A Dynamic Dimming Method and System Based on Illuminance Sensing of Dual-Channel Cameras

Abstract

This application belongs to the field of lighting control technology, and discloses a dynamic dimming method and system based on dual-channel camera illuminance sensing. Through tunnel pavement grid division and dual-channel image processing, combined with dynamic target detection and real-time illuminance calculation, it realizes precise dimming control based on vehicle speed and ambient light, without the need to deploy independent dedicated illuminance sensors for illuminance detection. It has the advantages of improving the accuracy and energy-saving effect of tunnel lighting dimming control, realizing stable illuminance perception under all light conditions, and reducing the hardware deployment and maintenance costs.

Description

Technical Field

[0001] This application relates to the technical field of lighting control. Specifically, it relates to a dynamic dimming method and system based on dual-channel camera illuminance sensing. Background Art

[0002] In intelligent lighting control systems for scenarios such as highway tunnels, lamp dimming needs to comprehensively consider multi-dimensional parameters such as traffic flow, vehicle speed, external illuminance of the tunnel, and internal illuminance of the tunnel. Current mainstream technologies usually use cameras to detect traffic flow and vehicle speed, and at the same time rely on independently deployed dedicated illuminance sensors (such as photoresistors, silicon photocells, etc.) to detect the illuminance inside and outside the tunnel. However, such dedicated sensors have significant defects: on the one hand, their single-point detection mode is difficult to cover the complex light gradients inside the tunnel (such as the strong light area at the entrance, the middle shadow area, and the exit transition area), which is likely to lead to the formation of local illuminance monitoring blind spots; on the other hand, the installation and maintenance costs of independent sensors are relatively high, and there is hardware redundancy with the video monitoring system, which does not conform to the development trend of the integration of intelligent devices. In fact, as the core device of video monitoring, the imaging pixel value of a camera has a clear linear response relationship with the environmental illuminance and fully has the potential to synchronously sense illuminance, but it has not been fully utilized in existing systems.

[0003] Although visible light cameras can provide rich color and detail information, their imaging performance highly depends on the environmental light: in low illuminance scenarios (such as at night or in the middle of the tunnel), the image signal-to-noise ratio significantly decreases, and even the "black screen" phenomenon occurs; in strong light scenarios (such as direct sunlight at the tunnel entrance), it is easy to cause overexposure due to pixel saturation and lose the illuminance information of key areas. Infrared cameras can achieve clear imaging in a completely dark environment, but the grayscale images they output lack color information, and the detection effect on objects with low infrared light reflectivity (such as dark-colored vehicles) is not good. In the prior art, single-channel cameras (only visible light or only infrared) are limited by the spectral response range and cannot stably sense the illuminance of the target and the environment under full lighting conditions. For example, the strong light at the tunnel entrance may cause the visible light camera to be overexposed and distorted, while the infrared images in the area without light inside the tunnel are difficult to accurately reflect the actual illuminance level perceived by the human eye. Therefore, dual-channel cameras play an important role in all-weather target detection.

[0004] The prior art also has the following problems: insufficient spatio-temporal alignment accuracy of cross-spectral images leads to illuminance calculation errors; there is a lack of collaborative optimization between dynamic target detection and illuminance perception; the coupling degree between vehicle speed estimation and illuminance adjustment strategies is low; and it is impossible to achieve refined dimming control based on grid management. These problems seriously restrict the intelligent level and energy-saving effect of the tunnel lighting system.

[0005] In view of the above problems, the prior art urgently needs to be improved. Summary of the Invention

[0006] The purpose of this application is to provide a dynamic dimming method and system based on dual-channel camera illuminance sensing, which has the advantages of improving the accuracy and energy-saving effect of tunnel lighting dimming control, realizing stable illuminance perception under full light conditions, and reducing the hardware deployment and maintenance costs.

[0007] In the first aspect, this application provides a dynamic dimming method based on dual-channel camera illuminance sensing for dimming control of lighting fixtures in a tunnel based on a dual-channel camera with a visible light channel and an infrared channel, including the steps of:

[0008] A1. Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture;

[0009] A2. Collect dual-channel images of the tunnel road surface through the dual-channel camera, and perform preprocessing and cross-spectrum spatio-temporal alignment on the dual-channel images to obtain an affine transformation matrix for spatio-temporal alignment and the dual-channel images after spatio-temporal alignment; the dual-channel images include a visible light channel image and an infrared channel image;

[0010] A3. Based on the dual-channel images after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix;

[0011] A4. Evaluate the average vehicle speed of each grid according to the dynamic target detection result;

[0012] A5. Calculate the real-time illuminance value of each grid according to the dual-channel images after spatio-temporal alignment and the affine transformation matrix after time compensation; the real-time illuminance value includes a visible light illuminance value and an infrared illuminance value;

[0013] A6. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the real-time illuminance value and the average vehicle speed evaluation result.

[0014] Preferably, in step A2, performing cross-spectrum spatio-temporal alignment on the dual-channel images includes:

[0015] Using the preprocessed visible light channel image and infrared channel image as the target visible light image and target infrared image, perform SIFT feature point matching on the target visible light image and target infrared image to obtain a preliminary affine transformation matrix;

[0016] Estimate the global average motion vector of the target visible light image based on the optical flow method to correct the translation parameter of the preliminary affine transformation matrix to obtain a corrected affine transformation matrix;

[0017] With the goal of maximizing the normalized mutual information and minimizing the SIFT feature point matching error, an objective function related to the affine transformation matrix is established; the normalized mutual information is related to the entropy and mutual information of the target visible light image and the target infrared image;

[0018] Using the corrected affine transformation matrix as the initial value of the affine transformation matrix, the objective function is iteratively solved by the gradient ascent method to obtain the final affine transformation matrix;

[0019] The target infrared image is affine-transformed using the final affine transformation matrix, and the affine result is stitched with the target visible light image to obtain a spatio-temporally aligned dual-channel image.

[0020] Preferably, step A3 includes:

[0021] A301. Using the improved YOLOv11 model to perform object detection on the spatio-temporally aligned dual-channel image to obtain the object detection bounding box and ReID feature vector of the vehicle;

[0022] A302. Based on the optical flow method, estimate the average motion vector of the object region enclosed by the object detection bounding box to correct the translation parameter of the affine transformation matrix and obtain the time-compensated affine transformation matrix.

[0023] Preferably, step A4 includes:

[0024] A401. According to the object detection bounding box and the ReID feature vector, use the Deepsort algorithm to perform object tracking on each vehicle to obtain the tracking trajectory;

[0025] A402. Calculate the speed of each vehicle according to the tracking trajectory;

[0026] A403. For each grid, perform a weighted average operation on the speeds of the vehicles on all lanes passing through this grid to obtain the average vehicle speed of this grid.

[0027] Preferably, in step A403, the average vehicle speed of the grid is calculated by the following formula:

[0028] ;

[0029] ;

[0030] where, is the average vehicle speed of the grid, is the speed of the i-th vehicle on all lanes of this grid, is 's weight, N is the total number of vehicles on all lanes of this grid, is the average speed of the lanes on all lanes of this grid, is the standard deviation of the speeds of the vehicles on all lanes of the grid.

[0031] Preferably, step A5 includes:

[0032] A501. For each grid, calculate the visible light illumination value of each grid according to the average pixel value of the visible light channel image within the grid after spatio-temporal alignment, the linear conversion coefficient from the visible light pixel value to the illuminance, and the preset ambient background illuminance compensation value;

[0033] A502. For each grid, perform an affine transformation on the infrared channel image after spatio-temporal alignment using the affine transformation matrix after time compensation, and calculate the infrared illuminance value corresponding to the average pixel value of the affine transformation result within the grid to obtain the initial infrared illuminance value;

[0034] A503. For each grid, compare the average pixel value of the visible light channel image within the grid after spatio-temporal alignment with the preset pixel value threshold, determine the dynamic infrared compensation weight, and combine with the initial infrared illuminance value to calculate the final infrared illuminance value of each grid.

[0035] Preferably, step A6 includes:

[0036] A601. For each grid, calculate the dimming intensity of each grid according to the preset reference dimming intensity, the ratio of the average vehicle speed of the grid to the maximum designed vehicle speed of the tunnel, the preset speed influence coefficient, the preset tunnel lighting required illuminance, and the maximum value of the visible light illumination value and the infrared illuminance value of the grid;

[0037] A602. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the dimming intensity of each grid.

[0038] Preferably, in step A601, calculate the dimming intensity of each grid according to the following formula:

[0039] ;

[0040] wherein, is the dimming intensity of the j-th grid, is the reference dimming intensity, is the average vehicle speed of the j-th grid, is the maximum designed vehicle speed of the tunnel, is the speed influence coefficient, is the tunnel lighting required illuminance, is the visible light illumination value of the j-th grid, is the infrared illuminance value of the j-th grid.

[0041] Preferably, in step A501, the visible light illumination value of the grid containing the vehicle with the headlights on is set to invalid;

[0042] In step A601, if the visible light illumination value of a grid is invalid, the maximum value of the visible light illumination value and the infrared illumination value of the grid is the infrared illumination value.

[0043] In a second aspect, the present application provides a dynamic dimming system based on dual-channel camera illuminance sensing, including a dual-channel camera with a visible light channel and an infrared channel, a plurality of lighting fixtures arranged above the tunnel road surface, and a control terminal;

[0044] The dual-channel camera is used to collect dual-channel images of the tunnel road surface and upload them to the control terminal; the dual-channel images include visible light channel images and infrared channel images;

[0045] The control terminal is used to execute:

[0046] Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture;

[0047] Preprocess and perform cross-spectrum spatio-temporal alignment on the dual-channel images to obtain an affine transformation matrix for spatio-temporal alignment and the dual-channel images after spatio-temporal alignment; the dual-channel images include visible light channel images and infrared channel images;

[0048] Based on the dual-channel images after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix;

[0049] Evaluate the average vehicle speed of each grid according to the dynamic target detection result;

[0050] Calculate the real-time illuminance value of each grid according to the dual-channel images after spatio-temporal alignment and the affine transformation matrix after time compensation; the real-time illuminance value includes visible light illumination value and infrared illumination value;

[0051] Adjust the light intensity of the lighting fixtures corresponding to each grid according to the real-time illuminance value and the average vehicle speed evaluation result.

[0052] Beneficial effects: The dynamic dimming method and system based on dual-channel camera illuminance sensing provided by the present application realize precise dimming control based on vehicle speed and ambient light through tunnel road surface grid division and dual-channel image processing, combined with dynamic target detection and real-time illuminance calculation, without the need to deploy independent dedicated illuminance sensors for illuminance detection, having the advantages of improving the accuracy and energy-saving effect of tunnel lighting dimming control, realizing stable illuminance perception under all lighting conditions, and reducing the hardware deployment and maintenance costs. Description of the Drawings

[0053] Figure 1 This is a flowchart of the dynamic dimming method based on dual-channel camera illuminance sensing provided by the embodiments of the present application.

[0054] Figure 2 This is a schematic structural diagram of the dynamic dimming system based on dual-channel camera illuminance sensing provided by the embodiments of the application.

[0055] Label description: 1. Dual-channel camera; 2. Lighting fixture; 3. Control terminal. Specific implementation manners

[0056] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0057] It should be noted that: Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0058] Please refer to Figure 1 , a dynamic dimming method based on dual-channel camera illuminance sensing in some embodiments of the present application, which is used to perform dimming control on the lighting fixtures in the tunnel based on a dual-channel camera with a visible light channel and an infrared channel (preferably a near-infrared channel), and includes the steps:

[0059] A1. Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture;

[0060] A2. Collect dual-channel images of the tunnel road surface through the dual-channel camera, and perform preprocessing and cross-spectrum spatio-temporal alignment on the dual-channel images to obtain an affine transformation matrix for spatio-temporal alignment and the dual-channel images after spatio-temporal alignment; the dual-channel images include visible light channel images and infrared channel images;

[0061] A3. Based on the dual-channel images after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix;

[0062] A4. Evaluate the average vehicle speed of each grid according to the dynamic target detection results;

[0063] A5. Calculate the real-time illuminance values of each grid according to the dual-channel images after spatio-temporal alignment and the affine transformation matrix after time compensation; the real-time illuminance values include visible light illuminance values and infrared illuminance values;

[0064] A6. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the real-time illuminance values and the average vehicle speed evaluation results.

[0065] In some embodiments, the method can be implemented based on Figure 2 the dynamic dimming system based on dual-channel camera illuminance sensing shown.

[0066] Among them, the division of the tunnel road surface into grids refers to dividing the tunnel road surface into multiple grid units according to the coverage range of each lighting fixture in the tunnel. Each grid unit corresponds to at least one control area of a lighting fixture. Specifically, it can be implemented by a spatial division method based on the installation position and coverage radius of the lighting fixture, such as a rectangular grid or a hexagonal grid. This division realizes the regional pertinence of dimming control and eliminates the single-point detection blind area.

[0067] Among them, cross-spectrum spatio-temporal alignment refers to performing geometric transformation and time synchronization processing on visible light and infrared dual-channel images. Specifically, it can be implemented by an affine transformation matrix optimization method combining SIFT feature point matching and optical flow method. This operation solves the spatial misalignment problem caused by the spectral response difference and sampling time difference of the dual-channel camera, and provides a spatio-temporal consistency basis for multi-spectral data fusion.

[0068] Among them, dynamic target detection refers to identifying the position and motion state of moving vehicles in the tunnel. Specifically, it can be implemented by an improved YOLOv11 model combined with a Deepsort tracking algorithm. This detection provides target motion trajectory data for vehicle speed evaluation and supports the calculation of dynamic dimming parameters.

[0069] Among them, the average vehicle speed evaluation refers to statistically calculating the weighted average of the vehicle speeds related to each grid. Specifically, it can be implemented by a speed calculation based on the tracking trajectory combined with a standard deviation weight adjustment method. This evaluation reflects the dynamic change of the lighting demand caused by vehicle passage and serves as the basis for adjusting the dimming intensity.

[0070] Among them, the real-time illuminance value calculation refers to separately extracting the visible light and infrared channel image data and converting them into physical illuminance values. Specifically, it can be implemented by a pixel value linear conversion combined with a dynamic infrared compensation weight optimization method. This calculation utilizes the complementary characteristics of the dual-channel spectrum and preferentially selects effective illuminance data in overexposed or low illuminance scenarios.

[0071] Among them, the dimming intensity adjustment refers to dynamically controlling the output light intensity of the lamp according to the illuminance and vehicle speed parameters, and specifically, it can be implemented by a formulaic calculation method combining the reference intensity with the speed proportionality coefficient and the illuminance deviation compensation. This adjustment realizes the real-time matching of the illumination intensity and the vehicle passing demand, replacing the function of the independent illuminance sensor.

[0072] The core innovation of this application lies in the integration of the collaborative perception of the dual-channel camera and the dynamic dimming strategy, synchronously solving the problems of hardware redundancy, single-channel perception limitation, and local monitoring blind area in the traditional tunnel lighting system. Specifically, it is manifested as: realizing all-weather illuminance perception by using the spectral complementarity of the visible light and infrared channels, eliminating the dual-channel data deviation through cross-spectral spatio-temporal alignment and dynamic compensation, combining grid-based area control and vehicle speed dynamic parameter optimization of the dimming strategy, and forming an integrated intelligent lighting control scheme that does not rely on independent sensors.

[0073] The working process and principle of this application are as follows: First, obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel, and each grid corresponds to at least one lighting fixture. This grid division method ensures the regional pertinence of the dimming control and avoids the blind area problem of single-point detection. Then, collect the dual-channel images of the tunnel road surface through the dual-channel camera, including the visible light channel image and the infrared channel image. Preprocess these images and perform cross-spectral spatio-temporal alignment to obtain the affine transformation matrix for spatio-temporal alignment and the dual-channel image after spatio-temporal alignment. This step eliminates the spatio-temporal deviation caused by spectral differences and provides a geometrically consistent dual-channel data basis for subsequent fusion analysis. Based on the dual-channel image after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel, and at the same time perform time compensation on the affine transformation matrix. This improves the spatio-temporal consistency of vehicle detection and avoids tracking deviation caused by the dynamic error of the camera. Then, evaluate the average vehicle speed of each grid according to the dynamic target detection result. This takes the vehicle speed as an important dynamic parameter of the dimming intensity, reflecting the real-time change of the lighting demand for vehicle passing in different regions. Calculate the real-time illuminance values of each grid according to the dual-channel image after spatio-temporal alignment and the affine transformation matrix after time compensation, including the visible light illuminance value and the infrared illuminance value. This dual-channel real-time illuminance calculation utilizes the spectral complementarity characteristics of the visible light and infrared channels, and preferentially selects the effective illuminance value in strong light or low illuminance scenarios respectively, overcoming the imaging defects of a single channel. Finally, adjust the light intensity of the lighting fixtures corresponding to each grid according to the real-time illuminance value and the average vehicle speed evaluation result. This realizes the fine matching of the illumination intensity in the tunnel and the vehicle passing demand, and improves the real-time performance and adaptability of the lighting control while eliminating the dependence on independent sensors.

[0074] Through the above solution, the present application realizes the illuminance perception of the entire tunnel area based on a dual-channel camera, overcoming the problem of local illuminance monitoring blind spots caused by the single-point detection mode. Through the coupling of grid illuminance perception and dynamic dimming strategy, the accuracy and real-time performance of tunnel lighting control are improved. At the same time, the reuse of camera functions eliminates hardware redundancy and reduces the system deployment and maintenance costs. The cross-spectral fusion of dual-channel images enhances the robustness of illuminance perception, and reliable illuminance data can be obtained in both strong light and low illuminance scenarios. The introduction of dynamic target detection and vehicle speed assessment enables the lighting control to better adapt to traffic flow changes and improves the intelligent level of the system. Overall, this solution significantly improves the tunnel lighting effect, enhances driving safety, and realizes the efficient utilization of energy.

[0075] Preferably, the preprocessing in step A2 includes data synchronization of the visible light channel image and the infrared channel image, median filtering and CLAHE enhancement processing of the visible light channel image, and Gaussian-Laplacian sharpening processing of the infrared channel image.

[0076] Among them, the data synchronization is achieved by a hardware trigger signal combined with a timestamp calibration method (such as calibration by interpolation). The preprocessing sequence follows the logical process of noise reduction first and then enhancement. The data synchronization establishes a timing reference for subsequent processing, and the differential processing of the visible light and infrared channels forms a complementarity for their respective spectral characteristics.

[0077] Through the above technical solution, the present application realizes the effective preprocessing of dual-channel images. The data synchronization eliminates the time misalignment problem caused by hardware delay or transmission difference, and establishes a unified time reference for subsequent spatio-temporal alignment. The median filtering and CLAHE enhancement processing improve the anti-noise ability and local contrast of the visible light image, adapting to the brightness differences in different areas of the tunnel. The Gaussian-Laplacian sharpening processing enhances the edge details of the infrared image while suppressing thermal noise. The combined application of these preprocessing steps enables the visible light and infrared channel images to achieve complementary optimization in the three dimensions of noise reduction, contrast, and detail clarity, providing high-quality input data for subsequent cross-spectral spatio-temporal alignment, thereby improving the accuracy of spatio-temporal alignment and the accuracy of real-time illuminance value calculation.

[0078] In some embodiments, in step A2, the cross-spectral spatio-temporal alignment of the dual-channel images includes:

[0079] Using the preprocessed visible light channel image and infrared channel image as the target visible light image and target infrared image, performing SIFT feature point matching on the target visible light image and target infrared image to obtain a preliminary affine transformation matrix;

[0080] Estimate the global average motion vector of the target visible light image based on the optical flow method, and use it to correct the translation parameter of the preliminary affine transformation matrix to obtain the corrected affine transformation matrix;

[0081] Establish an objective function related to the affine transformation matrix with the goal of maximizing the normalized mutual information and minimizing the SIFT feature point matching error; the normalized mutual information is related to the entropy and mutual information of the target visible light image and the target infrared image;

[0082] Use the corrected affine transformation matrix as the initial value of the affine transformation matrix, and iteratively solve the objective function by the gradient ascent method to obtain the final affine transformation matrix;

[0083] Perform an affine transformation on the target infrared image using the final affine transformation matrix, and splice the affine result with the target visible light image to obtain the spatio-temporally aligned dual-channel image.

[0084] Specifically, first extract the feature points of the visible light and infrared images through the SIFT algorithm to overcome the matching difficulties caused by the texture differences of cross-spectral images and generate a preliminary geometric transformation relationship. Subsequently, introduce the optical flow method to analyze the global motion trend of the visible light image and dynamically correct the translation deviation caused by camera jitter or vehicle movement. For example, in a tunnel scene, the image smear error caused by the vehicle passing quickly can be corrected. When constructing the multi-objective optimization function, the normalized mutual information effectively quantifies the statistical correlation between spectra by calculating the ratio of the information entropy and joint entropy of the two images. For example, in the strong light area at the tunnel entrance, the overexposed area of the visible light image can supplement the statistical features through the high dynamic range information of the infrared image. The gradient ascent method iteratively updates through the gradient direction in the parameter space. For example, based on the corrected affine transformation matrix, the rotation, scaling, and translation parameters of the affine transformation matrix are adjusted simultaneously in each iteration to balance the minimization of geometric error and the maximization of statistical correlation. The finally generated affine transformation matrix accurately maps the infrared image to the visible light coordinate system, realizing pixel-level alignment of the two-channel images and providing a geometrically consistent dual-channel data basis for subsequent illuminance calculation.

[0085] Among them, the SIFT feature point matching process is a prior art and will not be elaborated here. The obtained preliminary affine transformation matrix can be expressed as:

[0086] ;

[0087] Among them, is the preliminary affine transformation matrix, , are the scaling factors of the preliminary affine transformation matrix, , are the shear factors of the preliminary affine transformation matrix, , is the translation parameter of the preliminary affine transformation matrix.

[0088] Among them, when estimating the global average motion vector of the target visible light image based on the optical flow method, according to M consecutive frames of target visible light images (M can be set according to actual needs), the global average motion vector can be calculated using the following formula:

[0089] ;

[0090] ;

[0091] Among them, is the horizontal component in the global average motion vector, is the vertical component in the global average motion vector, is the inter-frame horizontal displacement of the pixel point (x, y), is the inter-frame vertical displacement of the pixel point (x, y).

[0092] Among them, and can be obtained in the following way: First, use the Sobel operator to calculate the spatial gradient and temporal gradient between adjacent frames of target visible light images, construct the spatial gradient matrix A and the temporal gradient matrix b, and then calculate the inter-frame horizontal displacement matrix and the inter-frame vertical displacement matrix according to the following formula, and extract and from the inter-frame horizontal displacement matrix and the inter-frame vertical displacement matrix. Specifically, extract the (x, y)-th element of the inter-frame horizontal displacement matrix and the inter-frame vertical displacement matrix to obtain and :

[0093] ;

[0094] Among them, is the inter-frame horizontal displacement matrix, is the inter-frame vertical displacement matrix.

[0095] Among them, the corrected affine transformation matrix can be expressed as:

[0096] .

[0097] Optionally, the objective function can be:

[0098] ;

[0099] is the objective function, represents the affine transformation matrix, is the target visible light image, is the target infrared image, represents the result of the affine transformation using to perform an affine transformation on The affine transformation result, is and The global similarity between, are the SIFT feature points of the target visible light image, is The SIFT feature points of, is the weight coefficient, used to balance statistical dependence and geometric consistency.

[0100] Among them, , is and The mutual information between (the calculation method of mutual information is the prior art and will not be elaborated here), is The entropy of (the calculation method of the entropy of the image is the prior art and will not be elaborated here), is The entropy of.

[0101] Among them, can be adaptively valued based on the feature point density, for example, calculated by the following formula: , is the preset reference value (can be adjusted according to actual needs, for example, 0.5), is and The number of pairs of successfully matched SIFT feature points between, is and The total number of SIFT feature points of.

[0102] When iteratively solving the objective function by the gradient ascent method, iterative updates are performed based on the corrected affine transformation matrix. Each iteration calculates the gradient of the objective function with respect to the parameters of the affine transformation matrix (scaling factor, shear factor, translation parameter), multiplies the gradient by the preset learning efficiency, and adds it to the parameters of the affine transformation matrix in the previous step to obtain the affine transformation matrix for this iteration update. Iterate in this way until the objective function converges to obtain the final affine transformation matrix.

[0103] Among them, using the final affine transformation matrix to perform an affine transformation on the target infrared image and splicing the affine result with the target visible light image to obtain a spatio-temporally aligned dual-channel image can be expressed by the following formula: , where represents the spatio-temporally aligned dual-channel image obtained by splicing, is the splicing function, is the final affine transformation matrix, represents the affine transformation result obtained by performing an affine transformation on using the final affine transformation matrix.

[0104] In some embodiments, step A3 includes:

[0105] A301. Using the improved YOLOv11 model to perform object detection on the spatio-temporally aligned dual-channel image to obtain the object detection bounding box of the vehicle and the ReID feature vector;

[0106] A302. Estimating the average motion vector of the target region enclosed by the object detection bounding box based on the optical flow method to correct the translation parameter of the affine transformation matrix (referring to the final affine transformation matrix in the previous text) to obtain the time-compensated affine transformation matrix.

[0107] Through the above technical solutions, the present application realizes accurate vehicle detection and tracking under complex lighting conditions. The improved YOLOv11 model makes full use of the dual-channel image information, improving the robustness of detection. The motion estimation based on the optical flow method and the affine transformation matrix compensation effectively eliminate the spatio-temporal deviation caused by vehicle motion and camera jitter, ensuring the accuracy of subsequent illuminance calculation. The introduction of the ReID feature vector provides a reliable data association basis for multi-object tracking, improving the overall performance of the system.

[0108] Among them, in the input end of the YOLOv11 backbone network of the improved YOLOv11 model, a dynamic spectral adaptive weight (DSAW) module is added to achieve: quantifying the global response intensity of each channel through global average pooling; dynamically allocating the weights of the visible light channel and the infrared channel through a fully connected layer combined with a non-linear activation function; ensuring weight normalization through Sigmoid output to avoid a single channel dominating or weakening; finally outputting the weighted and fused feature map; expressed by the formula:

[0109] ;

[0110] where, H is the image height, W is the image width, c is the channel index (c = 1 is the visible light channel, c = 2 is the infrared channel), is the pixel value of the (i, j) pixel point of the image of channel c in the spatio-temporally aligned dual-channel image, represents the image of channel c in the spatio-temporally aligned dual-channel image, is the Sigmoid function, and are the weight matrices of the first fully connected layer and the second fully connected layer respectively, is the non-linear activation function, is the global response intensity of channel c, is the fusion weight for channel c. Through the above operations, it can adaptively adjust the contributions of different spectra. In low-light scenarios, the infrared weight approaches 1, relying on infrared data to enhance details. In normal lighting scenarios, the visible light weight approaches 1, retaining color information; is the feature map after weighted fusion. This feature map is input into the YOLOv11 backbone network for object detection to obtain the object detection bounding box of the vehicle and the ReID feature vector.

[0111] Among them, in step A302, for the detected target region, the average motion vector of this target region can be calculated according to the following formula:

[0112] ;

[0113] ;

[0114] Among them, represents the i-th target region, is the horizontal component of the average motion vector of the i-th target region, is the vertical component of the average motion vector of the i-th target region.

[0115] In step A302, the translation parameters of the affine transformation matrix are corrected to obtain the affine transformation matrix after time compensation. The mean value of of each target region and of each target region can be added to the two translation parameters in the final affine transformation matrix respectively. It is expressed by the formula:

[0116] ;

[0117] Among them, is the affine transformation matrix after time compensation, , are the scaling factors of the final affine transformation matrix, , are the shear factors of the final affine transformation matrix, , are the translation parameters of the final affine transformation matrix, is the mean value of of each target region, is the mean value of of each target region.

[0118] In some embodiments, step A4 includes:

[0119] A401. Based on the object detection bounding boxes and ReID feature vectors, use the Deepsort algorithm to perform object tracking on each vehicle to obtain the tracking trajectories;

[0120] A402. Calculate the speed of each vehicle according to the tracking trajectories;

[0121] A403. For each grid, perform a weighted average operation on the speeds of the vehicles on all lanes passing through this grid to obtain the average vehicle speed of this grid.

[0122] Specifically, in the object tracking stage, the object detection bounding boxes between consecutive frames are input into the Deepsort algorithm, and the position of the object in the next frame is predicted through Kalman filtering. When calculating the speed, the time interval of the tracking trajectory is calibrated to the actual frame rate of the dual-channel camera, and the speed value can be calculated by the difference method. During the weighted average process, the weights of each lane can be dynamically configured according to the tunnel design specifications, and the abnormal speed data with a standard deviation exceeding the set threshold is automatically excluded. By fusing geometric tracking and appearance feature matching, the continuity of the vehicle trajectories under complex lighting conditions is ensured. At the same time, the lane-differentiated weighted strategy makes the grid average vehicle speed more in line with the actual traffic flow state, providing accurate input parameters for subsequent dimming control.

[0123] Among them, the application of the Deepsort algorithm is based on Kalman filtering for object position prediction, and the detection bounding boxes are associated with the predicted trajectories through the Hungarian algorithm. The ReID feature vectors are generated by the improved YOLOv11 model and contain the deep semantic information of the vehicle appearance. During the trajectory association process, the ReID feature vectors are used to calculate the similarity between objects to improve the matching accuracy. The unmatched object detection bounding boxes are initialized as new trajectories, and a survival period (such as 30 frames) is set for the lost trajectories and they are deleted after exceeding the survival period.

[0124] ]Among them, the speed of the vehicle can be calculated by the difference method based on the tracking trajectory, which is an existing technology and will not be elaborated here.

[0125] Through the above technical solutions, the present application realizes the stable tracking of vehicles in complex scenarios, effectively solves the problem of broken tracking trajectories caused by object occlusion or light change. At the same time, by fusing the ReID feature vectors, the ability to distinguish vehicles with similar appearances is improved, reducing the error of vehicle identity swapping. In addition, the speed calculation method based on the tracking trajectory provides more continuous and stable speed information compared with the speed estimation of single-frame detection. Finally, the weighted average strategy is adopted to calculate the grid average vehicle speed, considering the characteristics of different lanes, making the speed evaluation result more accurate and reliable, and providing accurate input parameters for subsequent dimming control.

[0126] In some preferred embodiments, in step A403, the average vehicle speed of the grid is calculated by the following formula:

[0127] ;

[0128] ;

[0129] wherein, is the average vehicle speed of the grid, is the speed of the i-th vehicle on all lanes of the grid, is 's weight, N is the total number of vehicles on all lanes of the grid, is the average speed of the lanes on all lanes of the grid, is the standard deviation of the speeds of the vehicles on all lanes of the grid.

[0130] Through the above technical solution, the present application can effectively improve the calculation accuracy and robustness of the grid average vehicle speed. Specifically, by introducing a dynamic weight mechanism, the method enables vehicles with speeds close to the overall average level to obtain higher weights in the calculation, while the influence of those vehicles with speeds significantly deviating from the average level (such as vehicles with sudden acceleration or deceleration) is appropriately reduced. This processing method can effectively suppress the interference of abnormal speed values on the overall evaluation result, thus more accurately reflecting the true movement state of the vehicle flow within the grid. Further, by introducing the standard deviation as a parameter for weight calculation, the method realizes the adaptive adjustment of the vehicle speed distribution characteristics. When the speed distribution within the lane is relatively concentrated, the weight is more sensitive to speed differences and can more finely distinguish the contributions of different vehicles; while when the speed distribution is relatively dispersed, the sensitivity of the weight is correspondingly reduced, avoiding the problem of unbalanced weight distribution caused by accidental speed fluctuations. Therefore, this technical solution not only improves the accuracy of grid average vehicle speed estimation, but also enhances the adaptability and stability of the algorithm in complex traffic scenarios. This provides more reliable input parameters for subsequent tunnel lighting control based on vehicle speed, and helps to achieve more accurate and efficient intelligent lighting control.

[0131] In some embodiments, step A5 includes:

[0132] A501. For each grid, calculate the visible light illumination value of each grid according to the average pixel value within the grid of the visible light channel image after spatio-temporal alignment, the linear conversion coefficient from visible light pixel value to illumination, and the preset environmental background illumination compensation value;

[0133] A502. For each grid, perform an affine transformation on the infrared channel image after spatio-temporal alignment using the time-compensated affine transformation matrix, and calculate the infrared illumination value corresponding to the average pixel value of the affine transformation result within the grid to obtain the initial infrared illumination value;

[0134] For each grid, compare the average pixel value of the visible light channel image after spatio-temporal alignment within the grid with a preset pixel value threshold to determine the dynamic infrared compensation weight, and combine it with the initial infrared illuminance value to calculate the final infrared illuminance value of each grid.

[0135] Among them, in step A501, the visible light illuminance value of each grid can be calculated according to the following formula:

[0136] ;

[0137] Among them, is the visible light illuminance value of the j-th grid, is the linear conversion coefficient from visible light pixel value to illuminance (usually 0.02 lx / pixel, but not limited to this), is the average pixel value of the visible light channel image after spatio-temporal alignment within the j-th grid, is the preset environmental background illuminance compensation value used to eliminate the influence of dark current or environmental stray light (can be set according to actual needs, for example, 5 lx).

[0138] Among them, in step A502, after performing an affine transformation on the infrared channel image after spatio-temporal alignment using the time-compensated affine transformation matrix, calculate the average pixel value of the affine transformation result within the grid, and then use the pre-calibrated mapping function from infrared pixel value to illuminance to convert this average pixel value into the initial infrared illuminance value.

[0139] Among them, in step A503, the final infrared illuminance value of each grid can be calculated through the following formula:

[0140] ;

[0141] Among them, is the final infrared illuminance value of the j-th grid, is the initial infrared illuminance value of the j-th grid, is the preset infrared compensation weight coefficient used to control the infrared contribution intensity in the overexposed area of visible light (can be set according to actual needs, for example, 0.7), is the preset pixel value threshold (can be set according to actual needs, for example, 200), is the standard deviation of the Gaussian distribution used to control the transition smoothness of the overexposed area (usually 30, but not limited to this).

[0142] Through the above technical solutions, the present application realizes adaptive illuminance perception based on a dual-channel camera. By introducing the ambient background illuminance compensation value, the non-linear response problem of visible light pixel values in low illuminance or overexposed areas is overcome, and the accuracy of visible light illuminance values is improved. The infrared image is affine-transformed using the affine transformation matrix after time compensation, reducing the spatio-temporal matching error caused by vehicle movement and enhancing the spatial consistency of the initial infrared illuminance value. A dynamic near-infrared compensation weight mechanism is adopted to adaptively adjust the credibility of the infrared illuminance value according to the real-time state of the visible light channel image. When the visible light image is normal, the infrared compensation weight is reduced to avoid spectral redundancy, and when the visible light image is overexposed, the infrared compensation weight is increased to utilize the all-weather perception advantage of the infrared channel. This dynamic weight allocation strategy based on the state feedback of the visible light channel realizes the adaptive optimal fusion of the dual-channel illuminance perception results, effectively overcomes the risk of perception failure of a single channel under extreme lighting conditions, and improves the reliability and stability of illuminance perception.

[0143] In some embodiments, step A6 includes:

[0144] A601. For each grid, calculate the dimming intensity of each grid according to the preset reference dimming intensity, the ratio of the average vehicle speed of this grid to the maximum designed vehicle speed of the tunnel, the preset speed influence coefficient, the preset tunnel lighting required illuminance, and the maximum value of the visible light illuminance value and the infrared illuminance value of this grid;

[0145] A602. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the dimming intensity of each grid.

[0146] Among them, in step A601, the dimming intensity of each grid can be calculated according to the following formula:

[0147] ;

[0148] Wherein, is the dimming intensity of the j-th grid, is the reference dimming intensity (determined by the rated power of the lighting fixture, for example, set as the light intensity corresponding to the rated power of the lighting fixture), is the average vehicle speed of the j-th grid, is the maximum designed vehicle speed of the tunnel, is the speed influence coefficient (usually set according to the length range of the tunnel. For example, if the tunnel length is greater than 1 km, it is set to 0.9 to give priority to ensuring driving safety; otherwise, it is set to 0.6 to focus on energy conservation), is the tunnel lighting required illuminance (which can be set according to actual needs, for example, 100 lx), is the visible light illuminance value of the j-th grid, is the infrared illuminance value of the j-th grid. In the normal lighting area, is relatively high, and the dimming is mainly dominated by visible light illumination. In the area where visible light fails (such as overexposure or low illumination), the dimming is mainly dominated by infrared illumination.

[0149] In step A602, according to the calculated dimming intensity, it is converted into the power of the corresponding lighting fixture, and the working power of the corresponding lighting fixture is adjusted according to the calculation result. For example, the power is adjusted through a PWM control signal.

[0150] Through the above technical solutions, the present application effectively solves the problem of insufficient adaptability of traditional dimming strategies to changes in traffic flow states. By introducing the ratio of the average vehicle speed to the maximum designed vehicle speed and combining the speed influence coefficient, a quantitative relationship between the dynamic change of vehicle speed and dimming intensity is established, enabling the dimming intensity to adaptively increase as the vehicle speed increases, and avoiding potential safety hazards caused by dimming delay in high-speed states. At the same time, by selecting the maximum value of visible light and infrared illumination as input parameters, the complementary advantages of different spectral data are utilized respectively in strong light overexposure or low-light failure scenarios, ensuring the stability and reliability of illumination perception and eliminating the influence of local monitoring blind spots on dimming accuracy. In addition, based on grid-based zoning to calculate the dimming intensity, fine-tuning of lamp control is achieved, reducing energy waste caused by differences in lighting requirements between regions.

[0151] Preferably, in step A501, the visible light illumination value of the grid containing the vehicle with its headlights on is set to invalid.

[0152] In step A601, if the visible light illumination value of a grid is invalid, the maximum value of the visible light illumination value and the infrared illumination value of this grid is the infrared illumination value.

[0153] This solution solves the problem of distorted visible light illumination perception caused by strong light pollution from vehicle headlights through the visible light illumination value failure mechanism and infrared illumination value substitution strategy under specific conditions. Specifically, in step A501, the visible light illumination value of the grid containing the vehicle with its headlights on is set to invalid, avoiding the abnormal increase in the pixel value of the visible light image caused by the active light source of the headlights and preventing the overestimation of illumination evaluation due to data distortion in the visible light channel in such areas; in step A601, when it is detected that the visible light illumination value is invalid, the infrared illumination value is forced to be used as the maximum value to participate in the dimming calculation, taking advantage of the characteristic that the near-infrared channel is insensitive to light sources outside the visible spectrum, ensuring that light intensity adjustment decisions can still be made based on stable infrared illumination data in an environment with strong headlight interference. This dual-channel data dynamic selection mechanism not only retains the high-precision advantage of the visible light channel under natural lighting conditions but also compensates for the perception reliability in special scenarios through the infrared channel, achieving robust illumination perception and dimming control under all-weather complex lighting conditions.

[0154] Reference Figure 2, this application provides a dynamic dimming system based on dual-channel camera illuminance sensing, including a dual-channel camera 1 with a visible light channel and an infrared channel, a plurality of lighting fixtures 2 arranged above the tunnel road surface, and a control terminal 3;

[0155] The dual-channel camera 1 is used to collect dual-channel images of the tunnel road surface and upload them to the control terminal 3; the dual-channel images include visible light channel images and infrared channel images;

[0156] The control terminal 3 is used to execute:

[0157] Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture 2 in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture 2 (the specific process refers to step A1 in the previous text);

[0158] Preprocess and perform cross-spectrum spatio-temporal alignment on the dual-channel images to obtain an affine transformation matrix for spatio-temporal alignment and the dual-channel images after spatio-temporal alignment; the dual-channel images include visible light channel images and infrared channel images (the specific process refers to step A2 in the previous text);

[0159] Based on the dual-channel images after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix (the specific process refers to step A3 in the previous text);

[0160] Evaluate the average vehicle speed of each grid according to the dynamic target detection result (the specific process refers to step A4 in the previous text);

[0161] Calculate the real-time illuminance value of each grid according to the dual-channel images after spatio-temporal alignment and the affine transformation matrix after time compensation; the real-time illuminance value includes visible light illuminance value and infrared illuminance value (the specific process refers to step A5 in the previous text);

[0162] Adjust the light intensity of the lighting fixtures 2 corresponding to each grid according to the real-time illuminance value and the average vehicle speed evaluation result (the specific process refers to step A6 in the previous text).

[0163] In summary, this application has at least the following advantages:

[0164] 1. Solve the dual-channel data asynchrony problem through high-precision data synchronization to avoid "ghosting" of moving targets;

[0165] 2. Combine global similarity and local geometric constraints to construct a spatially aligned objective function, optimize it by the gradient ascent method, and perform two-time global and local time dynamic compensation to achieve pixel-level spatio-temporal alignment and improve the subsequent detection and illuminance inversion accuracy;

[0166] 3. Adaptively adjust the contributions of different spectra according to the lighting conditions, relying on near-infrared light under low illuminance and visible light under normal lighting, improving the accuracy of target detection and anti-interference ability;

[0167] 4. Overcome the failure problem of a single channel under extreme lighting by excluding vehicle interference and dynamically compensating for overexposed areas, improving the illuminance detection accuracy.

[0168] 5. The dynamic dimming strategy that integrates speed and dual-channel illuminance values reduces the dimming response delay, increases the lighting intensity when the vehicle speed is relatively high, realizes more intelligent and faster-responsive dimming control, and balances safety and energy conservation;

[0169] 6. Divide the tunnel pavement into grids for management, which can accurately locate local illuminance anomalies and realize local dimming repair.

[0170] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A dynamic dimming method based on dual-channel camera illuminance sensing, which is used to perform dimming control on the lighting fixtures in a tunnel based on a dual-channel camera with a visible light channel and an infrared channel, is characterized in that Including the steps: A1. Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture; A2. Collect the dual-channel image of the tunnel road surface through a dual-channel camera, and perform preprocessing and cross-spectrum spatio-temporal alignment on the dual-channel image to obtain an affine transformation matrix for spatio-temporal alignment and the dual-channel image after spatio-temporal alignment; the dual-channel image includes a visible light channel image and an infrared channel image; A3. Based on the dual-channel image after spatio-temporal alignment, perform dynamic target detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix; A4. Evaluate the average vehicle speed of each grid according to the dynamic target detection result; A5. Calculate the real-time illuminance value of each grid according to the dual-channel image after spatio-temporal alignment and the affine transformation matrix after time compensation; the real-time illuminance value includes visible light illuminance value and infrared illuminance value; A6. Adjust the light intensity of the lighting fixture corresponding to each grid according to the real-time illuminance value and the average vehicle speed evaluation result; Step A5 includes: A501. For each grid, calculate the visible light illuminance value of each grid according to the average pixel value of the visible light channel image after spatio-temporal alignment within the grid, the linear conversion coefficient from visible light pixel value to illuminance, and the preset environmental background illuminance compensation value; A502. For each grid, perform affine transformation on the infrared channel image after spatio-temporal alignment with the affine transformation matrix after time compensation, and calculate the infrared illuminance value corresponding to the average pixel value of the affine transformation result within the grid to obtain the initial infrared illuminance value; A503. For each grid, compare the average pixel value of the visible light channel image after spatio-temporal alignment within the grid with the preset pixel value threshold, determine the dynamic infrared compensation weight, and combine the initial infrared illuminance value to calculate the final infrared illuminance value of each grid.

2. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 1, wherein In step A2, performing cross-spectrum spatio-temporal alignment on the dual-channel image includes: Using the visible light channel image and the infrared channel image after preprocessing as the target visible light image and the target infrared image, perform SIFT feature point matching on the target visible light image and the target infrared image to obtain a preliminary affine transformation matrix; Estimate the global average motion vector of the target visible light image based on the optical flow method to correct the translation parameter of the preliminary affine transformation matrix to obtain a corrected affine transformation matrix; Taking maximizing the normalized mutual information and minimizing the SIFT feature point matching error as the goal, establish an objective function related to the affine transformation matrix; the normalized mutual information is related to the entropy and mutual information of the target visible light image and the target infrared image; Using the corrected affine transformation matrix as the initial value of the affine transformation matrix, iteratively solve the objective function by the gradient ascent method to obtain the final affine transformation matrix; Perform affine transformation on the target infrared image using the final affine transformation matrix, and splice the affine result with the target visible light image to obtain the dual-channel image after spatio-temporal alignment.

3. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 1, wherein Step A3 includes: A301. Use the improved YOLOv11 model to perform object detection on the two-channel images after spatio-temporal alignment to obtain the object detection bounding boxes of vehicles and the ReID feature vectors. A302. Estimate the average motion vector of the object region enclosed by the object detection bounding box based on the optical flow method to correct the translation parameter of the affine transformation matrix and obtain the time-compensated affine transformation matrix.

4. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 3, wherein Step A4 includes: A401. According to the object detection bounding box and the ReID feature vector, use the Deepsort algorithm to perform object tracking on each vehicle to obtain the tracking trajectories. A402. Calculate the speed of each vehicle according to the tracking trajectories. A403. For each grid, perform a weighted average operation on the speeds of the vehicles on all lanes passing through this grid to obtain the average vehicle speed of this grid.

5. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 4, wherein In step A403, the average vehicle speed of the grid is calculated by the following formula: ； ； Among them, is the average vehicle speed of the grid, is the speed of the i-th vehicle on all lanes of the grid, is the weight of, N is the total number of vehicles on all lanes of the grid, is the average speed of the lanes on all lanes of the grid, is the standard deviation of the speeds of the vehicles on all lanes of the grid.

6. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 1, wherein Step A6 includes: A601. For each grid, calculate the dimming intensity of each grid according to the preset reference dimming intensity, the ratio of the average vehicle speed of this grid to the maximum designed vehicle speed of the tunnel, the preset speed influence coefficient, the preset tunnel lighting required illuminance, and the maximum value of the visible light illuminance value and the infrared illuminance value of this grid. A602. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the dimming intensity of each grid.

7. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 6, wherein, In step A601, the dimming intensity of each grid is calculated according to the following formula: ； Among them, is the dimming intensity of the j-th grid, is the reference dimming intensity, is the average vehicle speed of the j-th grid, is the maximum designed vehicle speed of the tunnel, is the speed influence coefficient, is the required illumination of the tunnel lighting, is the visible light illumination value of the j-th grid, is the infrared illumination value of the j-th grid.

8. The dynamic dimming method based on dual-channel camera illuminance sensing according to claim 6, wherein In step A501, set the visible light illuminance value of the grid containing the vehicle with the headlight on to be invalid. In step A601, if the visible light illuminance value of a grid is invalid, the maximum value of the visible light illuminance value and the infrared illuminance value of this grid is the infrared illuminance value.

9. A dynamic dimming system based on dual-channel camera illuminance sensing, which is used to implement the dynamic dimming method based on dual-channel camera illuminance sensing according to any one of claims 1-8, and is characterized in that, It includes a two-channel camera with a visible light channel and an infrared channel, multiple lighting fixtures arranged above the tunnel road surface, and a control terminal. The two-channel camera is used to collect the two-channel images of the tunnel road surface and upload them to the control terminal; the two-channel images include visible light channel images and infrared channel images. The control terminal is used to execute: Obtain the tunnel road surface grid division result determined in advance according to the coverage range of each lighting fixture in the tunnel; each grid of the grid division result corresponds to at least one lighting fixture. Perform preprocessing and cross-spectrum spatio-temporal alignment on the two-channel images to obtain the affine transformation matrix for spatio-temporal alignment and the two-channel images after spatio-temporal alignment; the two-channel images include visible light channel images and infrared channel images. Based on the two-channel images after spatio-temporal alignment, perform dynamic object detection on the vehicles in the tunnel and perform time compensation on the affine transformation matrix. Evaluate the average vehicle speed of each grid according to the dynamic object detection results. Calculate the real-time illuminance values of each grid according to the two-channel images after spatio-temporal alignment and the time-compensated affine transformation matrix; the real-time illuminance values include visible light illuminance values and infrared illuminance values. Adjust the light intensity of the lighting fixtures corresponding to each grid according to the real-time illuminance values and the average vehicle speed evaluation results.

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