Vehicle driving night vision auxiliary sensing system

Abstract

The invention relates to the technical field of vehicle driving, and discloses a vehicle driving night vision auxiliary sensing system, which is characterized in that an image acquisition module extracts a vehicle road area according to a night vision infrared image of a vehicle, and divides the vehicle road area into a plurality of segmented vehicle road areas; the area classification module analyzes the segmented vehicle road area and determines a corresponding area category, and the area category comprises an interference road area and a perception road area; a coefficient calculation module calculates an area obstacle coefficient based on the sensing pixel value corresponding to each sensing road area; the auxiliary sensing module calculates a front road obstacle coefficient according to the position of the vehicle and each sensing road area, adjusts the night vision infrared image, performs night vision danger auxiliary sensing on the vehicle based on the adjusted night vision infrared image, can dynamically distinguish an interference area from a sensing area, and improves the safety of the vehicle. The accurate night vision infrared image is determined in combination with multi-dimensional parameters, so that accurate obstacle risk perception is realized, and more reliable and safer night vision assistance is provided for a driver.

Description

Technical Field

[0001] The present invention relates to the field of vehicle driving technology, and in particular to a vehicle driving night vision auxiliary perception system. Background Art

[0002] With the rapid development of the automotive industry, nighttime driving safety has become a key research topic in the field of intelligent transportation. Due to insufficient light and low visibility at night, drivers find it difficult to accurately identify obstacles or other potential hazards on the road, resulting in a significantly higher traffic accident rate at night than during the day.

[0003] Traditional night vision assisted perception relies on vehicle lighting systems, such as headlights. However, the lighting effect of vehicle lighting systems is limited in extreme darkness or in adverse weather conditions, and they cannot provide sufficient visual support for the driver. The lack of refined segmentation and classification of road areas makes it impossible to effectively distinguish between interference areas (such as vegetation and buildings on both sides of the road) and effective sensing areas (such as obstacles in the center of the road). This reduces the system's perception accuracy and practicality, resulting in insufficient sensitivity and robustness in obstacle detection, making it difficult to adapt to complex and changing night driving scenarios. Summary of the Invention

[0004] An embodiment of the present invention provides a night vision assistance perception system for vehicle driving. The present invention can dynamically distinguish between interference areas and perception areas, and determine accurate night vision infrared images based on multi-dimensional parameters, thereby achieving accurate obstacle risk perception and providing drivers with more reliable and safer night vision assistance.

[0005] In order to achieve the above object, the present invention provides a vehicle driving night vision auxiliary perception system, comprising: An image acquisition module is used to acquire a night vision infrared image corresponding to the vehicle at the current moment, extract a vehicle road area, and divide the vehicle road area into a plurality of segmented vehicle road areas; A region classification module is used to analyze each segmented vehicle road region and determine a region category corresponding to each segmented vehicle road region, wherein the region category includes an interference road region and a perception road region; A coefficient calculation module, configured to extract each perceived road area and calculate a regional obstacle coefficient corresponding to the perceived road area based on corresponding perceived pixel values ​​within each perceived road area; An auxiliary perception module is used to determine the vehicle position of the vehicle, calculate the road obstacle coefficient of the vehicle ahead based on the vehicle position and each perception road area, adjust the night vision infrared image according to the road obstacle coefficient ahead, and perform night vision hazard auxiliary perception of the vehicle based on the adjusted night vision infrared image.

[0006] Furthermore, the image acquisition module is used to: The image acquisition module is used to collect original image data of the road scene based on the vehicle-mounted infrared camera corresponding to the vehicle; The image acquisition module is used to perform image preprocessing on the original image data to obtain a corresponding night vision infrared image, wherein the image preprocessing includes non-uniformity correction and grayscale stretching.

[0007] Furthermore, the region classification module is used to: The region classification module is used to extract pixel values ​​from the segmented vehicle road region and determine a corresponding region pixel value sequence; The region classification module is used to determine a moment before the current moment and determine a previous pixel value sequence corresponding to the previous moment; The region classification module is used to determine the image change factor of the segmented vehicle road region based on the region pixel value sequence and the previous pixel value sequence; The region classification module is used to pre-set a preset image change factor and determine the region category corresponding to each segmented vehicle road region based on the relationship between the image change factor and the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as an interference road region when the image change factor is less than the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as a perceived road region when the image change factor is greater than or equal to the preset image change factor.

[0008] Furthermore, the region classification module is used to: The region classification module is used to perform one-to-one matching between the region pixel value sequence and the pixel values ​​in the previous pixel value sequence to obtain a plurality of corresponding pixel value matching pairs; The region classification module is used to calculate the pixel value deviation corresponding to each pixel value matching pair, wherein the pixel value deviation is the absolute value of the difference between the two pixel values ​​in the pixel value matching pair; The region classification module is used to determine the maximum pixel value deviation from all pixel value deviations; The region classification module is used to extract the minimum pixel value from the previous pixel value sequence and the maximum pixel value from the region pixel value sequence; The region classification module is used to determine the extreme pixel value deviation between the minimum pixel value and the maximum pixel value, wherein the extreme pixel value deviation is the difference between the maximum pixel value and the minimum pixel value; The region classification module is configured to use a ratio of the extreme pixel value deviation to the pixel value deviation as an image variation factor for segmenting a vehicle road region.

[0009] Furthermore, the coefficient calculation module is used to: The coefficient calculation module is used to construct a perception pixel value fitting curve according to the corresponding perception pixel values ​​in each perception road area, and determine a plurality of closed perception pixel value areas corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on all closed perceived pixel value areas.

[0010] Furthermore, the coefficient calculation module is used to: The coefficient calculation module is used to perform curve fitting on the corresponding perception pixel values ​​in each perception road area to obtain a perception pixel value fitting curve; The coefficient calculation module is used to determine the initial perception pixel value and the final perception pixel value corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to connect the initial perception pixel value and the final perception pixel value to obtain a curved connecting line; The coefficient calculation module is used to obtain a plurality of closed perception pixel value areas based on the curve connecting line and the perception pixel value fitting curve.

[0011] Furthermore, the coefficient calculation module is used to: The coefficient calculation module is used to determine the standard perception pixel value corresponding to the perception road area, wherein the standard perception pixel value is the mean value corresponding to the perception road area; The coefficient calculation module is used to mark the standard perception pixel value on the perception pixel value fitting curve; The coefficient calculation module is used to determine the region center corresponding to each closed perception pixel value region, and generate a region mark for each region center according to the position of each region center and the standard perception pixel value; The coefficient calculation module is used to generate a first region mark for the corresponding region center when the region center is smaller than the standard perception pixel value; The coefficient calculation module is used to generate a second region mark for the corresponding region center when the region center is equal to the standard perception pixel value; The coefficient calculation module is used to generate a third region mark for the corresponding region center when the region center is greater than the standard perception pixel value; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on the first area mark, the second area mark and the third area mark.

[0012] Furthermore, the coefficient calculation module is used to: The coefficient calculation module is used to count the number of first marks marked in the first area, count the number of second marks marked in the second area, and count the number of third marks marked in the third area; The regional obstacle coefficient corresponding to the perceived road area is calculated according to the following formula: ; Wherein, q is the regional obstacle coefficient corresponding to the perceived road area, w1 is the first marker number, w2 is the second marker number, w3 is the third marker number, and max is the maximum value symbol.

[0013] Furthermore, the auxiliary perception module is used to: The auxiliary perception module is used to determine the shortest road distance between each perception road area and the vehicle position; The auxiliary perception module is used to calculate the product value of each shortest road distance and the corresponding regional obstacle coefficient as the relative obstacle coefficient; The auxiliary perception module is used to perform weighted average summation on the relative obstacle coefficients corresponding to each perception road area to obtain the obstacle coefficient of the road ahead of the vehicle.

[0014] Furthermore, the auxiliary perception module is used to: The auxiliary perception module is used to obtain the current image enhancement adjustment value of the night vision infrared image; The auxiliary perception module is used to pre-set a first preset front road obstacle coefficient and a second preset front road obstacle coefficient; The auxiliary sensing module is used to pre-set a first preset adjustment value, a second preset adjustment value and a third preset adjustment value; The auxiliary perception module is configured to calculate a first product value of the first preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is less than the first preset front road obstacle coefficient, and adjust the night vision infrared image based on the first product value to obtain an adjusted night vision infrared image; The auxiliary perception module is configured to, when the front road obstacle coefficient is greater than or equal to the first preset front road obstacle coefficient and less than the second preset front road obstacle coefficient, calculate a second product value of the second preset adjustment value and the current image enhancement adjustment value, and adjust the night vision infrared image based on the second product value to obtain an adjusted night vision infrared image; The auxiliary perception module is used to calculate a third product value of the third preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is greater than or equal to the second preset front road obstacle coefficient, and adjust the night vision infrared image based on the third product value to obtain an adjusted night vision infrared image.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a vehicle driving night vision auxiliary perception system, wherein an image acquisition module extracts a vehicle road area according to a night vision infrared image of a vehicle and divides the area into a plurality of segmented vehicle road areas; a region classification module analyzes the segmented vehicle road area and determines a corresponding region category, wherein the region category includes an interference road area and a perception road area; a coefficient calculation module calculates a region obstacle coefficient based on a perception pixel value corresponding to each perception road area; an auxiliary perception module calculates a front road obstacle coefficient according to a vehicle position and each perception road area, adjusts the night vision infrared image, and performs night vision hazard auxiliary perception on the vehicle based on the adjusted night vision infrared image, and can dynamically distinguish between interference areas and perception areas, determine an accurate night vision infrared image in combination with multi-dimensional parameters, thereby realizing accurate obstacle risk perception, and providing a more reliable and safer night vision assistance for the driver. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings: Figure 1 A schematic structural diagram of a vehicle driving night vision auxiliary perception system in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0018] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a vehicle driving night vision auxiliary perception system, comprising: An image acquisition module is used to acquire a night vision infrared image corresponding to the vehicle at the current moment, extract a vehicle road area, and divide the vehicle road area into a plurality of segmented vehicle road areas; In this embodiment, the extracted vehicle road area is cut into a plurality of small blocks as segmented vehicle road areas, such as by fixed-size grids, which is not specifically limited here.

[0023] In some embodiments of the present application, the image acquisition module is used to: The image acquisition module is used to collect original image data of the road scene based on the vehicle-mounted infrared camera corresponding to the vehicle; The image acquisition module is used to perform image preprocessing on the original image data to obtain a corresponding night vision infrared image, wherein the image preprocessing includes non-uniformity correction and grayscale stretching.

[0024] In this embodiment, the original image data refers to the image directly captured by the vehicle-mounted infrared camera. Non-uniformity correction and grayscale stretching are means for clearly processing the original image data, which will not be repeated here.

[0025] The beneficial effect of the above technical solution is that the present invention performs non-uniformity correction and grayscale stretching on the original image data, which can further ensure the clarity of the image.

[0026] A region classification module is used to analyze each segmented vehicle road region and determine a region category corresponding to each segmented vehicle road region, wherein the region category includes an interference road region and a perception road region; In some embodiments of the present application, the region classification module is used to: The region classification module is used to extract pixel values ​​from the segmented vehicle road region and determine a corresponding region pixel value sequence; The region classification module is used to determine a moment before the current moment and determine a previous pixel value sequence corresponding to the previous moment; The region classification module is used to determine the image change factor of the segmented vehicle road region based on the region pixel value sequence and the previous pixel value sequence; The region classification module is used to pre-set a preset image change factor and determine the region category corresponding to each segmented vehicle road region based on the relationship between the image change factor and the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as an interference road region when the image change factor is less than the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as a perceived road region when the image change factor is greater than or equal to the preset image change factor.

[0027] In this embodiment, the previous moment here is a historical moment, and the previous moment is a moment that includes both pedestrians and vehicles, thereby ensuring that the determination of the perceived road area is based on a real dynamic background.

[0028] In this embodiment, when determining the previous pixel value sequence corresponding to the previous moment, the previous vehicle road area corresponding to the previous moment is first determined, and the corresponding multiple previous segmented vehicle road areas are determined, and then the pixel value is determined. The specific method is consistent with the method of determining the regional pixel value sequence at the current moment, and will not be repeated here.

[0029] In this embodiment, the image change factor may represent the change of the segmented vehicle road area.

[0030] In this embodiment, the preset image change factor is preferably 5, and can be adjusted according to actual conditions.

[0031] In this embodiment, the interfering road area includes flowers, grass, trees, and buildings.

[0032] In this embodiment, the sensed road area includes pedestrians and vehicles.

[0033] In this embodiment, the interfering road area and the perceived road area corresponding to the current moment can be determined based on the previous pixel value sequence corresponding to the previous moment. In other words, the determination of the interfering road area and the perceived road area changes in real time based on the current moment. This application provides a determination method for a specific moment, and specific adaptive determinations can be made at future moments.

[0034] The beneficial effect of the above technical solution is: the present invention determines the interfering road area and the perceived road area corresponding to each segmented vehicle road area based on the relationship between the image change factor and the preset image change factor, ensures the intelligence and speed of the judgment, avoids the existing human eye recognition method, lays the foundation for night vision assisted perception, improves the accuracy and practicality of night vision assisted perception, and avoids the perception of erroneous obstacles.

[0035] In some embodiments of the present application, the region classification module is used to: The region classification module is used to perform one-to-one matching between the region pixel value sequence and the pixel values ​​in the previous pixel value sequence to obtain a plurality of corresponding pixel value matching pairs; The region classification module is used to calculate the pixel value deviation corresponding to each pixel value matching pair, wherein the pixel value deviation is the absolute value of the difference between the two pixel values ​​in the pixel value matching pair; The region classification module is used to determine the maximum pixel value deviation from all pixel value deviations; The region classification module is used to extract the minimum pixel value from the previous pixel value sequence and the maximum pixel value from the region pixel value sequence; The region classification module is used to determine the extreme pixel value deviation between the minimum pixel value and the maximum pixel value, wherein the extreme pixel value deviation is the difference between the maximum pixel value and the minimum pixel value; The region classification module is configured to use a ratio of the extreme pixel value deviation to the pixel value deviation as an image variation factor for segmenting a vehicle road region.

[0036] In this embodiment, the regional pixel value sequence and the pixel values ​​in the previous pixel value sequence are matched one by one. For example, if the pixel value corresponding to a certain position in the segmented vehicle road area is 120, the pixel value corresponding to the same position is extracted from the previous pixel value sequence, such as 115, and the two pixel values ​​are matched.

[0037] The beneficial effect of the above technical solution is: the present invention matches the regional pixel value sequence with the pixel values ​​in the previous pixel value sequence one by one, which can ensure the accuracy of the road area change analysis and avoid errors. The extreme pixel value deviation of the minimum pixel value and the maximum pixel value can be determined to determine the maximum change of the road area. The ratio of the extreme pixel value deviation to the pixel value deviation is used as the image change factor for segmenting the vehicle road area, and then the image change factor for segmenting the vehicle road area is obtained, which ensures the accuracy and comprehensiveness of the road change analysis and provides reliable data support for the analysis of obstacles on the road ahead.

[0038] A coefficient calculation module, configured to extract each perceived road area and calculate a regional obstacle coefficient corresponding to the perceived road area based on corresponding perceived pixel values ​​within each perceived road area; In this embodiment, for the convenience of distinction, the corresponding pixel value in the sensed road area is used as the sensed pixel value.

[0039] In some embodiments of the present application, the coefficient calculation module is used to: The coefficient calculation module is used to construct a perception pixel value fitting curve according to the corresponding perception pixel values ​​in each perception road area, and determine a plurality of closed perception pixel value areas corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on all closed perceived pixel value areas.

[0040] In some embodiments of the present application, the coefficient calculation module is used to: The coefficient calculation module is used to perform curve fitting on the corresponding perception pixel values ​​in each perception road area to obtain a perception pixel value fitting curve; The coefficient calculation module is used to determine the initial perception pixel value and the final perception pixel value corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to connect the initial perception pixel value and the final perception pixel value to obtain a curved connecting line; The coefficient calculation module is used to obtain a plurality of closed perception pixel value areas based on the curve connecting line and the perception pixel value fitting curve.

[0041] In this embodiment, the initial perceived pixel value and the final perceived pixel value are determined, and these two points are connected to obtain a curved connecting line. The curved connecting line passes through the perceived pixel value fitting curve, thereby obtaining multiple closed perceived pixel value areas.

[0042] The beneficial effect of the above technical solution is: the present invention obtains multiple closed perception pixel value areas based on the curve connecting line and the perception pixel value fitting curve, realizes the refined division of the perception pixel values, integrates and analyzes the perception pixel values ​​within the variation range, and effectively ensures the accuracy of vehicle driving night vision auxiliary perception.

[0043] In some embodiments of the present application, the coefficient calculation module is used to: The coefficient calculation module is used to determine the standard perception pixel value corresponding to the perception road area, wherein the standard perception pixel value is the mean value corresponding to the perception road area; The coefficient calculation module is used to mark the standard perception pixel value on the perception pixel value fitting curve; The coefficient calculation module is used to determine the region center corresponding to each closed perception pixel value region, and generate a region mark for each region center according to the position of each region center and the standard perception pixel value; The coefficient calculation module is used to generate a first region mark for the corresponding region center when the region center is smaller than the standard perception pixel value; The coefficient calculation module is used to generate a second region mark for the corresponding region center when the region center is equal to the standard perception pixel value; The coefficient calculation module is used to generate a third region mark for the corresponding region center when the region center is greater than the standard perception pixel value; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on the first area mark, the second area mark and the third area mark.

[0044] In this embodiment, the standard perception pixel value is marked on the perception pixel value fitting curve, that is, the position of the standard perception pixel value is determined.

[0045] In this embodiment, different marks are generated according to the position corresponding to the center of gravity of the region.

[0046] The beneficial effect of the above technical solution is: the present invention calculates the regional obstacle coefficient corresponding to the perceived road area based on the first regional marker, the second regional marker and the third regional marker. When the regional center of gravity is less than, equal to or greater than the standard perception pixel value, the obstacle conditions represented are different. Therefore, regional marking is performed here to further feedback the change status of regional obstacles.

[0047] In some embodiments of the present application, the coefficient calculation module is used to: The coefficient calculation module is used to count the number of first marks marked in the first area, count the number of second marks marked in the second area, and count the number of third marks marked in the third area; The regional obstacle coefficient corresponding to the perceived road area is calculated according to the following formula: ; Wherein, q is the regional obstacle coefficient corresponding to the perceived road area, w1 is the first marker number, w2 is the second marker number, w3 is the third marker number, and max is the maximum value symbol.

[0048] In this embodiment, max(w2, w3) refers to selecting the maximum value from w2 and w3. If the two are the same, one is randomly selected.

[0049] An auxiliary perception module is used to determine the vehicle position of the vehicle, calculate the road obstacle coefficient of the vehicle ahead based on the vehicle position and each perception road area, adjust the night vision infrared image according to the road obstacle coefficient ahead, and perform night vision hazard auxiliary perception of the vehicle based on the adjusted night vision infrared image.

[0050] In some embodiments of the present application, the auxiliary perception module is used to: The auxiliary perception module is used to determine the shortest road distance between each perception road area and the vehicle position; The auxiliary perception module is used to calculate the product value of each shortest road distance and the corresponding regional obstacle coefficient as the relative obstacle coefficient; The auxiliary perception module is used to perform weighted average summation on the relative obstacle coefficients corresponding to each perception road area to obtain the obstacle coefficient of the road ahead of the vehicle.

[0051] In this embodiment, when determining the shortest road distance, it can be determined based on the spatial coordinates of the sensed road area and the spatial coordinates of the vehicle position.

[0052] In this embodiment, different weights are assigned to each perceived road area, and the weighting can be specifically assigned according to a subjective weighting method or an objective weighting method.

[0053] The beneficial effect of the above technical solution is that the present invention performs weighted averaging on the relative obstacle coefficients corresponding to each perceived road area to obtain the vehicle's front road obstacle coefficient. The front road obstacle coefficient can provide feedback on the relative presence of the vehicle and the front obstacle, which can better lay the foundation for image enhancement processing.

[0054] In some embodiments of the present application, the auxiliary perception module is used to: The auxiliary perception module is used to obtain the current image enhancement adjustment value of the night vision infrared image; The auxiliary perception module is used to pre-set a first preset front road obstacle coefficient and a second preset front road obstacle coefficient; The auxiliary sensing module is used to pre-set a first preset adjustment value, a second preset adjustment value and a third preset adjustment value; The auxiliary perception module is configured to calculate a first product value of the first preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is less than the first preset front road obstacle coefficient, and adjust the night vision infrared image based on the first product value to obtain an adjusted night vision infrared image; The auxiliary perception module is configured to, when the front road obstacle coefficient is greater than or equal to the first preset front road obstacle coefficient and less than the second preset front road obstacle coefficient, calculate a second product value of the second preset adjustment value and the current image enhancement adjustment value, and adjust the night vision infrared image based on the second product value to obtain an adjusted night vision infrared image; The auxiliary perception module is used to calculate a third product value of the third preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is greater than or equal to the second preset front road obstacle coefficient, and adjust the night vision infrared image based on the third product value to obtain an adjusted night vision infrared image.

[0055] In this embodiment, the current image enhancement adjustment value is set based on the dynamic range and signal-to-noise ratio performance of the vehicle-mounted infrared camera. The current image enhancement adjustment value can enhance the contrast, brightness, sharpness, etc. of the image.

[0056] In this embodiment, the first preset road ahead obstacle coefficient is preferably 12, and the second preset road ahead obstacle coefficient is preferably 16, which can be adaptively adjusted according to actual needs.

[0057] In this embodiment, the first preset adjustment value is preferably 1.15, the second preset adjustment value is preferably 1.25, and the third preset adjustment value is preferably 1.35.

[0058] In this embodiment, the first product value, the second product value or the third product value is obtained according to the above, and then the night vision infrared image is enhanced to obtain an adjusted night vision infrared image.

[0059] The beneficial effect of the above technical solution is: the present invention selects the corresponding preset adjustment value according to the front road obstacle coefficient, the first preset front road obstacle coefficient and the second preset front road obstacle coefficient, realizes the adjustment of the current image enhancement adjustment value, and further realizes the enhanced processing of the night vision infrared image, which can provide enhanced information for the image in the night vision scene, thereby improving the visual perception ability under night vision, can reduce noise interference, and can also enhance the obstacle contour, has higher adaptability and reliability, and can effectively reduce the risk of night driving accidents.

[0060] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0061] While the present invention has been described above with reference to exemplary embodiments, various modifications may be made and equivalent components may be substituted without departing from the scope of the present invention. In particular, the various features of the disclosed embodiments may be combined with one another in any manner, provided no structural conflicts exist. These combinations are not fully described in this specification for reasons of space and resource conservation.

[0062] Those skilled in the art will understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A vehicle driving night vision auxiliary perception system, characterized in that: include: An image acquisition module is used to acquire a night vision infrared image corresponding to the vehicle at the current moment, extract a vehicle road area, and divide the vehicle road area into a plurality of segmented vehicle road areas; A region classification module is used to analyze each segmented vehicle road region and determine a region category corresponding to each segmented vehicle road region, wherein the region category includes an interference road region and a perception road region; A coefficient calculation module, configured to extract each perceived road area and calculate a regional obstacle coefficient corresponding to the perceived road area based on corresponding perceived pixel values ​​within each perceived road area; An auxiliary perception module is used to determine the vehicle position of the vehicle, calculate the road obstacle coefficient of the vehicle ahead based on the vehicle position and each perception road area, adjust the night vision infrared image according to the road obstacle coefficient ahead, and perform night vision hazard auxiliary perception of the vehicle based on the adjusted night vision infrared image.

2. The vehicle driving night vision auxiliary perception system according to claim 1, characterized in that: The image acquisition module is used for: The image acquisition module is used to collect original image data of the road scene based on the vehicle-mounted infrared camera corresponding to the vehicle; The image acquisition module is used to perform image preprocessing on the original image data to obtain a corresponding night vision infrared image, wherein the image preprocessing includes non-uniformity correction and grayscale stretching.

3. The vehicle driving night vision auxiliary perception system according to claim 1, characterized in that: The region classification module is used to: The region classification module is used to extract pixel values ​​from the segmented vehicle road region and determine a corresponding region pixel value sequence; The region classification module is used to determine a moment before the current moment and determine a previous pixel value sequence corresponding to the previous moment; The region classification module is used to determine the image change factor of the segmented vehicle road region based on the region pixel value sequence and the previous pixel value sequence; The region classification module is used to pre-set a preset image change factor and determine the region category corresponding to each segmented vehicle road region based on the relationship between the image change factor and the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as an interference road region when the image change factor is less than the preset image change factor; The region classification module is configured to classify the corresponding segmented vehicle road region as a perceived road region when the image change factor is greater than or equal to the preset image change factor.

4. The vehicle driving night vision auxiliary perception system according to claim 3, characterized in that: The region classification module is used to: The region classification module is used to perform one-to-one matching between the region pixel value sequence and the pixel values ​​in the previous pixel value sequence to obtain a plurality of corresponding pixel value matching pairs; The region classification module is used to calculate the pixel value deviation corresponding to each pixel value matching pair, wherein the pixel value deviation is the absolute value of the difference between the two pixel values ​​in the pixel value matching pair; The region classification module is used to determine the maximum pixel value deviation from all pixel value deviations; The region classification module is used to extract the minimum pixel value from the previous pixel value sequence and the maximum pixel value from the region pixel value sequence; The region classification module is used to determine the extreme pixel value deviation between the minimum pixel value and the maximum pixel value, wherein the extreme pixel value deviation is the difference between the maximum pixel value and the minimum pixel value; The region classification module is configured to use a ratio of the extreme pixel value deviation to the pixel value deviation as an image variation factor for segmenting a vehicle road region.

5. The vehicle driving night vision auxiliary perception system according to claim 1, characterized in that: The coefficient calculation module is used for: The coefficient calculation module is used to construct a perception pixel value fitting curve according to the corresponding perception pixel values ​​in each perception road area, and determine a plurality of closed perception pixel value areas corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on all closed perceived pixel value areas.

6. The vehicle driving night vision auxiliary perception system according to claim 5, characterized in that: The coefficient calculation module is used for: The coefficient calculation module is used to perform curve fitting on the corresponding perception pixel values ​​in each perception road area to obtain a perception pixel value fitting curve; The coefficient calculation module is used to determine the initial perception pixel value and the final perception pixel value corresponding to the perception pixel value fitting curve; The coefficient calculation module is used to connect the initial perception pixel value and the final perception pixel value to obtain a curved connecting line; The coefficient calculation module is used to obtain a plurality of closed perception pixel value areas based on the curve connecting line and the perception pixel value fitting curve.

7. The vehicle driving night vision auxiliary perception system according to claim 6, characterized in that: The coefficient calculation module is used for: The coefficient calculation module is used to determine the standard perception pixel value corresponding to the perception road area, wherein the standard perception pixel value is the mean value corresponding to the perception road area; The coefficient calculation module is used to mark the standard perception pixel value on the perception pixel value fitting curve; The coefficient calculation module is used to determine the region center corresponding to each closed perception pixel value region, and generate a region mark for each region center according to the position of each region center and the standard perception pixel value; The coefficient calculation module is used to generate a first region mark for the corresponding region center when the region center is smaller than the standard perception pixel value; The coefficient calculation module is used to generate a second region mark for the corresponding region center when the region center is equal to the standard perception pixel value; The coefficient calculation module is used to generate a third region mark for the corresponding region center when the region center is greater than the standard perception pixel value; The coefficient calculation module is used to calculate the regional obstacle coefficient corresponding to the perceived road area based on the first area mark, the second area mark and the third area mark.

8. The vehicle driving night vision auxiliary perception system according to claim 7, characterized in that: The coefficient calculation module is used for: The coefficient calculation module is used to count the number of first marks marked in the first area, count the number of second marks marked in the second area, and count the number of third marks marked in the third area; The regional obstacle coefficient corresponding to the perceived road area is calculated according to the following formula: ； Wherein, q is the regional obstacle coefficient corresponding to the perceived road area, w1 is the first marker number, w2 is the second marker number, w3 is the third marker number, and max is the maximum value symbol.

9. The vehicle driving night vision auxiliary perception system according to claim 1, characterized in that: The auxiliary perception module is used to: The auxiliary perception module is used to determine the shortest road distance between each perception road area and the vehicle position; The auxiliary perception module is used to calculate the product value of each shortest road distance and the corresponding regional obstacle coefficient as the relative obstacle coefficient; The auxiliary perception module is used to perform weighted average summation on the relative obstacle coefficients corresponding to each perception road area to obtain the obstacle coefficient of the road ahead of the vehicle.

10. The vehicle driving night vision auxiliary perception system according to claim 1, characterized in that: The auxiliary perception module is used to: The auxiliary perception module is used to obtain the current image enhancement adjustment value of the night vision infrared image; The auxiliary perception module is used to pre-set a first preset front road obstacle coefficient and a second preset front road obstacle coefficient; The auxiliary sensing module is used to pre-set a first preset adjustment value, a second preset adjustment value and a third preset adjustment value; The auxiliary perception module is configured to calculate a first product value of the first preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is less than the first preset front road obstacle coefficient, and adjust the night vision infrared image based on the first product value to obtain an adjusted night vision infrared image; The auxiliary perception module is configured to, when the front road obstacle coefficient is greater than or equal to the first preset front road obstacle coefficient and less than the second preset front road obstacle coefficient, calculate a second product value of the second preset adjustment value and the current image enhancement adjustment value, and adjust the night vision infrared image based on the second product value to obtain an adjusted night vision infrared image; The auxiliary perception module is used to calculate a third product value of the third preset adjustment value and the current image enhancement adjustment value when the front road obstacle coefficient is greater than or equal to the second preset front road obstacle coefficient, and adjust the night vision infrared image based on the third product value to obtain an adjusted night vision infrared image.

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