CN118192638A - A UAV control platform capable of three-dimensional holographic inspection - Google Patents

Abstract

The invention relates to the technical field of three-dimensional holographic inspection, and discloses an unmanned aerial vehicle control platform capable of three-dimensional holographic inspection, which comprises an image acquisition module, wherein the unmanned aerial vehicle holographic inspection module comprises: the illumination intensity detection module: collecting illumination intensity data of the current environment from the image data, wherein the illumination intensity data are expressed in lux units; illumination adaptability processing module: compressing or expanding the dynamic range of the original image and the image; local contrast enhancement module: selectively enhancing the contrast of important parts according to the image texture characteristics and the illumination conditions, and inhibiting irrelevant background noise; and the adaptive algorithm design module: defining a nonlinear relation function between illumination intensity and hologram brightness based on human eye visual characteristics; rendering output module: and sending the processed image data into a holographic projection algorithm to reconstruct and render the three-dimensional holographic image.

Description

A UAV control platform capable of three-dimensional holographic inspection

Technical Field

The present invention relates to the technical field of three-dimensional holographic patrol, and in particular to an unmanned aerial vehicle control platform capable of three-dimensional holographic patrol.

Background technique

With the maturity of drone technology, small, intelligent, and long-endurance drones are widely used in many fields, including remote sensing mapping, aerial patrols, and environmental monitoring. Drones provide a powerful carrier for three-dimensional holographic inspections due to their flexibility, relatively low cost, and no need for direct human intervention.

Holographic projection technology has made significant breakthroughs in recent years, moving from the laboratory to practical use, and can generate realistic three-dimensional images, allowing users to observe the target scene as if they were there. This immersive display technology provides a new way to visualize image data collected by drones.

After searching, the invention patent with Chinese patent number CN116520890B discloses a drone control platform capable of three-dimensional holographic patrol. Compared with the existing technology, the invention patent with Chinese patent number CN116520890B can solve the problem of inaccurate prediction of the flight trajectory of obstacles, resulting in unreasonable obstacle avoidance path planning.

However, while improving the safety of drone flight paths, the three-dimensional holographic patrol of drones also depends on a suitable lighting environment. Too strong or too weak light will affect the visibility and clarity of the holographic image, especially under strong light outdoors. The holographic image may become blurred, which will affect the accuracy of the drone patrol information. Therefore, a drone control platform capable of three-dimensional holographic patrol is proposed herein.

Summary of the invention

The purpose of the present invention is to solve the shortcomings of the prior art that the technology depends on a suitable lighting environment, and to propose a drone control platform capable of three-dimensional holographic patrol.

In order to achieve the above object, the present invention adopts the following technical solutions:

A UAV control platform capable of three-dimensional holographic patrol includes an image acquisition module. The UAV holographic patrol module includes:

Light intensity detection module: collects the light intensity data of the current environment from the image data, expressed in lux units;

Lighting Adaptive Processing Module: compress or expand the dynamic range of original images and pictures;

Local contrast enhancement module: selectively enhances the contrast of important parts according to image texture features and lighting conditions, and suppresses irrelevant background noise;

Adaptive algorithm design module: Based on the visual characteristics of the human eye, it defines the nonlinear relationship function between light intensity and holographic image brightness;

Rendering output module: The processed image data is sent to the holographic projection algorithm to reconstruct and render the three-dimensional holographic image.

The light intensity detection module measures the sensor to capture the light in the environment in real time and convert it into an analog or digital signal. Based on the output voltage Vout at a specific light intensity, the sensor's spectral response curve and conversion coefficient are found according to the sensor's data sheet, which is recorded as the proportional constant K between the light intensity and the sensor output signal. The light intensity is:

I＝(Vout-Io)/K

Among them, I represents the light intensity of the current environment, and the unit is usually Lux;

Io refers to the dark current compensation value or dark voltage, which is the output voltage of the sensor under no light or very low light conditions.

The illumination adaptation processing module includes a color space conversion unit and a saturation adjustment unit. The camera based on the drone's dynamic range captures photos of multiple exposure levels in the same scene, including low-exposure dark detail photos, normal exposure photos and high-exposure bright detail photos. The pictures with different exposure levels are geometrically corrected and aligned, and then HDR synthesis is performed to merge the best brightness values of the same pixel at different exposure levels. Finally, the brightness, contrast and saturation parameters of the synthesized image are dynamically adjusted according to the real-time data of the illumination sensor to adapt to the lighting conditions of the current environment.

The color space conversion unit in the illumination adaptation processing module converts the image from the RGB color space to HSV, where H represents the color type, S represents the color purity, and V represents the color brightness. The conversion process is to find the maximum value Max and the minimum value Min in the RGB components, and calculate the brightness V, that is, the maximum value Max. When the maximum value is equal to the minimum value, the hue and saturation are both 0, otherwise, the hue H and saturation S are calculated: The saturation is:

The hue H is obtained based on the proportional relationship of the color component R, G or B corresponding to Max. When Max is the red component R, the hue H is between red and green.

The saturation adjustment unit in the light adaptation processing module first designs a monotonically increasing or decreasing function, which means that the saturation is reduced when the light intensity is low and the saturation is increased when the light intensity is high:

Math1S'＝S+k*I_light

Where k is a coefficient that adjusts the saturation according to the light intensity, and the positive or negative value determines the direction of the adjustment; S stands for the original saturation, which is a property of color that indicates the purity or intensity of the color, between 0 and 1;

I_light represents light intensity, which measures the light level in the environment in lux.

At the same time, according to the saturation limit of saturation, excessive adjustment can be prevented to cause color distortion, and a new relationship is established:

Math1S'＝clamp(S+a*I_light^b,0,1)

The clamp(x,min,max) function is used to limit the value of x between min and max, and a and b are adjustment parameters.

The local contrast enhancement model divides the image into several sub-regions, and performs segmentation based on the texture, color, gradient or other features of the image. It analyzes the characteristics of each segmented region, adjusts the contrast independently, and smoothes the image while keeping the edges sharp based on the spatial distance and pixel value similarity. According to the specific content and lighting conditions of the image, a personalized contrast enhancement function is designed, and the degree of contrast enhancement is dynamically adjusted according to the light intensity. A function is designed with the light intensity and original contrast of the current block as input, and the output is the enhanced contrast:

C_enhanced＝C_original*(1+k*I_light^n)

Among them, C_original is the original contrast, I_light is the light intensity, k and n are adjustment coefficients determined according to experimental data and visual perception model;

Finally, additional gain is applied to particularly important image areas, and finally the enhanced areas are seamlessly spliced back into the original image frame.

The adaptive algorithm design module defines the nonlinear relationship function between light intensity and holographic image brightness based on the visual characteristics of the human eye, and adopts the gamma correction transformation function:

Code1brightness_adj＝pow(original_brightness,gamma)

Among them, gamma is a parameter that is dynamically adjusted with the ambient light intensity. The brighter the environment, the larger the gamma value is, so as to reduce the brightness of the holographic image. Conversely, it increases when the ambient light is weak. When the ambient light is strong, the brightness of the holographic image is reduced to prevent overexposure. At the same time, the color saturation and contrast are adjusted.

The rendering output module processes the holographic image in real time according to the brightness adjustment value calculated by the illumination response curve, dynamically adjusts the brightness, contrast and saturation of the original image of the holographic projection source, and directly modifies the intensity of the output signal in the signal processing stage of the holographic display system, and pushes the adjusted holographic image parameters to the holographic display device in real time.

The present invention has the following beneficial effects:

1. In the present invention, by introducing and applying a local contrast enhancement algorithm, the detailed information of the image can be effectively highlighted, and the details can be highlighted to ensure that the details of the shadow area are not lost under strong light, and the details of the bright area are not ignored under weak light, thereby enhancing the overall texture of the holographic image.

2. In the present invention, the adaptive algorithm can flexibly respond to changes in lighting under different scenes, time and weather conditions, so that the drone does not need to worry about the impact of changes in lighting conditions on the quality of the holographic image when performing holographic patrol tasks, thereby improving the stability and effectiveness of the drone's work.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a system block diagram of a drone control platform capable of three-dimensional holographic patrol proposed by the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1

As shown in FIG1 , the present invention proposes a UAV control platform capable of three-dimensional holographic patrol, including an image acquisition module. The UAV holographic patrol module includes:

Light intensity detection module: collects the light intensity data of the current environment from the image data, expressed in lux units;

Lighting Adaptive Processing Module: compress or expand the dynamic range of original images and pictures;

Local contrast enhancement module: selectively enhances the contrast of important parts according to image texture features and lighting conditions, and suppresses irrelevant background noise;

Adaptive algorithm design module: Based on the visual characteristics of the human eye, it defines the nonlinear relationship function between light intensity and holographic image brightness;

Rendering output module: The processed image data is sent to the holographic projection algorithm to reconstruct and render the three-dimensional holographic image.

The light intensity detection module measures the sensor to capture the light in the environment in real time and convert it into an analog or digital signal. Based on the output voltage Vout at a specific light intensity, the sensor's spectral response curve and conversion coefficient are found according to the sensor's data sheet, which is recorded as the proportional constant K between the light intensity and the sensor output signal. The light intensity is:

I＝(Vout-Io)/K

Among them, I represents the light intensity of the current environment, and the unit is usually Lux;

Io refers to the dark current compensation value or dark voltage, which is the output voltage of the sensor under no light or very low light conditions.

The illumination adaptation processing module includes a color space conversion unit and a saturation adjustment unit. The camera based on the drone's dynamic range captures photos of multiple exposure levels in the same scene, including low-exposure dark detail photos, normal exposure photos and high-exposure bright detail photos. The pictures with different exposure levels are geometrically corrected and aligned, and then HDR synthesis is performed to merge the best brightness values of the same pixel at different exposure levels. Finally, the brightness, contrast and saturation parameters of the synthesized image are dynamically adjusted according to the real-time data of the illumination sensor to adapt to the lighting conditions of the current environment.

The color space conversion unit in the illumination adaptation processing module converts the image from the RGB color space to HSV, where H represents the color type, S represents the color purity, and V represents the color brightness. The conversion process is to find the maximum value Max and the minimum value Min in the RGB components, and calculate the brightness V, that is, the maximum value Max. When the maximum value is equal to the minimum value, the hue and saturation are both 0, otherwise, the hue H and saturation S are calculated: The saturation is:

The hue H is obtained based on the proportional relationship of the color component R, G or B corresponding to Max. When Max is the red component R, the hue H is between red and green.

In this embodiment, in HDR synthesis, the best brightness values of the same pixel under different exposure levels are merged together to retain as much dynamic range information as possible, including details of bright areas without losing details of dark areas. This is achieved through a tone mapping algorithm, which converts an HDR image that exceeds the dynamic range of a standard display device into an LDR image that can be correctly displayed on a normal display, while trying to maintain the overall visual effect of the image. By adjusting pixel brightness, contrast, saturation and other attributes, it is ensured that the image looks natural and has rich levels of detail under any lighting conditions. Finally, the brightness, contrast and saturation parameters of the synthesized image are dynamically adjusted according to the real-time data of the light sensor to adapt it to the lighting conditions of the current environment.

Secondly, when converting the image from RGB color space to HSV, the calculation method is as follows:

H＝60*((G-B)/(Max-Min))mod 360

When Max is the green component G, the hue H is between green and blue:

H＝60*((B-R)/(Max-Min)+2)mod 360

When Max is the blue component B, the hue H is between blue and red:

H＝60*((R-G)/(Max-Min)+4)mod 360

Among them, mod 360 ensures that the hue H always cycles between 0° and 360°, 0° corresponds to red, 120° corresponds to green, and 240° corresponds to blue.

Embodiment 2

As shown in FIG1 , based on the first embodiment, the saturation adjustment unit in the illumination adaptability processing module first designs a monotonically increasing or decreasing function, which represents reducing the saturation when the illumination intensity is low and increasing the saturation when the illumination intensity is high:

Math1S'＝S+k*I_light

Where k is a coefficient that adjusts the saturation according to the light intensity, and the positive or negative value determines the direction of the adjustment; S stands for the original saturation, which is a property of color that indicates the purity or intensity of the color, between 0 and 1;

I_light represents light intensity, which measures the light level in the environment in lux.

At the same time, according to the saturation limit of saturation, excessive adjustment can be prevented to cause color distortion, and a new relationship is established:

Math1S'＝clamp(S+a*I_light^b,0,1)

The clamp(x,min,max) function is used to limit the value of x between min and max, and a and b are adjustment parameters.

The local contrast enhancement model divides the image into several sub-regions, and performs segmentation based on the texture, color, gradient or other features of the image. It analyzes the characteristics of each segmented region, adjusts the contrast independently, and smoothes the image while keeping the edges sharp based on the spatial distance and pixel value similarity. According to the specific content and lighting conditions of the image, a personalized contrast enhancement function is designed, and the degree of contrast enhancement is dynamically adjusted according to the light intensity. A function is designed with the light intensity and original contrast of the current block as input, and the output is the enhanced contrast:

C_enhanced＝C_original*(1+k*I_light^n)

Among them, C_original is the original contrast, I_light is the light intensity, k and n are adjustment coefficients determined according to experimental data and visual perception model;

Finally, additional gain is applied to particularly important image areas, and finally the enhanced areas are seamlessly spliced back into the original image frame.

The adaptive algorithm design module defines the nonlinear relationship function between light intensity and holographic image brightness based on the visual characteristics of the human eye, and adopts the gamma correction transformation function:

Code1brightness_adj＝pow(original_brightness,gamma)

Among them, gamma is a parameter that is dynamically adjusted with the ambient light intensity. The brighter the environment, the larger the gamma value is, so as to reduce the brightness of the holographic image. Conversely, it increases when the ambient light is weak. When the ambient light is strong, the brightness of the holographic image is reduced to prevent overexposure. At the same time, the color saturation and contrast are adjusted.

The rendering output module processes the holographic image in real time according to the brightness adjustment value calculated by the illumination response curve, dynamically adjusts the brightness, contrast and saturation of the original image of the holographic projection source, and directly modifies the intensity of the output signal in the signal processing stage of the holographic display system, and pushes the adjusted holographic image parameters to the holographic display device in real time.

In this embodiment, the setting process of the illumination adaptive adjustment algorithm is that the illumination sensor first detects the illumination intensity of the current environment and quantifies it into a numerical representation. The illumination intensity is in Lux. The human eye's perception of brightness is not linear, which means that the same brightness increment is easier to detect under low brightness conditions and more difficult to detect under high brightness conditions. The human eye's response to brightness follows a nonlinear relationship similar to a power law, which is expressed through a gamma curve:

Code1brightness_adj=pow(original_brightness,gamma) is used to describe that gamma correction simulates the nonlinear response of the human eye to brightness by exponentially transforming the input brightness signal. For holographic images, the brightness parameters of the holographic images are adjusted through gamma correction or other similar nonlinear transformation functions according to the ambient light intensity detected by the light sensor, so that the brightness is reduced in strong light and increased in weak light, thereby maintaining good visual effects under various lighting conditions.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims (8)

1. A drone control platform capable of three-dimensional holographic patrol, comprising an image acquisition module, wherein the drone holographic patrol module comprises: Light intensity detection module: collects the light intensity data of the current environment from the image data, expressed in lux units; Lighting Adaptive Processing Module: compress or expand the dynamic range of original images and pictures; Local contrast enhancement module: selectively enhances the contrast of important parts according to image texture features and lighting conditions, and suppresses irrelevant background noise; Adaptive algorithm design module: Based on the visual characteristics of the human eye, it defines the nonlinear relationship function between light intensity and holographic image brightness; Rendering output module: The processed image data is sent to the holographic projection algorithm to reconstruct and render the three-dimensional holographic image. 2. According to the unmanned aerial vehicle control platform capable of three-dimensional holographic patrol according to claim 1, it is characterized in that the light intensity detection module measures the light in the environment captured by the sensor in real time and converts it into an analog or digital signal. Based on the output voltage Vout under a specific light intensity, the spectral response curve and the conversion coefficient are found according to the sensor's data sheet, which is recorded as the proportional constant K between the light intensity and the sensor output signal. The light intensity is: I＝(Vout-Io)/K Among them, I represents the light intensity of the current environment, and the unit is usually Lux; Io refers to the dark current compensation value or dark voltage, which is the output voltage of the sensor under no light or very low light conditions. 3. According to the drone control platform capable of three-dimensional holographic patrol according to claim 1, it is characterized in that the illumination adaptability processing module includes a color space conversion unit and a saturation adjustment unit, and the camera based on the drone state range captures photos of multiple exposure levels in the same scene, including low-exposure dark detail photos, normal exposure photos and high-exposure bright detail photos, and geometric correction and alignment are performed on the pictures with different exposure levels, and then HDR synthesis is performed to merge the best brightness values of the same pixel at different exposure levels, and finally the brightness, contrast and saturation parameters of the synthesized image are dynamically adjusted according to the real-time data of the illumination sensor to adapt to the lighting conditions of the current environment. 4. According to the unmanned aerial vehicle control platform capable of three-dimensional holographic patrol according to claim 1, it is characterized in that the color space conversion unit in the illumination adaptability processing module converts the image from the RGB color space to HSV, where H represents the color type, S represents the color purity, and V represents the brightness of the color. The conversion process is to find the maximum value Max and the minimum value Min in the RGB components, calculate the brightness V, that is, the maximum value Max, when the maximum value is equal to the minimum value, the hue and saturation are both 0, otherwise, calculate the hue H and saturation S: Saturation is: The hue H is obtained based on the proportional relationship of the color component R, G or B corresponding to Max. When Max is the red component R, the hue H is between red and green. 5. According to the unmanned aerial vehicle control platform capable of three-dimensional holographic patrol according to claim 1, it is characterized in that the saturation adjustment unit in the light adaptability processing module first designs a monotonically increasing or decreasing function, which represents that the saturation is reduced when the light intensity is low and the saturation is increased when the light intensity is high: Math1S'＝S+k*I_light Where k is a coefficient that adjusts the saturation according to the light intensity, and the positive or negative value determines the direction of the adjustment; S stands for the original saturation, which is a property of color that indicates the purity or intensity of the color, between 0 and 1; I_light represents light intensity, which measures the light level in the environment in lux. At the same time, according to the saturation limit of saturation, excessive adjustment can be prevented to cause color distortion, and a new relationship is established: Math1S'＝clamp(S+a*I_light^b,0,1) The clamp(x,min,max) function is used to limit the value of x between min and max, and a and b are adjustment parameters. 6. According to claim 1, a drone control platform capable of three-dimensional holographic patrol is characterized in that the local contrast enhancement model divides the image into several sub-areas, performs segmentation based on the texture, color, gradient or other features of the image, performs characteristic analysis on each segmented area, independently adjusts the contrast, and smoothes the image while keeping the edge sharp based on the spatial distance and pixel value similarity. According to the specific content and lighting conditions of the image, a personalized contrast enhancement function is designed, and the degree of contrast enhancement is dynamically adjusted according to the light intensity, wherein a function number is designed with the light intensity and original contrast of the current block as input, and the output is the enhanced contrast: C_enhanced＝C_original*(1+k*I_light^n) Among them, C_original is the original contrast, I_light is the light intensity, k and n are adjustment coefficients determined according to experimental data and visual perception model; Finally, additional gain is applied to particularly important image areas, and finally the enhanced areas are seamlessly spliced back into the original image frame. 7. According to claim 1, a drone control platform capable of three-dimensional holographic patrol is characterized in that the adaptive algorithm design module defines a nonlinear relationship function between light intensity and holographic image brightness based on human visual characteristics, and adopts a gamma correction transformation function: Code1brightness_adj＝pow(original_brightness,gamma) Among them, gamma is a parameter that is dynamically adjusted with the ambient light intensity. The brighter the environment, the larger the gamma value is, so as to reduce the brightness of the holographic image. Conversely, it increases when the ambient light is weak. When the ambient light is strong, the brightness of the holographic image is reduced to prevent overexposure. At the same time, the color saturation and contrast are adjusted. 8. According to the drone control platform capable of three-dimensional holographic patrol as described in claim 1, it is characterized in that the rendering output module processes the holographic image in real time according to the brightness adjustment value calculated by the illumination response curve, dynamically adjusts the brightness, contrast and saturation of the original image of the holographic projection source, and directly modifies the intensity of the output signal in the signal processing stage of the holographic display system, and pushes the adjusted holographic image parameters to the holographic display device in real time.