Method, device and equipment for improving side face splicing effect of vehicle and medium

Abstract

The invention relates to a method, a device, equipment and a medium for improving a vehicle side splicing effect, and the method comprises the steps: obtaining a video sequence for a vehicle side, and detecting a vehicle region in the video sequence according to the video sequence; according to a preset light metering area, determining an image area, corresponding to the light metering area, of each frame of image in the video sequence; for each image area, performing photometric analysis on the image area to obtain a photometric analysis result, and according to the photometric analysis result, performing brightness compensation on the image area to adjust the brightness of the image area to preset reference brightness to obtain a vehicle area image subjected to brightness compensation; and splicing all the vehicle area images after brightness compensation to generate a complete vehicle side image. According to the scheme, through continuous and unified photometric analysis and brightness compensation processing, the splicing quality of the vehicle side image is remarkably improved, and the image details and the visual effect are enhanced.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and more specifically, to a method, apparatus, equipment, and medium for improving the side panel splicing effect of vehicles. Background Technology

[0002] Existing vehicle side image stitching techniques, such as those based on vehicle speed estimation or feature tracking, primarily focus on spatial alignment and pixel fusion, often neglecting the impact of lighting consistency on the final result. The conventional approach is to directly perform global histogram equalization or adaptive equalization on the entire frame. However, during vehicle side image capture, due to factors such as the curvature of the vehicle body and variations in ambient light angles, different parts (e.g., the front, windows, doors, rear, upper half, and lower half of the vehicle) may be under completely different lighting conditions. While global processing can improve overall contrast, it cannot resolve local overexposure or underexposure issues, resulting in significant brightness differences and detail loss in the stitched image, affecting subsequent vehicle recognition and feature analysis.

[0003] Existing technologies primarily focus on the accuracy of spatial alignment, such as stitching methods based on vehicle speed estimation or feature tracking. In terms of illumination processing, global image enhancement techniques are typically employed, such as histogram equalization (HE) or adaptive histogram equalization (AHE). However, global processing cannot address local illumination differences. For example, when a vehicle is half in sunlight and half in shadow, global equalization may amplify noise in the shadow areas while exacerbating overexposure in the sunlight areas. Another approach is to use high dynamic range (HDR) imaging, but this method requires multiple exposure sequences, making it unsuitable for single-camera, fixed-frame-rate video stream stitching scenarios, and it is computationally complex and difficult to process in real time. Several strategies of existing technologies can be found in [link to relevant documentation]. Figure 1 .

[0004] The main drawback of existing technologies lies in their overly coarse handling of lighting. Global image enhancement methods cannot cope with the complex local lighting variations on the sides of a vehicle. In strong light, dark areas such as windows may become noisy due to enhancement, while highlight areas of the vehicle body may lose texture details due to overexposure; in shadow or backlighting, some areas of the vehicle may be too dark. This uneven lighting directly leads to color differences and brightness banding in the stitched image, resulting in an unnatural visual effect and reducing the accuracy and reliability of using the stitched image for applications such as vehicle model recognition, damage detection, and stain analysis.

[0005] Ignoring local lighting: Global image enhancement methods cannot effectively handle the complex lighting changes inside the sides of vehicles, resulting in inconsistent visual effects in the stitched images.

[0006] Loss of detail: In cases of extremely uneven lighting, details in highlight or shadow areas are usually lost in order to preserve the overall effect, affecting vehicle recognition and feature analysis.

[0007] Obvious seams: Even if the spatial alignment is accurate, huge differences in brightness will create obvious seams in the overlapping areas of the splicing.

[0008] Poor adaptability: Existing methods are not robust to scenarios with rapidly changing light conditions (such as vehicles entering / exiting tunnels). Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method, apparatus, equipment and medium for improving the splicing effect of vehicle side panels, and aims to solve at least one of the above-mentioned technical problems.

[0010] In a first aspect, the technical solution of the present invention to solve the above-mentioned technical problems is as follows: a method for improving the splicing effect of vehicle side panels, the method comprising: Acquire a video sequence of the vehicle's side view, and detect the vehicle region within the video sequence based on the video sequence; Based on the preset metering area, determine the image area corresponding to the metering area for each frame in the video sequence; For each image region, a photometric analysis is performed on the image region to obtain the photometric analysis results, which include the average brightness. For each image region, brightness compensation is performed on the image region based on the photometric analysis results, so that the brightness of the image region is adjusted to the preset reference brightness, thus obtaining a brightness-compensated vehicle region image. All brightness-compensated vehicle area images are stitched together to generate a complete vehicle side image.

[0011] The beneficial effects of this invention are as follows: By acquiring video sequences of the vehicle's side profile and detecting the vehicle area, then dividing each image frame according to a preset metering area, and performing detailed metering analysis on each image area to obtain metering analysis results including average brightness, brightness compensation is then performed on each image area to adjust the brightness to a preset reference brightness. Finally, all brightness-compensated images are stitched together to generate a complete and uniformly illuminated vehicle side profile image. This continuous, non-segmented processing flow significantly improves the quality of the stitched image, enhances the smoothness of the visual effect, and optimizes the accuracy of brightness compensation, thus providing a more reliable and accurate data foundation for subsequent vehicle recognition and feature analysis. It also improves the robustness and practicality of the system. This not only enhances the overall effect of vehicle side profile image stitching but also provides an efficient and practical solution for vehicle monitoring and analysis in intelligent transportation systems.

[0012] Based on the above technical solution, the present invention can be further improved as follows.

[0013] Furthermore, the aforementioned preset reference brightness is determined based on the brightness of the first frame image in the video sequence.

[0014] Furthermore, the aforementioned preset reference brightness is dynamically adjusted based on the average brightness of the vehicle area.

[0015] Furthermore, the method also includes: For each brightness-compensated vehicle region image, a tracking algorithm is used to track the region of interest in the vehicle region image to obtain the tracking result; Based on the tracking results, the vehicle region image is subjected to moving mean filtering to obtain the filtered vehicle region image. All brightness-compensated vehicle area images are stitched together to generate a complete vehicle side image, including: All filtered vehicle area images are stitched together to generate a complete vehicle side image.

[0016] Furthermore, for each image region, brightness compensation is performed on the image region based on the photometric analysis results, including: Calculate the brightness adjustment coefficient of the image area based on the average brightness and contrast in the photometric analysis results; The pixel values ​​of the image area are adjusted according to the brightness adjustment coefficient so that the brightness of the image area reaches the preset reference brightness.

[0017] Furthermore, for each image region, brightness compensation is performed on the image region based on the photometric analysis results, including: Based on the average brightness in the photometric analysis results, determine the brightness compensation strategy for the image area; Use any of the following brightness compensation strategies to perform brightness compensation on the image region: Based on the average brightness of the image area, adjust the gamma value of the image area to make the brightness of the image area reach the preset reference brightness level; A linear mapping function is used to map the pixel values ​​of an image region from the current brightness range to the target brightness range. The pixel values ​​of the image region are adjusted using a non-linear function to bring the brightness of the image region to a preset reference brightness level.

[0018] Furthermore, the method also includes: Determine the histogram of each frame in the video sequence; Global adaptive image equalization is performed on the histogram of each frame to obtain the processed image; All processed images in the video sequence are stitched together to obtain a complete side image of the vehicle.

[0019] Secondly, in order to solve the above-mentioned technical problems, the present invention also provides a device for improving the splicing effect of vehicle side panels, the device comprising: The acquisition module is used to acquire video sequences from the side of the vehicle and detect the vehicle region in the video sequence based on the video sequence. The segmentation module is used to determine the image region corresponding to the metering region for each frame in the video sequence based on the preset metering region. The photometric analysis module is used to perform photometric analysis on each image region to obtain photometric analysis results, including average brightness. The brightness compensation module is used to perform brightness compensation on each image area based on the photometric analysis results, so that the brightness of the image area is adjusted to the preset reference brightness, and the vehicle area image after brightness compensation is obtained. The stitching module is used to stitch together all the brightness-compensated vehicle area images to generate a complete vehicle side image.

[0020] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method of improving the side splicing effect of the vehicle as described in this application.

[0021] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of improving the side splicing effect of a vehicle as described in this application.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.

[0024] Figure 1 A schematic diagram illustrating different strategies in the prior art as provided in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for improving the side panel splicing effect of a vehicle according to an embodiment of the present invention; Figure 3This is a schematic diagram of an image obtained due to uneven ambient lighting, provided as an embodiment of the present invention. Figure 4 This is a flowchart illustrating another method for improving the side panel splicing effect of a vehicle according to an embodiment of the present invention; Figure 5 A schematic diagram of an image region provided in one embodiment of the present invention; Figure 6 This is a schematic diagram of an image region before brightness compensation, provided as an embodiment of the present invention. Figure 7 This is a schematic diagram of an image region after brightness compensation, provided as an embodiment of the present invention. Figure 8 This is a schematic diagram of a brightness compensation process provided in one embodiment of the present invention; Figure 9 A complete vehicle side view schematic diagram provided for one embodiment of the present invention; Figure 10 This is a schematic diagram of a device for improving the splicing effect of the side of a vehicle according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0025] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0026] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0027] The solution provided in this invention can be applied to any application scenario requiring improved side panel splicing effects on vehicles. The solution provided in this invention can be executed by any electronic device, such as a user's terminal device, including at least one of the following: smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart TV, or smart in-vehicle device.

[0028] This invention provides a possible implementation, such as... Figure 2The flowchart shown illustrates a method for improving the side panel stitching effect of a vehicle. This method can be executed by any electronic device, such as a terminal device, or jointly executed by a terminal device and a server. For ease of description, the method provided in this embodiment will be described below using a terminal device as the execution subject as an example. Figure 2 The flowchart shown indicates that the method may include the following steps: S10, acquire a video sequence of the side of the vehicle, and detect the vehicle area in the video sequence based on the video sequence; S20, based on the preset metering area, determine the image area corresponding to the metering area for each frame in the video sequence; S30, For each image region, perform photometric analysis on the image region to obtain photometric analysis results, which include average brightness; S40, for each image region, perform brightness compensation on the image region according to the photometric analysis results, so that the brightness of the image region is adjusted to the preset reference brightness, and obtain the brightness-compensated vehicle region image. S50 stitches together all the brightness-compensated vehicle area images to generate a complete vehicle side image.

[0029] The method of this invention acquires a video sequence of the vehicle's side profile and detects the vehicle area. Then, it divides each image frame according to a preset metering area and performs detailed metering analysis on each image area to obtain metering analysis results including average brightness. Next, it performs brightness compensation on each image area to adjust the brightness to a preset reference brightness. Finally, it stitches together all brightness-compensated images to generate a complete and uniformly illuminated vehicle side profile image. This continuous, non-segmented processing significantly improves the quality of the stitched image, enhances the smoothness of the visual effect, and optimizes the accuracy of brightness compensation. This provides a more reliable and accurate data foundation for subsequent vehicle recognition and feature analysis, while also improving the robustness and practicality of the system. This not only enhances the overall effect of vehicle side profile image stitching but also provides an efficient and practical solution for vehicle monitoring and analysis in intelligent transportation systems.

[0030] The following specific embodiments further illustrate the solution of the present invention. In these embodiments, the present invention addresses the problems of loss of detail, brightness jumps, and obvious seams in existing vehicle side video image stitching technologies caused by uneven ambient lighting (such as vehicle body reflections, shadows, backlighting, etc.). The present invention proposes a method to improve the stitching effect through image area metering. This method performs independent exposure analysis and brightness compensation on different areas of the vehicle side to ensure consistent brightness and contrast of each image block during fusion stitching, thereby generating a complete vehicle side image with rich detail and a smooth visual effect.

[0031] The core process of this invention includes: video frame acquisition -> initial image processing (distortion correction) -> vehicle region detection and segmentation -> local metering and brightness compensation based on vehicle region -> image feature tracking and displacement calculation -> image stitching and fusion under brightness coordination.

[0032] Based on this, combined Figure 4 The method for improving the side panel splicing effect of a vehicle provided in this embodiment may include the following steps: S10, acquire a video sequence of the side of the vehicle, and detect the vehicle area in the video sequence based on the video sequence; In this context, a video sequence refers to a complete video stream composed of consecutive video frames (image frames). These frames are arranged in chronological order and together form a continuous dynamic image sequence. In the fields of video processing and computer vision, video sequences are often treated as a whole and used to analyze and process information in dynamic scenes.

[0033] In this context, the vehicle region in the video sequence refers to the image region corresponding to the vehicle in each frame. That is, the vehicle region consists of an image sequence corresponding to different parts of the complete vehicle.

[0034] Optionally, in order to obtain more details of the vehicle body through a camera at a very close distance, a 1.44mm fisheye lens was selected for video sequence acquisition in this application.

[0035] Prior to S20, the method also includes: Each frame of the video sequence undergoes preprocessing, such as lens distortion correction, to obtain a preprocessed video sequence. The vehicle region segmentation in S20 is then based on this preprocessed video sequence.

[0036] S20, based on the preset image region, determine the image region corresponding to the metering region for each frame of the video sequence; that is, determine the image region corresponding to the vehicle region in each frame of the video sequence within the preset image region, and obtain one image region for each frame of the video sequence.

[0037] Optionally, one way to determine the image region corresponding to the metering region for each frame in the video sequence based on the preset image region is as follows: analyze each frame in the entire video sequence to determine the boundary of the vehicle region; based on the geometric features and lighting conditions of the vehicle region, divide the vehicle region into multiple continuous image regions. The division of the image regions is based on the structural features of the vehicle side (such as the front, body, and rear of the vehicle) or changes in lighting conditions, and remains consistent throughout the entire video sequence.

[0038] In this invention, the specific implementation scheme for determining the image region corresponding to the metering region for each frame of the video sequence based on the preset image region can also be: First, millimeter-wave radar is used to monitor vehicle entry and exit. Millimeter-wave radar can perceive the vehicle's position and movement in real time, thus accurately detecting the vehicle area within video frames. By combining the vehicle position information provided by the millimeter-wave radar with the image data from the video frames, the boundary range of the vehicle in each frame can be accurately determined, achieving precise detection of the vehicle area.

[0039] After vehicle region detection is completed, the vehicle region in the entire video sequence is divided into multiple image regions based on the structural features of the vehicle's side and preset rules. These image regions are divided based on the overall structure of the vehicle's side, rather than segmenting the video sequence. Specifically, the vehicle's side is usually divided into several regions with specific functions or characteristics, such as the front, body, and rear regions. Each region has its unique lighting conditions and visual characteristics in the vehicle's side image, therefore requiring separate metering analysis and processing.

[0040] For example, the front of a vehicle may have high brightness due to the headlights or other reflective properties, while the body area may be affected by ambient light, and the rear area may exhibit different lighting characteristics due to the taillights or other factors. By dividing the vehicle's side into these image regions with different lighting characteristics, independent photometric analysis of each region can be performed more accurately, thus providing more accurate data support for subsequent brightness compensation and image stitching.

[0041] As an example, see Figure 5 As shown in the diagram, the boxes represent pre-defined image areas. The process of determining the image area corresponding to the metering area for each frame in a video sequence, based on the preset metering area, refers to continuously and non-segmentingly dividing the entire vehicle area within each frame of the video sequence according to the vehicle's geometry and lighting conditions. This division method ensures that each part of the vehicle can be accurately assigned to a specific area in each frame of the video sequence, thereby achieving comprehensive and detailed metering analysis of the vehicle's sides.

[0042] S30, For each image region, perform photometric analysis on the image region to obtain photometric analysis results, which include average brightness; In this context, photometric analysis of image regions refers to the continuous and uniform analysis and processing of each image region throughout the entire video sequence. Performing photometric analysis on each image region means that the analysis of the image region is ongoing across the entire video sequence, rather than targeting only certain segments or frames within the video sequence.

[0043] Alternatively, one way to implement the above method of performing photometric analysis on each image region to obtain the photometric analysis results is as follows: For each image region, luminance data for all pixels within that region is collected. This includes recording the luminance value for each pixel, typically represented by a grayscale level or the luminance component in the RGB color space.

[0044] For each image region, the average brightness of all pixels within that region is calculated. This average brightness is a key parameter in the photometric analysis results, used to assess the overall brightness level of the region.

[0045] S40, for each image region, perform brightness compensation on the image region according to the photometric analysis results, so that the brightness of the image region is adjusted to the preset reference brightness, and obtain the brightness-compensated vehicle region image. Among them, the vehicle area image after brightness compensation corresponds to the image of a part of the entire vehicle.

[0046] Optionally, the preset reference brightness is determined based on the brightness of the first frame of the video sequence, or it is dynamically adjusted based on the average brightness of the vehicle area, or it can be a preset ideal brightness value.

[0047] Brightness compensation for image regions can be achieved through different methods. Optionally, the first method for brightness compensation is as follows: Calculate the brightness adjustment coefficient of the image area based on the average brightness and contrast in the photometric analysis results; The pixel values ​​of the image area are adjusted according to the brightness adjustment coefficient so that the brightness of the image area reaches the preset reference brightness.

[0048] The brightness adjustment coefficient is calculated using the following formula: ; Furthermore, the pixel value adjustment is achieved through the following formula: Adjusted pixel value = original pixel value × adjustment factor; Optionally, the above photometric analysis results also include contrast, and the method further includes: Based on the contrast ratio, the brightness adjustment coefficient is adjusted to obtain the target adjustment coefficient. The contrast ratio is used to evaluate the illumination uniformity of the metering area, and the brightness adjustment coefficient is fine-tuned based on the illumination uniformity.

[0049] The second approach is to determine the brightness compensation strategy for the image region based on the average brightness in the photometric analysis results. Use any of the following brightness compensation strategies to perform brightness compensation on the image region: Based on the average brightness of the image area, adjust the gamma value of the image area to make the brightness of the image area reach the preset reference brightness level; A linear mapping function is used to map the pixel values ​​of an image region from the current brightness range to the target brightness range. The pixel values ​​of the image region are adjusted using a non-linear function to bring the brightness of the image region to a preset reference brightness level.

[0050] For details, please refer to Figure 8 The diagram shows the brightness compensation process.

[0051] Specifically, the target gamma value of the image region is calculated, and the target gamma value is determined according to the following formula: ; Apply the gamma correction formula to each pixel value of the image region: Adjusted pixel value = original pixel value γ; As an example, see Figure 6 and Figure 7 The two images shown, Figure 6 This is the image before brightness compensation. Figure 7 This is the image after brightness compensation.

[0052] S50 stitches together all the brightness-compensated vehicle area images to generate a complete vehicle side image.

[0053] The purpose of stitching together all the brightness-compensated vehicle area images is to stitch together a portion of the vehicle from all the brightness-compensated vehicle area images to obtain a complete image of the vehicle's side profile.

[0054] Optionally, the above method further includes: For each brightness-compensated vehicle region image, a tracking algorithm is used to track the region of interest in the vehicle region image to obtain the tracking result; Based on the tracking results, the vehicle region image is subjected to moving mean filtering to obtain the filtered vehicle region image. All brightness-compensated vehicle area images are stitched together to generate a complete vehicle side image, including: All filtered vehicle area images are stitched together to generate a complete vehicle side image.

[0055] On the brightness-compensated image sequence, feature point tracking (such as KCF, SiamFC) or optical flow methods are performed to accurately calculate the vehicle displacement (i.e., determine the vehicle displacement based on the tracking results). During the stitching process, in addition to spatial alignment, special consideration is given to the smooth brightness transition in the overlapping areas of adjacent frames. Multi-band fusion or feathering techniques can be used to ensure natural lighting at the seams. All video frames in all video sequences are iteratively processed, and the brightness-coordinated image blocks are stitched and merged to output a complete vehicle side image with uniform lighting and clear details.

[0056] As an example, see Figure 3 The diagram shown illustrates a complete vehicle side image where uneven ambient lighting (such as vehicle reflections, shadows, backlighting, etc.) results in lost details, inconsistent brightness, and noticeable seams in the stitched image. See also... Figure 9 The diagram shown is a complete side view of the vehicle after processing using this method.

[0057] The core of this invention lies in introducing a local metering and brightness compensation mechanism for vehicle areas during the vehicle side image stitching process to address the problem of uneven illumination. Vehicle areas in the video image are detected and segmented, serving as the basis for subsequent metering and compensation. A dynamic, image region-based brightness compensation method adaptively adjusts according to the actual illumination conditions of the vehicle area in each frame. This method includes, but is not limited to, regional gamma correction and adaptive linear transformation. During the image stitching and fusion stage, special attention is paid to the brightness transition in overlapping areas to ensure the continuity of illumination at the stitching seams. The method in this application includes, but is not limited to, multi-band fusion, transparency blending, and gradient domain fusion. This is a systematic solution that combines regional metering and brightness compensation with traditional vehicle tracking, displacement calculation, and image stitching techniques.

[0058] The solution of the present invention has the following beneficial effects: 1. Improve the quality of stitched images: Through local metering and compensation, it effectively overcomes problems such as loss of detail and brightness jumps caused by uneven lighting. The generated vehicle side image has overall harmonious lighting, richer details, and better visual effect.

[0059] 2. Enhanced reliability of subsequent analysis: Uniform lighting conditions provide a higher quality data foundation for subsequent analysis tasks such as vehicle model recognition, color determination, and surface condition detection based on stitched images, thereby improving the accuracy of the analysis.

[0060] 3. A dynamic, region-based brightness compensation method that adaptively adjusts the brightness based on the actual lighting conditions of the vehicle region in each frame. The method includes, but is not limited to, region gamma correction and adaptive linear transformation.

[0061] 4. Strong robustness: This method can adapt to more complex lighting environments, such as side lighting, backlighting, and local shadows, improving the stability and output quality of the entire splicing system under different times and weather conditions.

[0062] 5. High degree of process integration: This invention can be used as an enhancement module for existing splicing processes without changing the core tracking and splicing algorithms. It is easy to integrate and implement and has high practical value.

[0063] Based on and Figure 1 Using the same principle as the method shown, this embodiment of the invention also provides a device 20 for improving the splicing effect of vehicle side panels, such as... Figure 10 As shown, the device 20 for improving the side panel stitching effect of a vehicle may include an acquisition module 210, a division module 220, a light metering analysis module 230, a brightness compensation module 240, and a stitching module 250, wherein: The acquisition module 210 is used to acquire a video sequence of the side of the vehicle and detect the vehicle region in the video sequence based on the video sequence. The segmentation module 220 is used to determine the image region corresponding to the metering region for each frame in the video sequence based on the preset metering region; The photometric analysis module 230 is used to perform photometric analysis on each image region to obtain photometric analysis results, including average brightness. The brightness compensation module 240 is used to perform brightness compensation on each image area according to the photometric analysis results, so that the brightness of the image area is adjusted to a preset reference brightness, and the vehicle area image after brightness compensation is obtained. The stitching module 250 is used to stitch together all the brightness-compensated vehicle area images to generate a complete vehicle side image.

[0064] Optionally, the preset reference brightness is determined based on the brightness of the first frame of the video sequence.

[0065] Optionally, the preset reference brightness is dynamically adjusted based on the average brightness of the vehicle area.

[0066] Optionally, the above-mentioned device further includes: The tracking and filtering module is used to track the region of interest in each brightness-compensated vehicle region image using a tracking algorithm to obtain the tracking result; and to perform moving mean filtering on the vehicle region image based on the tracking result to obtain the filtered vehicle region image. When the stitching module 250 stitches together all the brightness-compensated vehicle area images to generate a complete vehicle side image, it is specifically used for: All filtered vehicle area images are stitched together to generate a complete vehicle side image.

[0067] Optionally, when the brightness compensation module 240 performs brightness compensation on each image region based on the photometric analysis results, it is specifically used for: Calculate the brightness adjustment coefficient of the image area based on the average brightness and contrast in the photometric analysis results; The pixel values ​​of the image area are adjusted according to the brightness adjustment coefficient so that the brightness of the image area reaches the preset reference brightness.

[0068] Optionally, when the brightness compensation module 240 performs brightness compensation on each image region based on the photometric analysis results, it is specifically used for: Based on the average brightness in the photometric analysis results, determine the brightness compensation strategy for the image area; Use any of the following brightness compensation strategies to perform brightness compensation on the image region: Based on the average brightness of the image area, adjust the gamma value of the image area to make the brightness of the image area reach the preset reference brightness level; A linear mapping function is used to map the pixel values ​​of an image region from the current brightness range to the target brightness range. The pixel values ​​of the image region are adjusted using a non-linear function to bring the brightness of the image region to a preset reference brightness level.

[0069] Optionally, the above-mentioned device further includes: The global adaptive equalization processing module is used to determine the histogram of each frame in the video sequence; perform global adaptive equalization processing on the histogram of each frame to obtain the processed image; and stitch together all the processed images in the video sequence to obtain a complete vehicle side image.

[0070] The device for improving the side panel stitching effect of the present invention can execute the method for improving the side panel stitching effect of the present invention provided in the present invention. The implementation principle is similar. The actions performed by each module and unit in the device for improving the side panel stitching effect of the present invention correspond to the steps in the method for improving the side panel stitching effect of the present invention. For detailed functional descriptions of each module of the device for improving the side panel stitching effect of the present invention, please refer to the descriptions of the corresponding methods for improving the side panel stitching effect of the present invention shown above, which will not be repeated here.

[0071] The aforementioned device for improving the side panel stitching effect of a vehicle can be a computer program (including program code) running on a computer device. For example, the device for improving the side panel stitching effect of a vehicle can be an application software. The device can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0072] In some embodiments, the device for improving the side-mounted stitching effect of a vehicle provided by the present invention can be implemented by a combination of hardware and software. As an example, the device for improving the side-mounted stitching effect of a vehicle provided by the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the method for improving the side-mounted stitching effect of a vehicle provided by the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0073] In other embodiments, the device for improving the side panel splicing effect of vehicles provided in this invention can be implemented in software. Figure 10 An apparatus for improving the side-view stitching effect of a vehicle is shown, which may be software in the form of programs and plug-ins, and includes a series of modules, including an acquisition module 210, a division module 220, a photometric analysis module 230, a brightness compensation module 240, and a stitching module 250, for implementing the method for improving the side-view stitching effect of a vehicle provided in the embodiments of the present invention.

[0074] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0075] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0076] In one alternative embodiment, an electronic device is provided, such as Figure 11 As shown, Figure 11 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0077] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0078] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0079] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0080] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0081] Among these, electronic devices can also be terminal devices. Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0082] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0083] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0084] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0085] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0086] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0087] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0088] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for improving the splicing effect of vehicle side panels, characterized in that, include: Acquire a video sequence of the side of the vehicle, and detect the vehicle region in the video sequence based on the video sequence; Based on the preset metering area, determine the image region corresponding to the metering area for each frame in the video sequence; For each of the image regions, a photometric analysis is performed on the image region to obtain a photometric analysis result, which includes an average brightness. For each image region, brightness compensation is performed on the image region according to the photometric analysis results, so that the brightness of the image region is adjusted to a preset reference brightness, thereby obtaining a brightness-compensated vehicle region image; All brightness-compensated vehicle area images are stitched together to generate a complete vehicle side image.

2. The method according to claim 1, characterized in that, The preset reference brightness is determined based on the brightness of the first frame image in the video sequence.

3. The method according to claim 1, characterized in that, The preset reference brightness is dynamically adjusted based on the average brightness of the vehicle area.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: For each of the brightness-compensated vehicle region images, a tracking algorithm is used to track the region of interest in the vehicle region image to obtain the tracking result; Based on the tracking results, the vehicle region image is subjected to sliding mean filtering to obtain the filtered vehicle region image. The process of stitching together all the brightness-compensated vehicle area images to generate a complete vehicle side image includes: All filtered vehicle area images are stitched together to generate a complete vehicle side image.

5. The method according to any one of claims 1 to 3, characterized in that, For each of the image regions, the step of performing brightness compensation on the image region based on the photometric analysis results includes: Calculate the brightness adjustment coefficient of the image region based on the average brightness and contrast in the photometric analysis results; The pixel values ​​of the image region are adjusted according to the brightness adjustment coefficient so that the brightness of the image region reaches a preset reference brightness.

6. The method according to any one of claims 1 to 3, characterized in that, For each of the image regions, the step of performing brightness compensation on the image region based on the photometric analysis results includes: Based on the average brightness in the photometric analysis results, determine the brightness compensation strategy for the image region; The image region is brightness compensated using any of the following brightness compensation strategies: Based on the average brightness of the image region, adjust the gamma value of the image region so that the brightness of the image region reaches a preset reference brightness level; The pixel values ​​of the image region are mapped from the current brightness range to the target brightness range using a linear mapping function. The pixel values ​​of the image region are adjusted using a non-linear function to bring the brightness of the image region to a preset reference brightness level.

7. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Determine the histogram of each frame in the video sequence; Global adaptive image equalization is performed on the histogram of each frame of the image to obtain the processed image; All processed images in the video sequence are stitched together to obtain a complete vehicle side image.

8. A device for improving the splicing effect of vehicle side panels, characterized in that, include: The acquisition module is used to acquire a video sequence of the side of the vehicle and detect the vehicle region in the video sequence based on the video sequence. The segmentation module is used to determine the image region corresponding to the metering region for each frame in the video sequence based on the preset metering region; A photometric analysis module is used to perform photometric analysis on each of the image regions to obtain photometric analysis results, the photometric analysis results including average brightness; The brightness compensation module is used to perform brightness compensation on each image region according to the photometric analysis result, so that the brightness of the image region is adjusted to a preset reference brightness, thereby obtaining a brightness-compensated vehicle region image. The stitching module is used to stitch together all the brightness-compensated vehicle area images to generate a complete vehicle side image.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

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