insufficient light outside the window captured by the high-definition camera into a clear and well-lit image.
- the window display is a part of the windshield; in the case of no electricity, the window display is a piece of transparent glass; in the case of the display with electricity, the background image can be covered and displayed .
- the AI image processing module adopts a software system based on artificial intelligence technology; the original image is intercepted by the image, and then calculated by the AI model, and the calculated image data is output to the window display for display; the AI model Including AI image enhancement model and AI image transformation model.
- the AI image enhancement model is mainly used to process images with low light and blurred vision, and through the calculation of the model, an image with a normal light environment and a clear vision can be obtained.
- the AI image transformation model is mainly used to transform the surrounding environment of the road, and the original scene image becomes a changed scene image through the calculation of the model.
- the window display includes a transparent display screen and a host; the host accepts image signals and controls the display to display; the display is embedded in the windshield; the size of the display is different from the size of the vehicle.
- the windows are the same size and use a transparent OLED display.
- the vehicle visual aid system is arranged on all windshields of the vehicle to perform the transformation of the overall environment.
- Some embodiments of the present disclosure provide a vehicle vision assistance system, including:
- the camera is used to capture the environment around the vehicle to obtain the original image
- An image processing module configured to acquire the original image captured by the camera, and when the brightness of the original image is lower than a threshold, process the original image to obtain an enhanced image, the brightness of the enhanced image is greater than the brightness of the enhanced image the brightness of the original image;
- the window display is used for acquiring the enhanced image processed by the image processing module and displaying the enhanced image.
- the image processing module is configured to perform ambient illumination processing on the original image, and replace the first ambient illumination of the original image with the second ambient illumination to obtain the enhanced image, the second ambient illumination
- the light intensity of the light is greater than the light intensity of the first ambient light
- the image processing module is configured to perform ambient illumination processing on the original image by using an ambient illumination processing model
- the ambient lighting processing model is obtained by using pictures of different ambient lighting in the same scene as samples for training.
- the image processing module is further configured to perform environment transformation processing on the enhanced image before outputting the enhanced image to the window display, and replace the first road surrounding environment of the original image with The surrounding environment of the second road.
- the image processing module is configured to perform environment transformation processing on the enhanced image by using an environment transformation processing model
- the environment transformation processing model is obtained by training pictures with different road surrounding environments as samples.
- the window display is part of a windshield of the vehicle
- the vehicle window display is used to be in a transparent state when it is not powered on; when powered on, the enhanced image is displayed.
- Some embodiments of the present disclosure provide a vehicle-mounted image display method, including:
- the enhanced image is displayed through a window display.
- the processing of the original image to obtain an enhanced image includes:
- the performing ambient lighting processing on the original image includes:
- the ambient lighting processing model is obtained by using pictures of different ambient lighting in the same scene as samples for training.
- the vehicle-mounted image display method further includes:
- an environment transformation process is performed on the enhanced image, and the first road surrounding environment of the original image is replaced with a second road surrounding environment.
- performing environment transformation processing on the enhanced image includes:
- the environment transformation processing model is obtained by training pictures with different road surrounding environments as samples.
- an in-vehicle image display device including:
- a processor and a memory the memory storing at least one piece of program code, the program code being loaded and executed by the processor to implement the method of any preceding item.
- Some embodiments of the present disclosure provide a computer-readable storage medium, characterized in that, the computer-readable storage medium stores at least one piece of program code, and the program code is loaded and executed by a processor to implement the above-mentioned item. method described.
- FIG. 1 is a schematic structural diagram of a vehicle visual aid system provided by some embodiments of the present disclosure
- FIG. 2 is a functional schematic diagram of an AI image processing system in the present disclosure
- Fig. 3 is the driving view transformation process diagram of the AI image enhancement function in the present disclosure
- FIG. 4 is a process diagram of the driving field of view transformation of the AI image transformation function in the present disclosure
- FIG. 5 is a flowchart of a vehicle-mounted image display method provided by some embodiments of the present disclosure.
- FIG. 6 is a schematic structural diagram of a vehicle-mounted image display device provided by some embodiments of the present disclosure.
- infrared cameras use infrared cameras to take pictures at night to assist driving.
- the pictures taken by the infrared camera are all grayscale pictures, which are quite different from the actual field of view, and the image quality is not high, and it is not clear if it is far away, which does not greatly improve the driver's field of vision.
- FIG. 1 is a schematic structural diagram of a vehicle visual aid system provided by some embodiments of the present disclosure.
- the system includes a camera 11 , an image processing module 12 and a window display 13 , and the camera 11 and the window display 13 are respectively connected to the image processor 12 .
- the camera 11 is used for photographing the environment around the vehicle to obtain the original image; usually, the camera 11 is installed in front of the vehicle, so what is usually photographed is the environment in front of the vehicle.
- the image processing module 12 is used to obtain the original image captured by the camera, and when the brightness of the original image is lower than the threshold, the original image is processed to obtain an enhanced image, and the brightness of the enhanced image is greater than that of the original image.
- the window display 13 is used for acquiring the enhanced image processed by the image processing module 12 and displaying the enhanced image.
- the present disclosure adopts the above-mentioned technical solution, collects the original image of the driving road and the surrounding environment in real time through the camera, processes the original image through image processing technology, and enhances the brightness of the image; the processed image is displayed through the window display.
- the image is obtained by changing the brightness of the original image, and the image quality is high, so that the driver can improve the driving field of vision to the greatest extent based on the enhanced image; it can greatly improve the safety of the driver, the vehicle and the people outside the vehicle.
- the original image is directly displayed through the window display 13 .
- raw images captured on a clear day usually do not need to be processed and are displayed directly on the window display.
- the raw images captured at night with dim lighting need to be processed and then displayed on the window display.
- the brightness of the original image may refer to the average brightness of each pixel in the original image, or the intermediate brightness, the maximum brightness, the minimum brightness, and the like.
- the threshold value can be set according to actual needs, for example, it can be the minimum brightness value of the image under the condition of ensuring a certain range of vision of the driver.
- the camera 11 is a high-definition camera, so as to ensure the clarity of the captured image.
- the image processing module 12 is an artificial intelligence (Artificial Intelligence, AI) image processing module, which uses AI technology to perform image processing, thereby ensuring that the brightness of the processed image is enhanced.
- AI Artificial Intelligence
- the image processing module 12 changes the overall brightness of the image by replacing the lighting environment of the original image, so that the driver can clearly see the road environment around the vehicle through the enhanced image.
- the image processing module 12 is configured to perform ambient illumination processing on the original image, and replace the first ambient illumination of the original image with a second ambient illumination to obtain an enhanced image, and the illumination intensity of the second ambient illumination is greater than that of the first ambient illumination. the light intensity.
- the first ambient light may be the ambient light of a dimly lit nighttime, foggy day, cloudy and rainy day and other scenes with weak light intensity
- the second ambient light may be a clear daytime, brightly lit nighttime, etc. with a strong light intensity The ambient lighting of the scene.
- the light intensity refers to the light intensity under specific ambient light, such as the light intensity at night with dim lighting, the light intensity in cloudy and rainy days, the light intensity in sunny daytime, etc.
- the light intensity of each ambient light There is no need to determine the specific value of the intensity, only the size relationship needs to be clear. For example, the light intensity of a sunny day is greater than the light intensity of other ambient lighting. Therefore, when the ambient lighting is transformed, other ambient lighting is replaced by sunny. daytime ambient lighting.
- the image processing module 12 is configured to perform ambient illumination processing on the original image by using an ambient illumination processing model
- the ambient lighting processing model is obtained by training images of different ambient lighting in the same scene as samples.
- the ambient lighting processing model may be a neural network model, such as a convolutional neural network model.
- the ambient lighting processing model learns the characteristics of different ambient lighting through the training of a large number of samples, and uses the characteristics of different ambient lighting to realize the conversion of pictures of different ambient lighting, such as converting the original picture at night with dim lighting into a sunny day. enhanced image.
- the training process using pictures of different ambient lighting in the same scene as samples can make the learned characteristics of each ambient lighting more accurate.
- the training samples can have pictures in various scenes, and each scene has pictures with different ambient lighting.
- the samples used for training only need to distinguish the ambient lighting of the pictures, and do not need to pay attention to whether they are in the same scene.
- the ambient lighting processing model may be formed by cascading two convolutional neural networks, such as a decomposition network + an enhancement network, the decomposition network is used to decompose the input original image into the first illumination The image and the reflection image, the enhancement network is used to perform enhancement processing on the reflection image, and synthesize the reflection image and the second illumination image to obtain an enhanced image.
- two convolutional neural networks such as a decomposition network + an enhancement network
- the image processing module 12 is further configured to perform environment transformation processing on the enhanced image before outputting the enhanced image to the window display, and replace the first road surrounding environment of the original image with the second road surrounding environment.
- This implementation method can transform a single driving roadside scene, improve the visual experience of drivers and passengers, increase the fun of driving, and improve the comfort of driving.
- the surrounding environment of the first road is desert
- the surrounding environment of the second road is snow
- the driver can have different visual experience by replacing the environment.
- the replacement process it is necessary to ensure that the main part of the road in the picture is not replaced.
- the image processing module 12 when the image processing module 12 performs the above-mentioned replacement of the surrounding environment of the road, it first identifies the road part through the recognition algorithm, and then replaces the surrounding environment of the road on both sides of the road.
- the two cannot be realized by using the same model. It can also be implemented by two parts of a model.
- the road surrounding environment refers to the parts on both sides of the two side lines of the road, and the road part is the two side lines of the road and the part between them. Therefore, when identifying the road part, it is only necessary to perform the road edge detection, and then divide the road part and the road surrounding environment part based on this.
- the image processing module 12 is configured to perform environment transformation processing on the enhanced image by adopting an environment transformation processing model
- the environment transformation processing model is obtained by using pictures with different road surrounding environments as samples.
- the environment transformation processing model here is actually an image synthesis model, which first separates the road and the vehicles on the road, and then combines the road, the vehicles and the environment on the road into one image.
- FIG. 1 is a schematic diagram of the connection relationship of each component of a vehicle driving visual intelligent assistance system according to a specific embodiment of the present disclosure.
- the vision system includes high-definition cameras that capture the environment around the vehicle.
- the HD camera's field of view is larger than the driver's driving field of view.
- the vehicle visual assistance system is further provided with an AI image processing module using AI image processing technology, and a window display 13 using transparent OLED display technology;
- the high-definition camera captures the environment around the vehicle, and transmits the captured original image to the AI image processing module; the AI image processing module sends the processed image signal to the window display 13 .
- the present disclosure collects driving road images in real time through a high-definition camera, processes the road images through AI (artificial intelligence) image processing technology, and displays the processed images through the OLED display screen embedded in the windshield, which can be clearly transformed into a brightly lit image of the same scene, maximizing driving visibility. It can greatly improve the safety of drivers, passengers and third parties. It is also possible to transform a single driving roadside scene to improve the fun of driving.
- AI artificial intelligence
- the AI image processing technology described can convert images of roads with insufficient light outside the window captured by high-definition cameras into clear, well-lit images.
- the window display 13 is part of the windshield of the vehicle.
- the window display 13 is used to be in a transparent state (completely transparent state) when it is not powered on; when it is powered on, it displays the enhanced image (usually in a semi-transparent state at this time).
- the window display 13 is realized by using organic light emitting diode (organic light emitting diode, OLED) technology, and the window display 13 can be a transparent OLED display, that is, a self-luminous and transparent organic OLED screen, the material is OLED, but a transparent process is adopted.
- OLED organic light emitting diode
- the window display 13 is a part of the windshield; in the case of no electricity, the window display 13 is a piece of transparent glass; in the case of the display with electricity, the background image can be covered and displayed.
- the AI image processing module adopts a software system based on artificial intelligence technology; the original image is intercepted 21 by the image, and then calculated by the AI model 22, and the calculated image data is output to the window display 13 for display;
- the AI model 22 includes an AI image enhancement 23 model and an AI image transformation 24 model.
- the AI model includes the aforementioned environment illumination processing model and environment transformation processing model.
- the intelligent assistance system for vehicle driving vision of the present disclosure due to the adoption of AI image processing technology, does not require additional technical means of light supplementation, but processes the dim and blurry scene seen by the current driver through the AI model, and outputs a set of A clearer and brighter scene is displayed to the driver, thereby restoring the driver's driving vision to the greatest extent and ensuring driving safety (as shown in Figure 3).
- the AI image enhancement model 23 (that is, the ambient lighting processing model) is mainly used to process images 31 with low light and blurred vision. image 32.
- the AI image transformation 24 model (ie, the environment transformation processing model) is mainly used to transform the surrounding environment of the road, and the original scene image 41 becomes the changed scene image 42 through the calculation of the model.
- the AI image processing technology can change the driving scene, and can convert the actual driving scene of the vehicle (such as a desert road section) into a virtual driving road section (such as a snow road section).
- the present disclosure can replace driving scenes through the technology of AI image processing to increase the entertainment of driving.
- the window display 13 includes a transparent display screen and a host; the host accepts image signals and controls the display to display; the display is embedded in the windshield; the size of the display is consistent with the size of the window , using a transparent OLED display. Usually it is a normal windshield, when the image is displayed, the windshield becomes a display. The driver drives through the images on the display.
- the vehicle vision assistance system is installed on all windshields (front, rear, left and right) of the vehicle to transform the overall environment.
- FIG. 5 is a flowchart of a method for displaying an in-vehicle image provided by some embodiments of the present disclosure. Referring to Figure 5, the method includes:
- This step is performed by the aforementioned camera 11 , and for the detailed process, please refer to the foregoing description of the camera 11 .
- This step is performed by the aforementioned image processing module 12, and for the detailed process, please refer to the above description of the image processing module 12.
- S53 Display the enhanced image through the window display.
- This step is performed by the aforementioned window display 13 , and for the detailed process, please refer to the foregoing description of the vehicle window display 13 .
- the present disclosure adopts the above-mentioned technical solution, collects the original image of the driving road and the surrounding environment in real time through the camera, processes the original image through image processing technology, and enhances the brightness of the image; the processed image is displayed through the window display.
- the image is obtained by changing the brightness of the original image, and the image quality is high, so that the driver can improve the driving field of vision to the greatest extent based on the enhanced image; it can greatly improve the safety of the driver, the vehicle and the people outside the vehicle.
- the original image is directly displayed through the window display.
- raw images captured on a clear day usually do not need to be processed and are displayed directly on the window display.
- the raw images captured at night with dim lighting need to be processed and then displayed on the window display.
- the brightness of the original image may refer to the average brightness of each pixel in the original image, or the intermediate brightness, the maximum brightness, the minimum brightness, and the like.
- the threshold value can be set according to actual needs, such as the minimum brightness value of the image under the condition of ensuring a certain range of vision of the driver.
- the original image is processed to obtain an enhanced image, including:
- the first ambient light may be the ambient light of a dimly lit nighttime, foggy day, cloudy and rainy day and other scenes with weak light intensity
- the second ambient light may be a clear daytime, brightly lit nighttime, etc. with a strong light intensity The ambient lighting of the scene.
- the light intensity refers to the light intensity under specific ambient light, such as the light intensity at night with dim lighting, the light intensity in cloudy and rainy days, the light intensity in sunny daytime, etc.
- the light intensity of each ambient light There is no need to determine the specific value of the intensity, only the size relationship needs to be clear. For example, the light intensity of a sunny day is greater than the light intensity of other ambient lighting. Therefore, when the ambient lighting is transformed, other ambient lighting is replaced by sunny. daytime ambient lighting.
- ambient lighting processing is performed on the original image, including:
- the ambient lighting processing model is obtained by training images of different ambient lighting in the same scene as samples.
- the ambient lighting processing model may be a neural network model, such as a convolutional neural network model.
- the ambient lighting processing model learns the characteristics of different ambient lighting through the training of a large number of samples, and uses the characteristics of different ambient lighting to realize the conversion of pictures of different ambient lighting, for example, convert the original picture in the dimly lit night into the sunny day enhanced image.
- the training process using pictures of different ambient lighting in the same scene as samples can make the learned characteristics of each ambient lighting more accurate.
- the training samples can have pictures in various scenes, and each scene has pictures with different ambient lighting.
- the samples used for training only need to distinguish the ambient lighting of the pictures, and do not need to pay attention to whether they are in the same scene.
- the ambient lighting processing model may be formed by cascading two convolutional neural networks, such as a decomposition network + an enhancement network, the decomposition network is used to decompose the input original image into the first illumination The image and the reflection image, the enhancement network is used to perform enhancement processing on the reflection image, and synthesize the reflection image and the second illumination image to obtain an enhanced image.
- two convolutional neural networks such as a decomposition network + an enhancement network
- the vehicle image display method further includes:
- an environment transformation process is performed on the enhanced image to replace the first road surrounding environment of the original image with the second road surrounding environment.
- This implementation method can transform a single driving roadside scene, improve the visual experience of drivers and passengers, increase the fun of driving, and improve the comfort of driving.
- the surrounding environment of the first road is desert
- the surrounding environment of the second road is snow
- the driver can have different visual experience by replacing the environment.
- the replacement process it is necessary to ensure that the main part of the road in the picture is not replaced.
- the road part is first identified by the recognition algorithm, and then the surrounding environment of the road on both sides of the road is replaced. Implemented in two parts.
- the road surrounding environment refers to the parts on both sides of the two side lines of the road, and the road part is the two side lines of the road and the part between them. Therefore, when identifying the road part, only the road edge detection is actually required, and then the road part and the road surrounding environment part can be divided according to this.
- Exemplarily, performing environment transformation processing on the enhanced image including:
- the environment transformation processing model is obtained by training pictures with different road surrounding environments as samples.
- the environment transformation processing model here is actually an image synthesis model, which first separates the road and the vehicles on the road, and then combines the road, the vehicles and the environment on the road into one image.
- Embodiments of the present disclosure also provide an in-vehicle image display device.
- the in-vehicle image display device may include a processor and a memory, the memory storing at least one piece of program code, the program code being loaded and executed by the processor to implement the aforementioned method.
- FIG. 6 is a schematic structural diagram of an in-vehicle image display device provided by an embodiment of the present disclosure.
- the in-vehicle image display device 600 includes a central processing unit (Central Processing Unit, CPU) 601, a system including a random access memory (Random Access Memory, RAM) 602 and a read-only memory (Read-Only Memory, ROM) 603 memory 604 , and a system bus 605 connecting the system memory 604 and the central processing unit 601 .
- CPU Central Processing Unit
- RAM random access memory
- ROM Read-Only Memory
- the in-vehicle image display device 600 also includes a basic input/output system (Input/Output, I/O system) 606 that helps transmit information between various devices in the computer, and is used to store an operating system 613, application programs 614 and other program modules 615 of the mass storage device 607.
- I/O system input/output system
- Basic input/output system 606 includes a display 608 for displaying information and input devices 609 such as control keys for user input of information. Both the display 608 and the input device 609 are connected to the central processing unit 601 through the input and output controller 610 connected to the system bus 605 .
- Mass storage device 607 is connected to central processing unit 601 through a mass storage controller (not shown) connected to system bus 605 .
- Mass storage device 607 and its associated computer-readable media provide non-volatile storage for in-vehicle image display device 600 . That is, mass storage device 607 may include a computer-readable medium (not shown) such as a hard disk or a CD-ROM drive.
- Computer-readable media can include computer storage media and communication media.
- Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
- Computer storage media include RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other solid-state storage Its technology, Compact Disc Read-Only Memory (CD-ROM), Digital Video Disc (DVD) or other optical storage, cassette, magnetic tape, magnetic disk storage or other magnetic storage devices.
- RAM random access memory
- ROM read only Memory
- EPROM Erasable Programmable Read Only Memory
- EEPROM Electrically Erasable Programmable Read Only Memory
- flash memory or other solid-state storage Its technology, Compact Disc Read-Only Memory (CD-ROM), Digital Video Disc (DVD) or other optical storage, cassette, magnetic tape, magnetic disk storage or other magnetic storage devices.
- CD-ROM Compact
- the in-vehicle image display apparatus 600 may also be operated by connecting to a remote computer on the network through a network such as the Internet. That is, the in-vehicle image display device 600 can be connected to the network 612 through the network interface unit 611 connected to the system bus 605, or, in other words, the network interface unit 611 can also be used to connect to other types of networks or remote computer systems (not shown). ).
- the above-mentioned memory also includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU.
- the CPU 601 implements the aforementioned in-vehicle image display method by executing the one or more programs.
- FIG. 6 does not constitute a limitation on the vehicle-mounted image display device 600, and may include more or less components than the one shown, or combine some components, or use different components layout.
- Embodiments of the present disclosure also provide a computer-readable storage medium, where at least one piece of program code is stored in the computer-readable storage medium, and the program code is loaded and executed by the processor to implement the above method.
- the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
- Embodiments of the present disclosure also provide a computer program product, where at least one piece of program code is stored in the computer program product, and the program code is loaded and executed by the processor to implement the above method.
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
- Closed-Circuit Television Systems (AREA)
- Controls And Circuits For Display Device (AREA)
Abstract
Description
Claims (13)
- 一种车辆视觉辅助系统,包括:摄像头(11),用于拍摄车辆周围的环境,得到原始图像;图像处理模块(12),用于获取所述摄像头(11)拍摄的所述原始图像,在所述原始图像的亮度低于阈值时,对所述原始图像进行处理,得到增强图像,所述增强图像的亮度大于所述原始图像的亮度;车窗显示器(13),用于获取所述图像处理模块(12)处理的增强图像,显示所述增强图像。
- 根据权利要求1所述的车辆视觉辅助系统,所述图像处理模块(12),用于对所述原始图像进行环境光照处理,将所述原始图像的第一环境光照替换成第二环境光照,得到所述增强图像,所述第二环境光照的光照强度大于所述第一环境光照的光照强度。
- 根据权利要求2所述的车辆视觉辅助系统,所述图像处理模块(12),用于采用环境光照处理模型对所述原始图像进行环境光照处理;其中,所述环境光照处理模型采用同一场景下的不同环境光照的图片作为样本训练得到。
- 根据权利要求2或3所述的车辆视觉辅助系统,所述图像处理模块(12),还用于在向所述车窗显示器(13)输出所述增强图像之前,对所述增强图像进行环境变换处理,将所述原始图像的第一道路周边环境替换成第二道路周边环境。
- 根据权利要求4所述的车辆视觉辅助系统,所述图像处理模块(12),用于采用环境变换处理模型对所述增强图像进行环境变换处理;其中,所述环境变换处理模型采用具有不同道路周边环境的图片作为样本训练得到。
- 根据权利要求1至5任一项所述的车辆视觉辅助系统,所述车窗显示器(13)属于所述车辆的挡风玻璃的一部分;所述车窗显示器(13),用于在不通电时呈透明状态;在通电时,显示所述增强图像。
- 一种车载图像显示方法,包括:拍摄车辆周围的环境,得到原始图像;在所述原始图像的亮度低于阈值时,对所述原始图像进行处理,得到增强图像,所述增强图像的亮度大于所述原始图像的亮度;通过车窗显示器显示所述增强图像。
- 根据权利要求7所述的车载图像显示方法,所述对所述原始图像进行处理,得到增强图像,包括:对所述原始图像进行环境光照处理,将所述原始图像的第一环境光照替换成第二环境光照,得到所述增强图像,所述第二环境光照的光照强度大于所述第一环境光照的光照强度。
- 根据权利要求8所述的车载图像显示方法,所述对所述原始图像进行环境光照处理,包括:采用环境光照处理模型对所述原始图像进行环境光照处理;其中,所述环境光照处理模型采用同一场景下的不同环境光照的图片作为样本训练得到。
- 根据权利要求8或9所述的车载图像显示方法,所述车载图像显示方法,还包括:在向所述车窗显示器输出所述增强图像之前,对所述增强图像进行环境变换处理,将所述原始图像的第一道路周边环境替换成第二道路周边环境。
- 根据权利要求10所述的车载图像显示方法,所述对所述增强图像进行环境变换处理,包括:采用环境变换处理模型对所述增强图像进行环境变换处理;其中,所述环境变换处理模型采用具有不同道路周边环境的图片作为样本训练得到。
- 一种车载图像显示装置,包括:处理器和存储器,所述存储器存储有至少一条程序代码,所述程序代码由所述处理器加载并执行以实现如权利要求7至11任一项所述的方法。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质存储有至少一条程序代码,所述程序代码由处理器加载并执行以实现如权利要求7 至11任一项所述的方法。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011510914.4A CN112606832A (zh) | 2020-12-18 | 2020-12-18 | 一种车辆智能辅助视觉系统 |
CN202011510914.4 | 2020-12-18 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2022127307A1 true WO2022127307A1 (zh) | 2022-06-23 |
Family
ID=75241125
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2021/122882 WO2022127307A1 (zh) | 2020-12-18 | 2021-10-09 | 车辆视觉辅助系统、车载图像显示方法及装置 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN112606832A (zh) |
WO (1) | WO2022127307A1 (zh) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112606832A (zh) * | 2020-12-18 | 2021-04-06 | 芜湖雄狮汽车科技有限公司 | 一种车辆智能辅助视觉系统 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104272345A (zh) * | 2012-05-18 | 2015-01-07 | 日产自动车株式会社 | 车辆用显示装置、车辆用显示方法以及车辆用显示程序 |
CN108515909A (zh) * | 2018-04-04 | 2018-09-11 | 京东方科技集团股份有限公司 | 一种汽车抬头显示系统及其障碍物提示方法 |
CN108528339A (zh) * | 2017-03-01 | 2018-09-14 | 京东方科技集团股份有限公司 | 一种显示系统及其显示方法和车辆 |
CN110610463A (zh) * | 2019-08-07 | 2019-12-24 | 深圳大学 | 一种图像增强方法及装置 |
DE102020003668A1 (de) * | 2020-06-19 | 2020-08-20 | Daimler Ag | Verfahren zum Anzeigen eines augmentierten Bildes |
CN112606832A (zh) * | 2020-12-18 | 2021-04-06 | 芜湖雄狮汽车科技有限公司 | 一种车辆智能辅助视觉系统 |
DE102019220168A1 (de) * | 2019-12-19 | 2021-06-24 | Conti Temic Microelectronic Gmbh | Helligkeits-Umwandlung von Bildern einer Kamera |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030095080A1 (en) * | 2001-11-19 | 2003-05-22 | Koninklijke Philips Electronics N.V. | Method and system for improving car safety using image-enhancement |
CN104627078B (zh) * | 2015-02-04 | 2017-03-08 | 上海咔酷咔新能源科技有限公司 | 基于柔性透明oled的汽车驾驶虚拟系统及其控制方法 |
CN105216715A (zh) * | 2015-10-13 | 2016-01-06 | 湖南七迪视觉科技有限公司 | 一种汽车驾驶员视觉辅助增强系统 |
WO2017134861A1 (ja) * | 2016-02-05 | 2017-08-10 | 日立マクセル株式会社 | ヘッドアップディスプレイ装置 |
CN109658519B (zh) * | 2018-12-28 | 2022-07-12 | 吉林大学 | 基于现实路况信息图像处理的车载多模式增强现实系统 |
CN109636924B (zh) * | 2018-12-28 | 2022-11-22 | 吉林大学 | 基于现实路况信息三维建模的车载多模式增强现实系统 |
CN110807740B (zh) * | 2019-09-17 | 2023-04-18 | 北京大学 | 一种面向监控场景车窗图像的图像增强方法与系统 |
CN111625457A (zh) * | 2020-05-27 | 2020-09-04 | 多伦科技股份有限公司 | 基于改进的dqn算法的虚拟自动驾驶测试优化方法 |
-
2020
- 2020-12-18 CN CN202011510914.4A patent/CN112606832A/zh active Pending
-
2021
- 2021-10-09 WO PCT/CN2021/122882 patent/WO2022127307A1/zh active Application Filing
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104272345A (zh) * | 2012-05-18 | 2015-01-07 | 日产自动车株式会社 | 车辆用显示装置、车辆用显示方法以及车辆用显示程序 |
CN108528339A (zh) * | 2017-03-01 | 2018-09-14 | 京东方科技集团股份有限公司 | 一种显示系统及其显示方法和车辆 |
CN108515909A (zh) * | 2018-04-04 | 2018-09-11 | 京东方科技集团股份有限公司 | 一种汽车抬头显示系统及其障碍物提示方法 |
CN110610463A (zh) * | 2019-08-07 | 2019-12-24 | 深圳大学 | 一种图像增强方法及装置 |
DE102019220168A1 (de) * | 2019-12-19 | 2021-06-24 | Conti Temic Microelectronic Gmbh | Helligkeits-Umwandlung von Bildern einer Kamera |
DE102020003668A1 (de) * | 2020-06-19 | 2020-08-20 | Daimler Ag | Verfahren zum Anzeigen eines augmentierten Bildes |
CN112606832A (zh) * | 2020-12-18 | 2021-04-06 | 芜湖雄狮汽车科技有限公司 | 一种车辆智能辅助视觉系统 |
Also Published As
Publication number | Publication date |
---|---|
CN112606832A (zh) | 2021-04-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Binas et al. | DDD17: End-to-end DAVIS driving dataset | |
US10504214B2 (en) | System and method for image presentation by a vehicle driver assist module | |
US10063786B2 (en) | Vehicle vision system with enhanced low light capabilities | |
US11891023B1 (en) | Using camera data to manage a vehicle parked outside in cold climates | |
CN108515909B (zh) | 一种汽车抬头显示系统及其障碍物提示方法 | |
US11970156B1 (en) | Parking assistance using a stereo camera and an added light source | |
US10616488B2 (en) | Image display method, vehicle display device, vehicle sun visor, and related vehicle | |
US11341614B1 (en) | Emirror adaptable stitching | |
JP2007142624A (ja) | 車両搭載撮像装置 | |
CN208479822U (zh) | 一种车用全景环视系统 | |
CN114338958B (zh) | 一种图像处理的方法及相关设备 | |
US20150042802A1 (en) | Vehicle safety control apparatus and method using cameras | |
WO2022127307A1 (zh) | 车辆视觉辅助系统、车载图像显示方法及装置 | |
CN113866983A (zh) | 抬头显示装置、抬头显示装置的显示方法 | |
US11531197B1 (en) | Cleaning system to remove debris from a lens | |
CN112740264A (zh) | 用于处理红外图像的设计 | |
KR20170010534A (ko) | 차량 사이드 영상 제공 장치 및 그 방법 | |
CN113840123B (zh) | 一种车载图像的图像处理装置、汽车 | |
CN205193902U (zh) | 一种驱雾行车记录仪 | |
CN211628409U (zh) | 一种基于鱼眼镜头的大场景识别相机 | |
CN203528379U (zh) | 一种短波红外驾驶员视觉增强系统 | |
TWM608886U (zh) | 汽車a柱視覺盲區顯示裝置 | |
CN202491757U (zh) | 一种虚拟倒车镜系统 | |
KR102339358B1 (ko) | 악천후 상황에서의 시인성 향상을 위한 차량용 영상처리 장치 | |
WO2024146088A1 (zh) | 图像处理方法、电子设备、计算机可读存储介质 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21905236 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 21905236 Country of ref document: EP Kind code of ref document: A1 |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 21905236 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 23/01/2024) |