A method for measuring the brightness of road marking based on the imaging principle of digital camera

Abstract

A kind of road marking brightness measurement method based on the imaging principle of digital camera belongs to the field of road engineering.The present application measures the brightness of road marking by using the imaging principle of digital camera, thereby replacing the existing road marking brightness measurement method to measure the service performance of road marking.Digital camera test can not only realize the regional measurement of road marking brightness, but also reduce the influence degree of sky brightness on road marking brightness test, and can effectively improve the efficiency of road marking brightness test.The present application can not only realize the accurate expression of road marking brightness, but also save the time and fund consumption of road detection, and then efficiently guarantee the safety of traffic operation, and has important economic value and social value.

Description

Technical Field

[0001] This invention belongs to the field of road engineering, and specifically relates to a method for measuring the brightness of road markings based on the imaging principle of a digital camera. Background Technology

[0002] With the increase in my country's highway mileage, traffic safety accidents have brought enormous challenges to the development of the transportation industry. As an important component of road construction, road markings mainly play a role in guiding traffic, improving traffic capacity, and reducing the incidence of traffic accidents. Evaluating their service performance is one of the necessary means to ensure road traffic safety.

[0003] In the evaluation indicators of motor vehicle lane lighting, visibility is crucial, and luminance is one of the indicators for measuring the visibility of road markings. Currently, the luminance of road markings is mainly obtained by measuring lux meters and luminance meters. Lux meters calculate luminance values ​​using the relationship between luminance L, illuminance E, and reflectance R. However, selecting multiple sampling points for luminance measurement results in a large workload and the entire measurement process is quite cumbersome. Furthermore, outdoor measurements inevitably change due to variations in sky brightness and distribution, making this method unreliable for accurate luminance measurement. Luminance meters, on the other hand, use detectors to obtain the luminous flux value of road markings within a unit solid angle to acquire luminance. They can be divided into imaging luminance meters and aiming luminance meters. Imaging luminance meters offer fast analysis speeds and good testing results, allowing for rapid measurement and output of luminance values ​​within a specific area. However, they are expensive and not easily portable, posing significant inconvenience for practical luminance measurements. Point-by-point luminance meters measure and analyze the luminance values ​​of the target object point by point, resulting in relatively long measurement times. In practical applications, the measured data exhibits substantial errors due to variations in ambient brightness, making them incomparable and yielding unsatisfactory results. Therefore, there is an urgent need to develop a precise, convenient, technologically mature, and cost-effective method for measuring the luminance of road markings.

[0004] With the development of social science and technology, significant progress has been made in the manufacturing, image quality, and processing of digital cameras. Digital cameras can achieve overall measurement of a specific area, simultaneously acquiring brightness data from multiple points. This saves measurement time and avoids problems such as brightness changes due to prolonged operation. Based on this advantage, applying digital camera technology to road marking brightness measurement can effectively integrate road marking detection time and resources, which is of great significance for improving traffic capacity and ensuring traffic safety. Therefore, this invention proposes a road marking brightness measurement method based on the imaging principle of digital cameras. Applying the imaging principle of digital cameras to road marking brightness measurement not only achieves accurate expression of road marking brightness but also saves time and money in road inspection, thereby efficiently ensuring traffic operation safety and possessing significant economic and social value. Summary of the Invention

[0005] To achieve accurate and convenient measurement of road marking brightness, this invention provides a method for measuring road marking brightness based on the imaging principle of a digital camera.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for measuring the brightness of road markings based on the imaging principle of a digital camera, the method being:

[0008] Step 1: After light passes through the lens and enters the camera, it forms a real-time image through the lens and aperture, and is finally converted into an electrical signal, thereby establishing the relationship between illuminance E and the brightness L of the road markings;

[0009] Step 2: In actual shooting, the camera lens focal length is small enough compared to the measurement distance. At this time, the ratio of the camera lens focal length to the measurement distance is negligible, further simplifying the conversion equation between illuminance E and brightness L.

[0010] Step 3: Based on optical and imaging principles, establish a linear relationship between the logarithm lgH of the object's exposure and the grayscale value D of the captured image within the latitude range;

[0011] Step 4: Within the linear range of the photosensitivity curve, the exposure amount is determined based on the camera's exposure time and illuminance.

[0012] Step 5: Substitute the relationships obtained in Step 2 and Step 4 into the linear formula in Step 3 to obtain the conversion relationship between grayscale values ​​and brightness values;

[0013] Step 6: Convert the image grayscale value to a brightness value. Based on this formula, obtain the brightness value of a certain area or a certain pixel from the grayscale value of the digital camera image.

[0014] Step 7: Based on the model of the digital camera used, calibrate the camera brightness and output the undetermined coefficients in the linear relationship of Step 3;

[0015] Step 8: Use a digital camera to capture images of road markings and perform brightness recognition of road markings based on image processing technology.

[0016] Furthermore, in step one, the relationship between the illuminance E and the luminance L of the road markings is shown in equation (1).

[0017]

[0018] In the formula, L represents the brightness value of the road marking, cd / m 2 τ represents the optical transmittance of the imaging system; F represents the camera aperture number; f represents the lens focal length (mm); l represents the distance between the camera lens and the target object (mm).

[0019] Furthermore, in step two, the conversion equation between illuminance E and luminance L is shown in equation (2).

[0020]

[0021] Furthermore, in step three, within the tolerance range, the linear relationship between the grayscale value D of the image and the logarithm lgH of the exposure is shown in equation (3).

[0022] D = algH + b (3)

[0023] In the formula, D represents the image grayscale value; a and b represent undetermined coefficients; and H represents the exposure.

[0024] Furthermore, in step four, the relationship between exposure amount, exposure time, and illuminance is shown in equation (4).

[0025] H = Et (4)

[0026] Where H represents exposure; E represents illuminance; and t represents exposure time.

[0027] Furthermore, in step five, the relationship between the grayscale value and the brightness value is shown in equation (5).

[0028]

[0029] Furthermore, in step six, the method for converting image grayscale values ​​into brightness values ​​is shown in equation (6).

[0030]

[0031] Further, in step seven, the F and t values ​​of the digital camera are adjusted in different environments to measure the ambient brightness. The exposure H is calculated according to the relationship between exposure and brightness. Then, the R, G, and B values ​​of the acquired image are output using digital image processing software. The conversion relationship between grayscale value and three primary colors is calculated using formula (7). On this basis, a scatter plot of the logarithm of grayscale value and exposure value is output. Data fitting is performed using Origin to find the values ​​of the undetermined coefficients a and b, thereby completing the calibration.

[0032] D=0.299R+0.587G+0.114B (7).

[0033] Furthermore, in step eight, when using a digital camera to acquire images, the aperture size and shutter speed are fixed for each set of images. After acquiring the marking images using a digital camera, the RGB values ​​of the marking positions in the images are extracted using Photoshop.

[0034] The advantages of this invention over the prior art are as follows:

[0035] 1. This invention utilizes the imaging principle of a digital camera to measure the brightness of road markings, thereby replacing existing methods for measuring road marking brightness and evaluating the service performance of road markings. Digital camera testing not only enables regional measurement of road marking brightness but also reduces the impact of sky brightness on road marking brightness testing, effectively improving the efficiency of road marking brightness testing.

[0036] 2. This invention can not only accurately express the brightness of road markings, but also save time and money in road inspection, thereby efficiently ensuring traffic operation safety and having significant economic and social value. Attached Figure Description

[0037] Figure 1 This is a graph showing the photosensitivity characteristics.

[0038] Figure 2 A curve showing the fit between image exposure and grayscale value;

[0039] Figure 3 Image information acquisition diagram;

[0040] Figure 4 This is a diagram showing the changes in retroreflection of different types of markings under wear conditions.

[0041] Figure 5 This is a graph showing the brightness changes of different types of markings under wear.

[0042] Figure 6 This is a graph showing the brightness variation of different types of markings at different viewing distances. Detailed Implementation

[0043] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0044] Example 1:

[0045] Step 1: After light passes through the lens and enters the camera, it forms a "real-time image" through the lens and aperture, and is finally converted into an electrical signal. The illuminance E and the brightness L of the road markings have the relationship shown in equation (1).

[0046]

[0047] In the formula, L represents the road marking brightness value (cd / m²). 2); τ represents the optical transmittance of the imaging system; F represents the camera aperture number; f represents the lens focal length (mm); l represents the distance between the camera lens and the target object (mm).

[0048] Step 2: In actual shooting, the focal length of the camera lens is small enough compared to the measurement distance. At this time, the ratio of the focal length of the camera lens to the measurement distance can be ignored. Therefore, the above formula can be converted into formula (2).

[0049]

[0050] Step 3: Based on optical and imaging principles, there exists a relationship between the object's exposure H and the grayscale value D of the captured image. Figure 1 The relationship is shown in the figure. Segments 1-2 and 3-4 are curved, indicating underexposure or overexposure within these intervals. In these segments, there is no clear linear relationship between the image's grayscale and the logarithm of exposure (lgH), therefore grayscale-brightness fitting cannot be performed. In the 2-3 interval, there is a relatively clear linear relationship. The range of variation on the horizontal axis in segment 2-3 is defined as the latitude; therefore, segment 2-3 can be used to calculate brightness values ​​with high accuracy.

[0051] Within the tolerance range, there is a linear relationship between the grayscale value of the image and the logarithm of the exposure, lgH, as shown in equation (3).

[0052] D = a lgH + b (3)

[0053] In the formula, D represents the image grayscale value; a and b represent undetermined coefficients; and H represents the exposure.

[0054] Step 4: Within the linear range of the photosensitive characteristic curve, the exposure can be determined by the exposure time and illuminance of the camera, as shown in equation (4).

[0055] H = E t (4)

[0056] Where H represents exposure; E represents illuminance; and t represents exposure time.

[0057] Step 5: Substituting equations (2) and (4) into equation (3) yields equation (5) which shows the relationship between grayscale value and brightness value.

[0058]

[0059] Step 6: The calculation method for converting image grayscale values ​​into brightness values ​​is shown in Equation (6). Based on this formula, the brightness value of a certain area or a certain pixel can be obtained from the grayscale values ​​of the digital camera image.

[0060]

[0061] Step 7: The digital camera used is a Sony α7RⅢ, and the lens is a Tamron 28-200mm. Specific parameters are shown in Table 1.

[0062] Table 1. Specifications of Sony α7RⅢ Camera and Lens

[0063]

[0064] The F and t values ​​of a digital camera were adjusted under different environments. The ambient brightness was measured using a TES137 luminance meter. The exposure H was calculated based on the relationship between exposure and brightness. Then, the R, G, and B values ​​of the acquired images were output using digital image processing software. The conversion relationship between grayscale values ​​and the three primary colors was calculated using formula (7). Based on this, a scatter plot of the logarithm of grayscale values ​​and exposure values ​​was output, and data fitting was performed using Origin. Figure 2 As shown, the values ​​of the undetermined coefficients a and b are obtained, thus completing the calibration.

[0065] D = 0.299R + 0.587G + 0.114B (7)

[0066] During calibration, to minimize the impact of external light on brightness values, the calibration should be conducted in a completely dark environment. The flash function of the digital camera should be turned off during the shooting process to ensure that all brightness information throughout the entire test and calibration process is emitted by a light source with uniform brightness.

[0067] In the image, the grayscale value ranges from approximately 35 to approximately 220. The grayscale value of the image has a good linear relationship with the logarithm of the exposure. The images taken by the camera in this area are basically similar to segments 2-3 of the photosensitive curve and can be used for brightness measurement. According to the Origin fitting data, the undetermined coefficients a = 207.15 and b = 110.10. Therefore, based on this, for image shooting under different environments, the aperture F and exposure time t are recorded, and the grayscale value of the target object can be calculated using equation (7).

[0068] Step 8: Use a digital camera to capture images of road markings, such as... Figure 3 As shown, since the driver's visual height and headlight position vary depending on the vehicle model, this embodiment selects a sedan for research, with the specific driver's visual height and headlight height being 1.3m and 0.7m, respectively. To ensure measurement accuracy, when acquiring images using a digital camera, the aperture size and shutter speed are fixed for each image set. After acquiring the marking images using a digital camera, the RGB values ​​of the marking positions in the images are extracted using Photoshop.

[0069] To simulate the wear and tear on road markings caused by lane changes and snowplows during actual use, a wheel-type polisher was used to conduct wear tests on the road markings. The results of digital camera measurements of road marking brightness and retroreflection coefficient tests are shown below. Figure 4 and 5 As shown in the figure, the retroreflectance coefficient of the gradation line is basically consistent with the change in brightness, showing a good correlation, which indicates the accuracy of the gradation line brightness measurement method based on digital camera.

[0070] Example 2:

[0071] This embodiment differs from Embodiment 1 in that a van was selected for the study, with the driver's visual height and headlight height being 1.6m and 0.9m respectively. The test results for road marking brightness under different visual conditions are as follows: Figure 6 As shown in the figure. Measurements at 20m showed that the brightness of all four types of road markings was 900 cd / m². 2 Therefore, the 20m outer marking line is visible.

Claims

1. A method for measuring the brightness of road markings based on the imaging principle of a digital camera, characterized in that: The method is as follows: Step 1: After light passes through the lens and enters the camera, it forms a real-time image through the lens and aperture, and is finally converted into an electrical signal, thereby establishing the illumination. brightness of road markings The relationship; the illuminance brightness of road markings The relationship is shown in equation (1). （1） In the formula, This indicates the brightness value of road markings, in cd / m. 2 ; Indicates the optical transmittance of the imaging system; Indicates the camera's aperture number; Indicates lens focal length, in mm; This indicates the distance between the camera lens and the target object, in mm; Step Two: In actual shooting, the camera lens focal length is sufficiently small compared to the measurement distance. Therefore, the ratio of the camera lens focal length to the measurement distance can be disregarded, further simplifying the illumination calculation. With brightness The conversion equation; the illuminance With brightness The transformation equation is shown in equation (2). （2） Step 3: Based on optical and imaging principles, establish the logarithm of the object's exposure. Compared with the grayscale value of the captured image Linear relationship within the tolerance range; grayscale values ​​of the image within the tolerance range. Logarithm of exposure The linear relationship between them is shown in equation (3). （3） In the formula, Represents the image grayscale value; a and b represent undetermined coefficients. Indicates exposure volume; Step 4: Within the linear range of the photosensitivity curve, the exposure amount is determined based on the camera's exposure time and illuminance. Step 5: Substitute the relationships obtained in Step 2 and Step 4 into the linear formula in Step 3 to obtain the conversion relationship between grayscale values ​​and brightness values; Step 6: Convert the image grayscale value to a brightness value. Based on this formula, obtain the brightness value of a certain area or a certain pixel from the grayscale value of the digital camera image. Step 7: Based on the model of the digital camera used, calibrate the camera brightness and output the undetermined coefficients in the linear relationship of Step 3; Step 8: Use a digital camera to capture images of road markings and perform brightness recognition of road markings based on image processing technology.

2. The method for measuring the brightness of road markings based on the imaging principle of a digital camera according to claim 1, characterized in that: In step four, the relationship between exposure amount, exposure time, and illuminance is shown in equation (4). （4） in, Indicates exposure volume; Indicates illuminance value; Indicates the exposure time.

3. The method for measuring the brightness of road markings based on the imaging principle of a digital camera according to claim 1, characterized in that: In step five, the relationship between the grayscale value and the brightness value is shown in equation (5). （5）。 4. The method for measuring the brightness of road markings based on the imaging principle of a digital camera according to claim 1, characterized in that: In step six, the method for converting image grayscale values ​​into brightness values ​​is shown in equation (6). （6）。 5. The method for measuring the brightness of road markings based on the imaging principle of a digital camera according to claim 1, characterized in that: In step seven, a digital camera is used to adjust the settings under different environments. , The value is obtained by measuring ambient brightness and calculating the exposure based on the relationship between exposure and brightness. Then, the brightness values ​​of the collected images are output using digital image processing software to output the R, G, and B values ​​of the images. The conversion relationship between gray values ​​and the three primary colors is calculated using formula (7). Based on this, a scatter plot of the logarithm of gray values ​​and exposure values ​​is output. Data fitting is performed using Origin to find the values ​​of the undetermined coefficients a and b, thereby completing the calibration. （7）。 6. The method for measuring the brightness of road markings based on the imaging principle of a digital camera according to claim 1, characterized in that: In step eight, when using a digital camera to acquire images, the aperture size and shutter speed are fixed for each set of images. After acquiring the marking images using the digital camera, the RGB values ​​of the marking positions in the images are extracted using Photoshop.

Images