A method, device, equipment and medium for enhancing a road surface static object image

Abstract

The application discloses a kind of road surface static object image enhancement method, device, equipment and medium. By obtaining and identifying the road surface static object image to be enhanced, the environmental element identification result is obtained;Randomly obtain a plurality of target environmental element comprehensive score, and according to environmental element comprehensive score calculation formula, parameter configuration is carried out, and configuration quantization result is obtained respectively;At least one static object sample image corresponding to the road surface static object image is obtained, and each static object sample image is respectively processed according to each configuration quantization result Image configuration processing, to obtain each configuration quantization static object sample image;It is combined with the road surface static object image respectively, to obtain each road surface static object enhancement image. Solve the problem that the number of images of road surface static object scene with speed reduction zone, manhole cover or rail is less, and image acquisition is time-consuming and laborious, improve the richness of image database, improve the efficiency of image acquisition of road surface static object scene.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method, apparatus, device, and medium for enhancing static images of road surfaces. Background Technology

[0002] In vehicles equipped with autonomous driving systems, camera-based environmental perception algorithms can acquire environmental information, providing reference information about the environment ahead for the vehicle's decision-making and control systems, thereby improving driving safety and comfort. With the development of computer hardware, environmental perception algorithms increasingly rely on machine learning methods, including deep learning-based segmentation algorithms. Environmental perception algorithms implemented using machine learning need to extract algorithm parameters from a large amount of diverse data; therefore, the amount of data in the dataset and its coverage of the scene significantly impact the final generalization performance of the machine learning algorithm.

[0003] In the process of realizing this invention, the inventors discovered the following defects in the existing technology: At present, the construction of datasets is generally achieved by data collection on real vehicles, which is time-consuming and labor-intensive, especially in scenarios with static objects on the road surface such as speed bumps, manhole covers, and railway tracks, requiring data collection personnel to spend extra effort to find suitable collection sites.

[0004] To construct an image database, data augmentation using random scaling and cropping can be employed during image segmentation algorithms. However, the enhancement effect remains limited by the presence of static objects in the original image and the original pose of those objects, thus offering limited simplification to the data acquisition process. Alternatively, random pasting can be used to generate augmented image sets; however, this method cannot guarantee the plausibility of object instances within the image background and may introduce labeling errors. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for enhancing images of static objects on a road surface, thereby improving the richness of the image database and increasing the efficiency of image acquisition for static object scenes on a road surface.

[0006] According to one aspect of the present invention, a method for enhancing a static object image of a road surface is provided, comprising:

[0007] Acquire static images of road surfaces to be enhanced, and perform environmental recognition on the static images of road surfaces to obtain environmental element recognition results;

[0008] Multiple comprehensive scores of target environmental elements are randomly obtained, and parameters are configured according to the comprehensive score calculation formula of environmental elements corresponding to the environmental element identification results, so as to obtain the configuration quantification results corresponding to the comprehensive scores of each target environmental element.

[0009] At least one static object sample image corresponding to the static object image on the road surface is obtained, and each static object sample image is processed according to the configuration quantization result to obtain each configuration quantized static object sample image.

[0010] Each configured quantized static object sample image is combined with the road surface static object image to obtain an enhanced image of each road surface static object.

[0011] According to another aspect of the present invention, an enhancement device for a static image of a road surface is provided, comprising:

[0012] The environmental element identification result determination module is used to acquire the static object image of the road surface to be enhanced, and to perform environmental identification on the static object image of the road surface to obtain the environmental element identification result.

[0013] The configuration quantification result determination module is used to randomly obtain the comprehensive scores of multiple target environmental elements, and configure parameters according to the comprehensive score calculation formula of the environmental element corresponding to the environmental element identification result, so as to obtain the configuration quantification result corresponding to each comprehensive score of the target environmental element.

[0014] A configuration quantization static object sample image determination module is used to obtain at least one static object sample image corresponding to a road surface static object image, and to perform image configuration processing on each static object sample image according to the configuration quantization results to obtain each configuration quantization static object sample image.

[0015] The road surface static object enhancement image module is used to combine each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhancement image.

[0016] According to another aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for enhancing static object images of road surfaces according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for enhancing static object images of road surfaces according to any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring an image of a static object on a road surface to be enhanced, performing environmental recognition on the image to obtain environmental element recognition results, randomly acquiring comprehensive scores for multiple target environmental elements, and configuring parameters according to the calculation formula for the comprehensive environmental element scores corresponding to the environmental element recognition results to obtain configuration quantization results corresponding to each target environmental element comprehensive score; acquiring at least one static object sample image corresponding to the road surface static object image, and performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image; and combining each configuration quantized static object sample image with the road surface static object image to obtain each enhanced image of the road surface static object. This solves the problem of a limited number of images for road surface static object scenes such as speed bumps, manhole covers, or railway tracks, and the time-consuming and labor-intensive image acquisition process, thereby improving the richness of the image database and the efficiency of image acquisition for road surface static object scenes.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for enhancing static object images on a road surface according to Embodiment 1 of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of a device for enhancing static object images on a road surface according to Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 3 of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "target," "current," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] Figure 1 The flowchart of a method for enhancing static road surface images is provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where static road surface images are enhanced and image databases are enriched. The method can be executed by a static road surface image enhancement device, which can be implemented in hardware and / or software.

[0028] Correspondingly, such as Figure 1 As shown, the method includes:

[0029] S110. Obtain the static object image of the road surface to be enhanced, and perform environmental recognition on the static object image of the road surface to obtain the environmental element recognition result.

[0030] The static road surface image can be an image of a static road surface object acquired in real time or an image that requires image enhancement. The environmental element recognition result can be the result of identifying environmental elements from the static road surface image.

[0031] In this embodiment, environmental recognition processing needs to be performed on the acquired multiple static road surface images to obtain different categories of environmental element recognition results. Specifically, the environmental element recognition results may include road surface geometric parameter recognition results, lighting condition recognition results, weather condition recognition results, and road surface area recognition results.

[0032] S120. Randomly obtain comprehensive scores of multiple target environmental elements, and configure parameters according to the comprehensive score calculation formula of the environmental elements corresponding to the environmental element identification results, so as to obtain the configuration quantification results corresponding to the comprehensive scores of each target environmental element.

[0033] The comprehensive score of the target environmental element can be a randomly obtained comprehensive score of the environmental element within a preset comprehensive score threshold. The calculation formula for the comprehensive environmental element score can be a calculation formula constructed based on the environmental element identification results and environmental element presentation coefficient parameters. The configuration quantization result can include the target configuration sample center, configuration channel illumination parameters, and configuration weather parameters, which are the results of configuration quantization of the static object sample image.

[0034] In this embodiment, when a comprehensive score of a target environmental element is obtained, parameters can be configured according to the comprehensive score of the target environmental element and the calculation formula of the comprehensive score of environmental elements, so as to obtain the corresponding parameter quantification results.

[0035] In addition, for randomly obtaining comprehensive scores of multiple target environmental elements, parameter configuration can be performed simultaneously through different processors according to the comprehensive scores of multiple target environmental elements, so as to realize batch configuration quantification processing of comprehensive scores of target environmental elements and obtain their respective corresponding configuration quantification results.

[0036] Optionally, the environmental element identification results include: road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results; before randomly obtaining the comprehensive scores of multiple target environmental elements and configuring parameters according to the environmental element comprehensive score calculation formula corresponding to the environmental element identification results, the method further includes: determining the road surface geometric parameter identification result score, illumination condition identification result score, and weather condition identification result score respectively based on the road surface geometric parameter identification results, illumination condition identification result, and weather condition identification results, and the parameter configuration scoring method matching each identification result; the environmental element comprehensive score calculation formula constructed based on the road surface geometric parameter identification result score, illumination condition identification result score, and weather condition identification result score, and based on the environmental element presentation coefficient parameter corresponding to the environmental element identification results, is: S enviornment =w1*S geometry +w2*S light +w3*S weather Among them, S enviornment S represents the comprehensive score of environmental factors. geometry S represents the score for the road surface geometric parameter identification result; light S represents the score for the illumination condition recognition result; weatherw1 represents the weather condition recognition result score; w2 represents the first environmental element presentation coefficient parameter corresponding to the road surface geometry parameter recognition result score; w3 represents the second environmental element presentation coefficient parameter corresponding to the lighting condition recognition result score; w1+w2+w3=1.

[0037] Specifically, the road surface geometric parameter recognition result can be the recognition result that describes the parameters of the road surface. The illumination condition recognition result can be the recognition result corresponding to the static object image of the road surface described by illumination conditions, and the recognition result can be described by RGB. The weather condition recognition result can be the recognition result corresponding to the static object image of the road surface based on weather conditions.

[0038] In this embodiment, the comprehensive environmental factor score has a parameter range, which can be set to 0 to 5. That is, the target comprehensive environmental factor score obtains a random number from the above parameter range, and the target comprehensive environmental factor score can be set to 4.

[0039] Specifically, the scoring process is performed by matching the road surface geometry parameter identification results, illumination condition identification results, and weather condition identification results with the parameter configuration scoring method that matches each identification result, thereby obtaining the scores for road surface geometry parameter identification results, illumination condition identification results, and weather condition identification results.

[0040] Furthermore, the scores for road surface geometry parameter identification, illumination condition identification, and weather condition identification also have certain parameter value ranges.

[0041] Correspondingly, each identification result score corresponds to an environmental element presentation coefficient parameter, and the sum of all environmental element presentation coefficient parameters is 1.

[0042] Optionally, the environmental element identification result further includes: road surface area identification result; the step of determining the road surface geometric parameter identification result score, illumination condition identification result score, and weather condition identification result score respectively based on the road surface geometric parameter identification result, illumination condition identification result, and weather condition identification result, and the parameter configuration scoring method matching each identification result, includes: obtaining the target sample center corresponding to the road surface static object image; dividing the road surface static object image into road surface areas based on the road surface area identification result to obtain a road surface divided static object image; wherein, the road surface divided static object image includes: a first divided road surface area, a second divided road surface area, and a third divided road surface area; and determining the road surface geometric parameter identification result score, illumination condition identification result score, and weather condition identification result score respectively based on the first divided road surface area, the second divided road surface area, the third divided road surface area, the road surface geometric parameter identification result, the illumination condition identification result, and the weather condition identification result, and the parameter configuration scoring method matching each identification result.

[0043] In this context, the target sample center can correspond to one sample center for each static object image of the road surface, and this sample center can be replaced by various configured quantized static object sample images. Understandably, it is necessary to analyze the situation of the target sample centers to determine the recognition result score.

[0044] Among them, the static object image of the road surface can be an image obtained by dividing the static object image of the road surface into road surface regions.

[0045] In this embodiment, different road surface geometry parameter recognition scores can be obtained when the center of the target sample hits different road surface regions. Additionally, a lighting condition recognition score is obtained by calculating the parameters between different road surface regions. Furthermore, a weather condition recognition score is determined based on the weather condition recognition results.

[0046] Optionally, the step of determining the scores for the road surface geometric parameter identification, illumination condition identification, and weather condition identification based on the first, second, and third road surface divisions, road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results, and the parameter configuration scoring method matching each identification result, includes: determining the scores for the road surface geometric parameter identification results and the formula based on the road surface geometric parameter identification results. The road surface geometric parameter recognition result score is determined; where M represents the target sample center; zone1 represents the first divided road surface region; zone2 represents the second divided road surface region; zone3 represents the third divided road surface region; based on the illumination condition recognition result, the channel illumination parameters corresponding to each divided road surface region are determined, and the root mean square error of the parameters is calculated based on the illumination parameters of each channel; the root mean square error threshold of each parameter is obtained, and the result is calculated according to the formula... The lighting condition recognition result score is determined; where RMSE represents the root mean square error of the parameters; α1 represents the first root mean square error threshold; α2 represents the second root mean square error threshold; α3 represents the third root mean square error threshold; and α1 < α2 < α3; based on the weather condition recognition result, and according to the formula... Determine the weather condition identification result score.

[0047] The channel illumination parameters can be obtained by describing different road surface regions using RGB parameters. The root mean square error (RMSE) describes the magnitude of the RMSE difference between the current channel illumination parameters and the standard channel illumination parameters. The RMSE threshold can be a pre-set threshold value for the RMSE. The RMSE threshold includes a first RMSE threshold, a second RMSE threshold, and a third RMSE threshold.

[0048] In this embodiment, it is necessary to determine whether the center of the target sample falls into the first, second, or third road surface division area, and obtain the corresponding road surface geometric parameter recognition result score based on whether it falls into the different road surface division areas.

[0049] Specifically, when the center of the target sample falls into the first divided road surface area, the road surface geometric parameter recognition result score is 1; when the center of the target sample falls into the second divided road surface area, the road surface geometric parameter recognition result score is 2; and when the center of the target sample falls into the third divided road surface area, the road surface geometric parameter recognition result score is 3.

[0050] In this embodiment, it is assumed that the channel illumination parameters corresponding to each divided road surface region are determined based on the illumination condition recognition results. Specifically, the channel illumination parameters corresponding to the first divided road surface region are R1, G1, B1; the channel illumination parameters corresponding to the second divided road surface region are R2, G2, B2; and the channel illumination parameters corresponding to the third divided road surface region are R3, G3, B3. Further, the root mean square error (RMSE) of the parameters is calculated based on the above channel illumination parameters. Then, this RMSE is compared with the first, second, and third RMSE thresholds to obtain the illumination condition recognition result score.

[0051] In addition, the weather condition recognition result can determine whether the current weather condition is normal, rainy, foggy, or snowy, thereby determining the weather condition recognition result score.

[0052] S130. Obtain at least one static object sample image corresponding to the static object image on the road surface, and perform image configuration processing on each static object sample image according to the configuration quantization result to obtain each configured quantized static object sample image.

[0053] The static object sample images can include sample images such as speed bumps, manhole covers, or railway tracks. The configured quantized static object sample image can be an image obtained by configuring the static object sample image according to the configured quantization result.

[0054] In this embodiment, a static object sample image can be configured using different configuration quantization results to obtain static object sample images with different configuration quantizations.

[0055] Optionally, the configuration quantization result includes: target configuration sample center, configuration channel illumination parameters, and configuration weather parameters; the step of performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image includes: performing image configuration processing on each static object sample image sequentially according to the target configuration sample center, configuration channel illumination parameters, and configuration weather parameters to obtain each configuration quantized static object sample image.

[0056] In this embodiment, it is assumed that the target configuration sample center O(x1,y1), the configuration channel illumination parameters (R1,G1,B1), and the configuration weather parameters C are in the configuration quantization result. weather Assuming the static object sample image is a manhole cover, the manhole cover is configured according to the above parameters to obtain a configured quantized static object sample image.

[0057] The advantage of this setting is that by configuring the quantization results, the static object sample image is processed with image configuration. The resulting configured quantized static object sample image matches the descriptive parameters in the road surface static object image. This enhanced road surface static object image can be better used for model training.

[0058] S140. Combine each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhanced image.

[0059] In this embodiment, the road surface area where the center of the target sample is hit is replaced by the configured quantized static object sample image, thereby obtaining the road surface static object enhancement image.

[0060] Optionally, the step of combining each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhancement image includes: acquiring a road surface segmentation static object image; and combining each configured quantized static object sample image with the road surface segmentation static object image to obtain each road surface static object enhancement image.

[0061] In this embodiment, it is assumed that the road surface segmentation static object image includes a first segmented road surface region, a second segmented road surface region, and a third segmented road surface region, and the target sample center hits the first segmented road surface region. Then, the configured quantized static object sample image is used to cover the first segmented road surface region corresponding to the road surface segmentation static object image, thereby obtaining an enhanced road surface static object image.

[0062] Optionally, after combining each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhanced image, the method further includes: adding each road surface static object enhanced image and the road surface static object image to an image library, so as to train a road surface static object segmentation algorithm model through the constructed image library.

[0063] In this embodiment, an image library is constructed using enhanced images of static road objects and images of static road objects. The images in the constructed image library can be used as training image samples to train a static road object segmentation algorithm model. It is understood that training the static road object segmentation algorithm model also includes parameter optimization operations.

[0064] The technical solution of this invention involves acquiring an image of a static object on a road surface to be enhanced, performing environmental recognition on the image to obtain environmental element recognition results, randomly acquiring comprehensive scores for multiple target environmental elements, and configuring parameters according to the calculation formula for the comprehensive environmental element scores corresponding to the environmental element recognition results to obtain configuration quantization results corresponding to each target environmental element comprehensive score; acquiring at least one static object sample image corresponding to the road surface static object image, and performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image; and combining each configuration quantized static object sample image with the road surface static object image to obtain each enhanced image of the road surface static object. This solves the problem of a limited number of images for road surface static object scenes such as speed bumps, manhole covers, or railway tracks, and the time-consuming and labor-intensive image acquisition process, thereby improving the richness of the image database and the efficiency of image acquisition for road surface static object scenes.

[0065] Example 2

[0066] Figure 2This is a schematic diagram of a road surface static object image enhancement device provided in Embodiment 2 of the present invention. The road surface static object image enhancement device provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or server to implement a road surface static object image enhancement method according to the embodiments of the present invention. Figure 2 As shown, the device includes: an environmental element identification result determination module 210, a configuration quantization result determination module 220, a configuration quantization static object sample image determination module 230, and a road surface static object enhancement image determination module 240.

[0067] Among them, the environmental element identification result determination module 210 is used to acquire the static object image of the road surface to be enhanced, and to perform environmental identification on the static object image of the road surface to obtain the environmental element identification result.

[0068] The configuration quantification result determination module 220 is used to randomly obtain the comprehensive scores of multiple target environmental elements, and configure parameters according to the environmental element comprehensive score calculation formula corresponding to the environmental element identification result, so as to obtain the configuration quantification result corresponding to each of the target environmental element comprehensive scores.

[0069] The configuration quantization static object sample image determination module 230 is used to obtain at least one static object sample image corresponding to the road surface static object image, and to perform image configuration processing on each static object sample image according to the configuration quantization result to obtain each configuration quantization static object sample image.

[0070] The road surface static object enhancement image module 240 is used to combine each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhancement image.

[0071] The technical solution of this invention involves acquiring an image of a static object on a road surface to be enhanced, performing environmental recognition on the image to obtain environmental element recognition results, randomly acquiring comprehensive scores for multiple target environmental elements, and configuring parameters according to the calculation formula for the comprehensive environmental element scores corresponding to the environmental element recognition results to obtain configuration quantization results corresponding to each target environmental element comprehensive score; acquiring at least one static object sample image corresponding to the road surface static object image, and performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image; and combining each configuration quantized static object sample image with the road surface static object image to obtain each enhanced image of the road surface static object. This solves the problem of a limited number of images for road surface static object scenes such as speed bumps, manhole covers, or railway tracks, and the time-consuming and labor-intensive image acquisition process, thereby improving the richness of the image database and the efficiency of image acquisition for road surface static object scenes.

[0072] Optionally, the environmental element identification results include: road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results.

[0073] Optionally, it also includes a comprehensive environmental element score calculation formula construction module, which can be specifically used to: before randomly acquiring multiple target environmental element comprehensive scores and configuring parameters according to the comprehensive environmental element score calculation formula corresponding to the environmental element identification results, determine the road surface geometry parameter identification result score, illumination condition identification result score, and weather condition identification result score respectively based on the road surface geometry parameter identification results, illumination condition identification result, and weather condition identification results, as well as the parameter configuration scoring method matching each identification result; and construct the comprehensive environmental element score calculation formula based on the road surface geometry parameter identification result score, illumination condition identification result score, and weather condition identification result score, and based on the environmental element presentation coefficient parameter corresponding to the environmental element identification results.

[0074] S enviornment =w1*S geometry +w2*S light +w3*S weather Among them, S enviornment

[0075] S represents the comprehensive score of environmental factors. geometry S represents the score for the road surface geometric parameter identification result; light S represents the score for the illumination condition recognition result; weatherw1 represents the weather condition recognition result score; w2 represents the first environmental element presentation coefficient parameter corresponding to the road surface geometry parameter recognition result score; w3 represents the second environmental element presentation coefficient parameter corresponding to the lighting condition recognition result score; w1+w2+w3=1.

[0076] Optionally, the environmental element identification result further includes: road surface area identification result. The environmental element comprehensive scoring calculation formula construction module can also be specifically used to: obtain the target sample center corresponding to the road surface static object image; divide the road surface static object image into road surface areas based on the road surface area identification result to obtain a road surface divided static object image; wherein, the road surface divided static object image includes: a first divided road surface area, a second divided road surface area, and a third divided road surface area; and determine the road surface geometric parameter identification result score, the lighting condition identification result score, and the weather condition identification result score respectively based on the first divided road surface area, the second divided road surface area, the third divided road surface area, the road surface geometric parameter identification result, the lighting condition identification result, and the weather condition identification result, as well as the parameter configuration scoring method matched with each identification result.

[0077] Optionally, the environmental element comprehensive scoring calculation formula construction module can also be specifically used for: identifying road surface geometric parameters and formulas. The road surface geometric parameter recognition result score is determined; where M represents the target sample center; zone1 represents the first divided road surface region; zone2 represents the second divided road surface region; zone3 represents the third divided road surface region; based on the illumination condition recognition result, the channel illumination parameters corresponding to each divided road surface region are determined, and the root mean square error of the parameters is calculated based on the illumination parameters of each channel; the root mean square error threshold of each parameter is obtained, and the result is calculated according to the formula... The lighting condition recognition result score is determined; where RMSE represents the root mean square error of the parameters; α1 represents the first root mean square error threshold; α2 represents the second root mean square error threshold; α3 represents the third root mean square error threshold; and α1 < α2 < α3; based on the weather condition recognition result, and according to the formula... Determine the weather condition identification result score.

[0078] Optionally, the configuration quantization result includes: target configuration sample center, configuration channel illumination parameters, and configuration weather parameters. The configuration quantization static object sample image determination module 230 can be specifically used to: perform image configuration processing on each static object sample image sequentially based on the target configuration sample center, configuration channel illumination parameters, and configuration weather parameters, to obtain each configuration quantized static object sample image.

[0079] Optionally, the road surface static object enhancement image module 240 can be specifically used to: acquire road surface segmentation static object images; and combine each configured quantized static object sample image with the road surface segmentation static object images to obtain each road surface static object enhancement image.

[0080] Optionally, it also includes an image library construction module, which can be specifically used to: after combining each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhanced image, add each road surface static object enhanced image and the road surface static object image to the image library, so as to train the road surface static object segmentation algorithm model through the constructed image library.

[0081] The road surface static object image enhancement device provided in the embodiments of the present invention can execute the road surface static object image enhancement method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0082] Example 3

[0083] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement Embodiment 3 of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0084] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0085] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0086] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for enhancing static object images of road surfaces.

[0087] In some embodiments, the method for enhancing a static image of a road surface can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for enhancing a static image of a road surface described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for enhancing a static image of a road surface by any other suitable means (e.g., by means of firmware).

[0088] The method includes: acquiring a static object image of a road surface to be enhanced, and performing environmental recognition on the static object image to obtain environmental element recognition results; randomly acquiring comprehensive scores of multiple target environmental elements, and configuring parameters according to the comprehensive score calculation formula corresponding to the environmental element recognition results to obtain configuration quantization results corresponding to each target environmental element comprehensive score; acquiring at least one static object sample image corresponding to the static object image of the road surface, and performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image; and combining each configuration quantized static object sample image with the static object image of the road surface to obtain each enhanced static object image of the road surface.

[0089] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0093] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0094] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0095] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0096] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0097] Example 4

[0098] Embodiment 4 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions, when executed by a computer processor, are used to perform a method for enhancing a static object image of a road surface. The method includes: acquiring a static object image of a road surface to be enhanced, and performing environmental recognition on the static object image of the road surface to obtain environmental element recognition results; randomly acquiring multiple comprehensive scores of target environmental elements, and configuring parameters according to the environmental element comprehensive score calculation formula corresponding to the environmental element recognition results to obtain configuration quantization results corresponding to each of the target environmental element comprehensive scores; acquiring at least one static object sample image corresponding to the static object image of the road surface, and performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image; and combining each configuration quantized static object sample image with the static object image of the road surface to obtain each enhanced static object image of the road surface.

[0099] Of course, the computer-executable instructions provided in the embodiments of the present invention, which include a computer-readable storage medium, are not limited to the method operations described above, but can also perform related operations in the method for enhancing static object images of road surfaces provided in any embodiment of the present invention.

[0100] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0101] It is worth noting that in the embodiments of the above-mentioned road surface static object image enhancement device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for enhancing static object images on a road surface, characterized in that, include: Acquire static images of road surfaces to be enhanced, and perform environmental recognition on the static images of road surfaces to obtain environmental element recognition results; Multiple comprehensive scores of target environmental elements are randomly obtained, and parameters are configured according to the comprehensive score calculation formula of environmental elements corresponding to the environmental element identification results, so as to obtain the configuration quantification results corresponding to the comprehensive scores of each target environmental element. Obtain at least one static object sample image corresponding to the static object image on the road surface, and perform image configuration processing on each static object sample image according to the configuration quantization results to obtain each configured quantized static object sample image; Each configured quantized static object sample image is combined with the road surface static object image to obtain each road surface static object enhanced image; The environmental element identification results include: road surface geometric parameter identification results, lighting condition identification results, and weather condition identification results; Before randomly acquiring the comprehensive scores of multiple target environmental elements and configuring parameters according to the comprehensive environmental element score calculation formula corresponding to the environmental element identification results, the method further includes: Based on the road surface geometry parameter identification results, illumination condition identification results, and weather condition identification results, as well as the parameter configuration scoring method matching each identification result, the scores for road surface geometry parameter identification results, illumination condition identification results, and weather condition identification results are determined respectively. The comprehensive environmental element score calculation formula, constructed based on the scores of the road surface geometric parameter identification, the illumination condition identification, and the weather condition identification, as well as the environmental element presentation coefficient parameters corresponding to the environmental element identification results, is as follows: ； in, This represents the overall score of environmental factors; This indicates the score for the road surface geometric parameter identification result; This indicates the score for the illumination condition recognition result; This indicates the score for the weather condition recognition result; This represents the first environmental element presentation coefficient parameter corresponding to the score of the road surface geometric parameter identification result; This represents the second environmental element presentation coefficient parameter corresponding to the lighting condition recognition result score; This represents the third environmental element presentation coefficient parameter corresponding to the weather condition identification result score; ; The environmental element identification results also include: road surface area identification results; Obtain the center of the target sample corresponding to the static object image of the road surface; Among them, the target sample center is a sample center corresponding to each static object image of the road surface, and the sample center is replaced by the static object sample images of each configuration quantization. Based on the road surface region recognition results, the road surface static object image is divided into road surface regions to obtain a road surface divided static object image; The road surface segmentation static object image includes: a first segmented road surface region, a second segmented road surface region, and a third segmented road surface region. Specifically, different road surface geometry parameter recognition scores are obtained when the center of the target sample hits different road surface regions; the illumination condition recognition score is obtained by calculating the parameters between different road surface regions. Based on the first, second, and third road surface divisions, the road surface geometric parameter identification results, the illumination condition identification results, and the weather condition identification results, as well as the parameter configuration scoring method matching each identification result, the scores for the road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results are determined respectively. Specifically, the road surface area where the center of the target sample is hit is replaced by the configured quantized static object sample image to obtain the road surface static object enhancement image.

2. The method according to claim 1, characterized in that, The step of determining the scores for road surface geometric parameter identification, illumination condition identification, and weather condition identification based on the first, second, and third road surface divisions, road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results, and the parameter configuration scoring method matching each identification result, includes: Based on the road surface geometric parameter identification results and formula The road surface geometric parameters are identified and scored. Where M represents the center of the target sample; This indicates the first division of the road surface area; This indicates the second division of the road surface area; This indicates the third division of the road surface area; Based on the illumination condition recognition results, the channel illumination parameters corresponding to each divided road surface area are determined, and the root mean square error of the parameters is calculated based on the illumination parameters of each channel. Obtain the root mean square error thresholds for each value, and then apply the formula... The lighting condition recognition result score is determined; Wherein, RMSE represents the root mean square error of the parameter; This represents the first root mean square error threshold; This represents the second root mean square error threshold; This represents the third root mean square error threshold; and ; Identify the results based on weather conditions, and apply the formula: The weather condition identification result score is determined.

3. The method according to claim 2, characterized in that, The configuration quantification results include: target configuration sample center, configuration channel illumination parameters, and configuration weather parameters; The step of performing image configuration processing on each static object sample image according to each configuration quantization result to obtain each configuration quantized static object sample image includes: Based on the target configuration sample center, configuration channel illumination parameters, and configuration weather parameters, image configuration processing is performed on each of the static object sample images in sequence to obtain each configured quantized static object sample image.

4. The method according to claim 3, characterized in that, The step of combining each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhanced image includes: Acquire static object images of road surface division; Each configured quantized static object sample image is combined with the road surface segmentation static object image to obtain an enhanced image of each road surface static object.

5. The method according to claim 4, characterized in that, After combining each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhanced image, the method further includes: The enhanced images of each static object on the road surface and the images of the static objects on the road surface are added to an image library, so as to train a static object segmentation algorithm model on the road surface using the constructed image library.

6. An enhancement device for static object images on a road surface, characterized in that, include: The environmental element identification result determination module is used to acquire the static object image of the road surface to be enhanced, and to perform environmental identification on the static object image of the road surface to obtain the environmental element identification result. The configuration quantification result determination module is used to randomly obtain the comprehensive scores of multiple target environmental elements, and configure parameters according to the comprehensive score calculation formula of the environmental element corresponding to the environmental element identification result, so as to obtain the configuration quantification result corresponding to each comprehensive score of the target environmental element. A configuration quantization static object sample image determination module is used to acquire at least one static object sample image corresponding to a road surface static object image, and to perform image configuration processing on each static object sample image according to the configuration quantization result to obtain each configuration quantization static object sample image. The road surface static object enhancement image module is used to combine each configured quantized static object sample image with the road surface static object image to obtain each road surface static object enhancement image; The environmental element identification results include: road surface geometric parameter identification results, lighting condition identification results, and weather condition identification results; This includes a module for constructing a comprehensive environmental element scoring formula, used to: before randomly acquiring multiple target environmental element comprehensive scores and configuring parameters according to the environmental element comprehensive scoring formula corresponding to the environmental element identification results, determine the road surface geometry parameter identification result score, illumination condition identification result score, and weather condition identification result score respectively based on the road surface geometry parameter identification results, illumination condition identification results, and weather condition identification results, as well as the parameter configuration scoring method matching each identification result; and construct the comprehensive environmental element scoring formula based on the road surface geometry parameter identification result score, illumination condition identification result score, and weather condition identification result score, and based on the environmental element presentation coefficient parameter corresponding to the environmental element identification results. ;in, This represents the overall score of environmental factors; This indicates the score for the road surface geometric parameter identification result; This indicates the score for the illumination condition recognition result; This indicates the score for the weather condition recognition result; This represents the first environmental element presentation coefficient parameter corresponding to the score of the road surface geometric parameter identification result; This represents the second environmental element presentation coefficient parameter corresponding to the lighting condition recognition result score; This represents the third environmental element presentation coefficient parameter corresponding to the weather condition identification result score; ; The environmental element identification results also include: road surface area identification results; the environmental element comprehensive score calculation formula construction module is further used to: obtain the target sample center corresponding to the road surface static object image; Among them, the target sample center is a sample center corresponding to each static object image of the road surface, and the sample center is replaced by the static object sample images of each configuration quantization. Based on the road surface region recognition result, the road surface static object image is divided into road surface regions to obtain a road surface divided static object image; wherein, the road surface divided static object image includes: a first divided road surface region, a second divided road surface region, and a third divided road surface region; Specifically, different road surface geometry parameter recognition scores are obtained when the center of the target sample hits different road surface regions; the illumination condition recognition score is obtained by calculating the parameters between different road surface regions. Based on the first, second, and third road surface divisions, the road surface geometric parameter identification results, the illumination condition identification results, and the weather condition identification results, as well as the parameter configuration scoring method matching each identification result, the scores for the road surface geometric parameter identification results, illumination condition identification results, and weather condition identification results are determined respectively. Specifically, the road surface area where the center of the target sample is hit is replaced by the configured quantized static object sample image to obtain the road surface static object enhancement image.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for enhancing static object images of road surfaces as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for enhancing a static image of a road surface as described in any one of claims 1-5.

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