Traffic load prediction method and related device based on image enhancement

Abstract

The embodiments of the present application relate to the field of image processing and provide a traffic load prediction method and related apparatus based on image enhancement. The method comprises: acquiring traffic images of a road area to be detected within a traffic load information collection period to obtain a first traffic image set; performing defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set; performing target extraction on a second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected; determining the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection period, being able to perform defogging and enhancement processing on the traffic images, and using the enhanced traffic images to perform traffic load prediction, thereby improving the accuracy of traffic load prediction.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a traffic load prediction method based on image enhancement and related devices. Background Art

[0002] Images captured in foggy weather will have reduced contrast and image quality due to light absorption and scattering, and the features of objects will become blurred, making it impossible to obtain more image details, resulting in reduced accuracy when using images for traffic load prediction. Summary of the Invention

[0003] The embodiments of the present application provide a traffic load prediction method and related devices based on image enhancement, which can perform defogging and enhancement processing on traffic images, and use the enhanced traffic images to predict traffic load, thereby improving the accuracy of traffic load prediction.

[0004] A first aspect of an embodiment of the present application provides a traffic load prediction method based on image enhancement, the method comprising:

[0005] Acquire traffic images of the road area to be detected within the traffic load information collection period to obtain a first traffic image set;

[0006] performing defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set;

[0007] performing target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected;

[0008] The traffic load information of the road area to be detected is determined according to the vehicle information and the traffic load information collection time.

[0009] In this example, traffic images of the road area to be detected within the traffic load information collection time are obtained to obtain a first traffic image set, dehazing and enhancing the first traffic image in the first traffic image set to obtain a second traffic image set, and target extraction is performed on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected. Based on the vehicle information and the traffic load information collection time, the traffic load information of the road area to be detected is determined. Therefore, the traffic images can be dehazing and enhanced, and the enhanced traffic images can be used to perform traffic load prediction, thereby improving the accuracy of traffic load prediction.

[0010] In one possible implementation, performing defogging and enhancement processing on the first traffic image in the first traffic image set to obtain the second traffic image set includes:

[0011] determining a first transmittance correction value according to environmental information of the road area to be detected;

[0012] Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set;

[0013] Performing transmittance calculation based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance;

[0014] performing defogging processing on the target traffic image using the target transmittance to obtain a second traffic image;

[0015] Repeat the above steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, where the target traffic image is any first traffic image in the first traffic image set, and performing dehazing processing on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtaining the second traffic image set based on each second traffic image.

[0016] In a possible implementation, determining the first transmittance correction value according to the environmental information of the road area to be detected includes:

[0017] extracting wind speed information, solid particulate matter information, and humidity information from the environmental information;

[0018] Acquire a joint influence degree between the humidity information and the solid particulate matter information;

[0019] determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information;

[0020] determining haze dispersion trend information based on the wind speed information;

[0021] determining a second reference transmittance correction value according to the haze dispersion trend information;

[0022] The first reference transmittance correction value and the second reference transmittance correction value are fused to obtain the first transmittance correction value.

[0023] In one possible implementation, obtaining the background light intensity value corresponding to the target traffic image to obtain the target background light intensity value includes:

[0024] Acquire an associated image of the target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image;

[0025] Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set;

[0026] Determining a background light intensity correction value based on reference background light intensity values ​​and corresponding variances in the reference background light intensity value set;

[0027] Acquire a first background light intensity value of the target traffic image;

[0028] The first background light intensity value is corrected using the background light intensity correction value to obtain the target background light intensity value.

[0029] In one possible implementation, determining the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection duration includes:

[0030] Extracting sub-vehicle information of each lane in the road area to be detected from the vehicle information to obtain a sub-vehicle information set;

[0031] The traffic load information of the road area to be detected is determined according to the sub-vehicle information set and the traffic load information collection time.

[0032] A second aspect of an embodiment of the present application provides a traffic load prediction device based on image enhancement, the device comprising:

[0033] an acquisition unit, configured to acquire traffic images of the road area to be detected within a traffic load information collection period to obtain a first traffic image set;

[0034] an enhancement unit, configured to perform defogging and enhancement processing on the first traffic image in the first traffic image set to obtain a second traffic image set;

[0035] an extraction unit, configured to perform target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected;

[0036] The determining unit is configured to determine the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection time.

[0037] In one possible implementation, the enhancement unit is specifically configured to:

[0038] determining a first transmittance correction value according to environmental information of the road area to be detected;

[0039] Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set;

[0040] Performing transmittance calculation based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance;

[0041] performing defogging processing on the target traffic image using the target transmittance to obtain a second traffic image;

[0042] Repeat the above steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, where the target traffic image is any first traffic image in the first traffic image set, and performing dehazing processing on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtaining the second traffic image set based on each second traffic image.

[0043] In a possible implementation, in determining the first transmittance correction value according to the environmental information of the road area to be detected, the enhancing unit is specifically configured to:

[0044] extracting wind speed information, solid particulate matter information, and humidity information from the environmental information;

[0045] Acquire a joint influence degree between the humidity information and the solid particulate matter information;

[0046] determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information;

[0047] determining haze dispersion trend information based on the wind speed information;

[0048] determining a second reference transmittance correction value according to the haze dispersion trend information;

[0049] The first reference transmittance correction value and the second reference transmittance correction value are fused to obtain the first transmittance correction value.

[0050] In one possible implementation, in acquiring the background light intensity value corresponding to the target traffic image to obtain the target background light intensity value, the enhancement unit is specifically configured to:

[0051] Acquire an associated image of the target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image;

[0052] Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set;

[0053] Determining a background light intensity correction value based on reference background light intensity values ​​and corresponding variances in the reference background light intensity value set;

[0054] Acquire a first background light intensity value of the target traffic image;

[0055] The first background light intensity value is corrected using the background light intensity correction value to obtain the target background light intensity value.

[0056] In one possible implementation, the determining unit is specifically configured to:

[0057] Extracting sub-vehicle information of each lane in the road area to be detected from the vehicle information to obtain a sub-vehicle information set;

[0058] The traffic load information of the road area to be detected is determined according to the sub-vehicle information set and the traffic load information collection time.

[0059] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions and execute the step instructions in the first aspect of the embodiment of the present application.

[0060] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0061] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0063] Figure 1A flow chart of a traffic load prediction method based on image enhancement is provided for an embodiment of the present application;

[0064] Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0065] Figure 3 A structural schematic diagram of a traffic load prediction device based on image enhancement is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0067] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0068] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0069] In order to better understand the traffic load prediction method based on image enhancement provided in the embodiment of the present application, the following first briefly introduces the scenario of applying the traffic load prediction method based on image enhancement. When predicting the traffic load of a road, it is usually for the purpose of analyzing the traffic in order to carry out better urban road planning, traffic diversion, etc. At this time, it is necessary to combine the real-time traffic conditions of the road with the historical traffic conditions for analysis. When conducting real-time traffic load analysis, due to the influence of factors such as weather and environment, there will be some inaccurate analysis through image analysis. For example, when there is heavy fog or haze weather, due to the influence of haze, the accuracy of directly extracting vehicle information from the image will be sharply reduced. Even when the visibility of haze is very low, if the vehicle is directly identified, it will lead to recognition errors, or the vehicle cannot be correctly identified, thereby affecting the prediction of road load, etc. In existing solutions, when performing vehicle recognition on traffic images, background light extraction is usually performed on the captured images. After extracting the background light intensity value, the transmittance is directly calculated without analyzing the severity of haze, etc., resulting in poor image enhancement results. As a result, the accuracy of vehicle information extraction using the enhanced images is reduced, which ultimately leads to a reduction in the accuracy of traffic load acquisition, thereby affecting the accuracy of subsequent road traffic diversion.

[0070] In order to solve the above problems, an embodiment of the present application provides a traffic load prediction method based on image enhancement, which can combine the environmental information of the road to be detected to determine a more accurate transmittance, and then use the transmittance to enhance the traffic image, thereby improving the image quality of the enhanced traffic image, and thus improving the accuracy of subsequent traffic load acquisition.

[0071] See also Figure 1 , Figure 1 The present invention provides a flow chart of a traffic load prediction method based on image enhancement. Figure 1 As shown, the method includes:

[0072] 101. Obtain traffic images of a road area to be detected within a traffic load information collection period to obtain a first traffic image set.

[0073] The road area to be inspected may be a road area with traffic congestion, such as a road area with traffic congestion during rush hour, an intersection, or a bridge intersection. The traffic load information acquisition duration can be understood as the duration of image sampling of the road to be inspected. For example, it can be a duration during a traffic congestion period, such as 10 minutes or 15 minutes. Within the acquisition duration, image acquisition is performed according to a certain acquisition cycle, such as a 0.5s or 1s acquisition cycle. Multiple images are acquired during each acquisition cycle, thereby obtaining a first traffic image set. The first traffic image includes the road area to be inspected and vehicles within the road area to be inspected.

[0074] 102. Perform defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set.

[0075] Environmental monitoring sensors can simultaneously collect environmental information about the road area to be inspected during image acquisition. This information includes information about wind speed, solid particulate matter, and air humidity. After collecting this environmental information, a transmittance correction value is determined. The background light intensity corresponding to the first traffic image is extracted. This transmittance correction value and the background light intensity are then used to calculate the transmittance of the first traffic image. Finally, this transmittance is used to defog the first traffic image to obtain a second traffic image. Defogging the first traffic image using this transmittance can be performed using a common defogging method to obtain the second traffic image.

[0076] 103. Perform object extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected.

[0077] A general target extraction method can be used to extract targets from the second traffic image to obtain vehicle information in the road area to be detected. For example, a general target extraction network or a general target extraction algorithm can be used for extraction. This is merely an example.

[0078] 104. Determine the traffic load information of the road area to be inspected based on the vehicle information and traffic load information collection time.

[0079] The sub-vehicle information for each lane in the road area to be inspected can be extracted from the vehicle information, and the traffic load information can be determined based on the sub-vehicle information and the duration of traffic load information collection. The traffic load information can reflect the average traffic volume in the road area to be inspected during the inspection period.

[0080] In this example, traffic images of the road area to be detected within the traffic load information collection time are obtained to obtain a first traffic image set, dehazing and enhancing the first traffic image in the first traffic image set to obtain a second traffic image set, and target extraction is performed on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected. Based on the vehicle information and the traffic load information collection time, the traffic load information of the road area to be detected is determined. Therefore, the traffic images can be dehazing and enhanced, and the enhanced traffic images can be used to perform traffic load prediction, thereby improving the accuracy of traffic load prediction.

[0081] In one possible implementation, a method for performing defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set includes:

[0082] A1. Determine a first transmittance correction value based on environmental information of the road area to be detected;

[0083] A2. Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set;

[0084] A3. Calculating transmittance based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance;

[0085] A4. Defogging the target traffic image using the target transmittance to obtain a second traffic image;

[0086] A5. Repeat the above steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, where the target traffic image is any first traffic image in the first traffic image set, and performing dehazing processing on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtain the second traffic image set based on each second traffic image.

[0087] Wind speed information, solid particulate matter information, and humidity information can be extracted from the environmental information. The humidity and solid particulate matter information are then used to determine a first reference transmittance correction value, while the wind speed information is used to determine a second reference transmittance correction value. Finally, the first reference transmittance correction value and the second reference transmittance correction value are fused to obtain the first transmittance correction value. Therefore, the first transmittance correction value can be determined by combining wind speed information, solid particulate matter information, and humidity information from the environmental information, thereby improving the accuracy of obtaining the first transmittance correction value.

[0088] When obtaining the target background light intensity value of the target traffic image, a general background light intensity value obtaining method may be used to obtain the target background light intensity value. For example, the target background light intensity value may be obtained by calculating the Euclidean distance between pixels.

[0089] Alternatively, associated images of the target traffic image may be extracted. The associated images are first traffic images captured within a preset time interval with the target traffic image, for example, images captured k moments before and k moments after the target traffic image. A reference background light intensity value and the variance of the corresponding background light intensity values ​​are calculated for each associated image in the associated image set. Finally, the variance and the reference background light intensity value are used to determine a correction value for the first background light intensity value corresponding to the target image, and correction processing is performed to obtain the target background light intensity value.

[0090] The transmittance can be calculated based on the target background light intensity value and the first transmittance correction value using the method shown in the following formula to obtain the target transmittance, specifically:

[0091] ,

[0092] in, is the target transmittance, is the first transmittance correction value, is the pixel value of the first sub-image block in the image block group, A is the target background light intensity value, c is the three color channels, is the minimum filter window, is the pixel point within the filter window, To obtain the minimum value, It is the value of the target background light intensity in three color channels.

[0093] A general defogging method may be used to perform defogging on the target traffic image using the target transmittance to obtain the second traffic image.

[0094] In this example, a first transmittance correction value is determined in combination with environmental information, and the target transmittance is calculated using the first transmittance correction value and the target background light intensity value of the target traffic image. Finally, the target transmittance is used to dehaze the target traffic image, thereby improving the image quality of the obtained second traffic image.

[0095] In one possible implementation, a method for determining a first transmittance correction value based on environmental information of the road area to be detected includes:

[0096] B1. extracting wind speed information, solid particulate matter information, and humidity information from the environmental information;

[0097] B2. Obtaining the joint influence between the humidity information and the solid particulate matter information;

[0098] B3. determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information;

[0099] B4. determining haze dispersion trend information based on the wind speed information;

[0100] B5. determining a second reference transmittance correction value based on the haze dispersion trend information;

[0101] B6. Fusing the first reference transmittance correction value and the second reference transmittance correction value to obtain the first transmittance correction value.

[0102] The combined impact of humidity and solid particulate matter information can be used to understand the degree of visual impact of solid particulate matter at different humidity levels. Solid particulate matter information can include information on solid particulate matter concentration and type. Different types of particulate matter have varying degrees of visual impact at varying concentrations and humidity levels. For example, for dust-based particulate matter, higher concentrations and higher humidity levels increase the viscosity of the dust particles, leading to greater visual impairment and increased obstruction of vehicles, resulting in lower transmittance. Other types of particulate matter, such as ash (fine particles produced during combustion, such as silicate particles from coal and wood burning); fog (small droplets or ice crystals formed by condensation of water vapor); and haze (particulate matter formed by dust or salt particles suspended in the atmosphere, causing the atmosphere to appear turbid and light blue or slightly yellow), all have corresponding information on their impact on transmittance. Therefore, a quantitative analysis can be performed combining the combined impact, humidity, and solid particulate matter information to determine the first reference transmittance correction value.

[0103] Determining haze dispersion trend information based on wind speed information can be understood as the presence of wind accelerates the speed of haze dispersion. Therefore, corresponding dispersion acceleration information can be determined based on wind speed information (including wind force and direction). Greater wind speeds increase the acceleration of dispersion, while lower wind speeds decrease the acceleration. Furthermore, the presence of wind imparts a certain tendency to haze dispersion, which is typically correlated with wind direction. Haze dispersion trend information can be determined based on wind speed information using common aerodynamic analysis methods. Since haze dispersion is gradual, the transmittance changes gradually as it disperses. Therefore, a second reference transmittance correction value can be determined based on the dispersion trend information. A greater dispersion force in the dispersion trend information indicates a greater likelihood of haze dispersal, resulting in a faster increase in transmittance, and vice versa. The second reference transmittance correction value can be determined based on the correlation between dispersion force and transmittance.

[0104] The weights of the first reference transmittance correction value and the second reference transmittance correction value may be obtained respectively, and weighted operations are performed according to the corresponding weights to perform fusion processing to obtain the first transmittance correction value.

[0105] In this example, the first transmittance correction value is determined by the humidity information, the solid particle information and the wind speed in the environmental information, so that the first transmittance correction value can be determined in combination with the trend of environmental changes, thereby improving the accuracy of determining the first transmittance correction value.

[0106] In one possible implementation, a method for obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value includes:

[0107] C1. Acquire an associated image of a target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image;

[0108] C2. Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set;

[0109] C3. determining a background light intensity correction value based on the reference background light intensity values ​​and corresponding variances in the reference background light intensity value set;

[0110] C4. Obtaining a first background light intensity value of the target traffic image;

[0111] C5. Correct the first background light intensity value using the background light intensity correction value to obtain the target background light intensity value.

[0112] The associated image may be a first traffic image captured within a preset time interval with the target traffic image, for example, images captured k moments before and k moments after the target traffic image, where k may be 5, 10, etc. A general background light intensity value acquisition method may be used to acquire a reference background light intensity value corresponding to the associated image and a first background light intensity value of the target traffic image.

[0113] According to the reference background light intensity values ​​and the corresponding variances in the reference background light intensity value set, the method for determining the background light intensity correction value can be: calculating the mean of the reference background light intensity values, and normalizing the variance to obtain a normalized value, which is between 0 and 1. The background light intensity correction value is determined by using the mean and normalized value of the reference background light intensity values. Specifically, it can be: the ratio of the sum of the mean and normalized values ​​of the reference background light intensity values ​​to the mean of the reference background light intensity values ​​is determined as the background light intensity correction value. During the calculation, it is necessary to assign a value to the normalized value, which can be understood as follows: if the normalized value is less than the threshold, it is negative, and if it is greater than the threshold, it is positive. Since the larger the normalized value, the greater the degree of discreteness, it can be indicated that the change in the atmospheric light intensity value may be large.

[0114] Specifically, because the variance can characterize the changing relationship between the reference background light intensity values ​​in the reference background light intensity value set, and because the intervals between shots are very short, the variance is likely small, even approaching 0. Since the variance is small, a correction process can be performed in conjunction with the mean and variance to amplify this fluctuation and provide feedback to correct the first background light intensity value, thereby improving the accuracy of the target background light intensity value. The target background light intensity value can be determined as the product of the background light intensity correction value and the first background light intensity value.

[0115] In this example, an associated image set is obtained by acquiring associated images of a target traffic image, wherein the associated image is a first traffic image taken within a preset time interval with the target traffic image, and background light intensity values ​​of the associated images in the associated image set are acquired to obtain a reference background light intensity value set. A background light intensity correction value is determined based on the reference background light intensity values ​​and corresponding variances in the reference background light intensity value set, and a first background light intensity value of the target traffic image is acquired. The first background light intensity value is corrected using the background light intensity correction value to obtain the target background light intensity value. Therefore, the background light intensity correction value can be determined in combination with the background light intensity values ​​of the associated image set and the related variation. Finally, the first background light intensity value is corrected to obtain the target background light intensity value, thereby improving the accuracy of the target background light intensity value.

[0116] In one possible implementation, a method for determining traffic load information of a road area to be inspected based on the vehicle information and the traffic load information collection duration includes:

[0117] D1. Extracting sub-vehicle information of each lane in the road area to be detected from the vehicle information to obtain a sub-vehicle information set;

[0118] D2. Determine the traffic load information of the road area to be detected based on the sub-vehicle information set and the traffic load information collection time.

[0119] Vehicle information includes the lane to which the vehicle belongs, allowing for the extraction of sub-vehicle information for each lane. Finally, the ratio of the sub-vehicle information to the traffic load information collection duration is used to determine the traffic load information, representing the traffic volume per unit time. The traffic load information is then represented by the traffic volume. Sub-vehicle information can include, for example, the number of vehicles.

[0120] For the same example as above, please refer to Figure 2 , Figure 2 A schematic structural diagram of a terminal provided in an embodiment of the present application, as shown in the figure, includes a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, the processor being configured to call the program instructions, and the program including instructions for executing the following steps;

[0121] Acquire traffic images of the road area to be detected within the traffic load information collection period to obtain a first traffic image set;

[0122] performing defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set;

[0123] performing target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected;

[0124] The traffic load information of the road area to be detected is determined according to the vehicle information and the traffic load information collection time.

[0125] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to implement the above functions, the terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0126] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0127] In line with the above, please see Figure 3 , Figure 3 The present invention provides a schematic diagram of a traffic load prediction device based on image enhancement. Figure 3 As shown, the device includes:

[0128] An acquisition unit 301 is configured to acquire traffic images of a road area to be detected within a traffic load information collection period to obtain a first traffic image set;

[0129] an enhancement unit 302, configured to perform defogging and enhancement processing on the first traffic image in the first traffic image set to obtain a second traffic image set;

[0130] An extraction unit 303 is configured to perform target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected;

[0131] The determining unit 304 is configured to determine the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection time.

[0132] In one possible implementation, the enhancing unit 302 is specifically configured to:

[0133] determining a first transmittance correction value according to environmental information of the road area to be detected;

[0134] Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set;

[0135] Performing transmittance calculation based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance;

[0136] performing defogging processing on the target traffic image using the target transmittance to obtain a second traffic image;

[0137] Repeat the above steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, where the target traffic image is any first traffic image in the first traffic image set, and performing dehazing processing on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtaining the second traffic image set based on each second traffic image.

[0138] In a possible implementation, in determining the first transmittance correction value according to the environmental information of the road area to be detected, the enhancing unit 302 is specifically configured to:

[0139] extracting wind speed information, solid particulate matter information, and humidity information from the environmental information;

[0140] Acquire a joint influence degree between the humidity information and the solid particulate matter information;

[0141] determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information;

[0142] determining haze dispersion trend information based on the wind speed information;

[0143] determining a second reference transmittance correction value according to the haze dispersion trend information;

[0144] The first reference transmittance correction value and the second reference transmittance correction value are fused to obtain the first transmittance correction value.

[0145] In one possible implementation, in acquiring the background light intensity value corresponding to the target traffic image to obtain the target background light intensity value, the enhancing unit 302 is specifically configured to:

[0146] Acquire an associated image of the target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image;

[0147] Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set;

[0148] Determining a background light intensity correction value based on reference background light intensity values ​​and corresponding variances in the reference background light intensity value set;

[0149] Acquire a first background light intensity value of the target traffic image;

[0150] The first background light intensity value is corrected using the background light intensity correction value to obtain the target background light intensity value.

[0151] In one possible implementation, the determining unit 304 is specifically configured to:

[0152] Extracting sub-vehicle information of each lane in the road area to be detected from the vehicle information to obtain a sub-vehicle information set;

[0153] The traffic load information of the road area to be detected is determined according to the sub-vehicle information set and the traffic load information collection time.

[0154] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the image enhancement-based traffic load prediction methods described in the above method embodiments.

[0155] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any one of the image enhancement-based traffic load prediction methods described in the above method embodiments.

[0156] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0157] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0159] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0161] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disk, etc., various media that can store program code.

[0162] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0163] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A traffic load prediction method based on image enhancement, characterized in that: The method comprises: Acquire traffic images of the road area to be detected within the traffic load information collection period to obtain a first traffic image set; performing defogging and enhancement processing on a first traffic image in the first traffic image set to obtain a second traffic image set; performing target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected; Determining traffic load information of the road area to be detected based on the vehicle information and traffic load information collection time; The performing defogging and enhancement processing on the first traffic image in the first traffic image set to obtain a second traffic image set includes: determining a first transmittance correction value according to environmental information of the road area to be detected; Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set; Performing transmittance calculation based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance; performing defogging processing on the target traffic image using the target transmittance to obtain a second traffic image; Repeating the steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set, and performing defogging on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtaining the second traffic image set based on each second traffic image; The determining of the first transmittance correction value according to the environmental information of the road area to be detected includes: extracting wind speed information, solid particulate matter information, and humidity information from the environmental information; Obtaining a combined influence degree between the humidity information and the solid particulate matter information, where the combined influence degree between the humidity information and the solid particulate matter information is a degree of visual impact of solid particulate matter in air with different air humidity; determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information; determining haze dispersion trend information based on the wind speed information; determining a second reference transmittance correction value according to a correlation between the dispersion strength and transmittance in the haze dispersion trend information; Fusing the first reference transmittance correction value and the second reference transmittance correction value to obtain the first transmittance correction value; The step of obtaining the background light intensity value corresponding to the target traffic image to obtain the target background light intensity value includes: Acquire an associated image of the target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image; Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set; Determining a background light intensity correction value based on reference background light intensity values ​​and corresponding variances in the reference background light intensity value set; Acquire a first background light intensity value of the target traffic image; Correcting the first background light intensity value using the background light intensity correction value to obtain the target background light intensity value; The transmittance is calculated based on the target background light intensity value and the first transmittance correction value using the method shown in the following formula to obtain the target transmittance, specifically: ， in, is the target transmittance, is the first transmittance correction value, is the pixel value of the first sub-image block in the image block group, A is the target background light intensity value, c is the three color channels, is the minimum filter window, is the pixel point within the filter window, To obtain the minimum value, It is the value of the target background light intensity in three color channels.

2. The traffic load prediction method based on image enhancement according to claim 1, characterized in that: The determining of the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection duration includes: Extracting sub-vehicle information of each lane in the road area to be detected from the vehicle information to obtain a sub-vehicle information set; The traffic load information of the road area to be detected is determined according to the sub-vehicle information set and the traffic load information collection time.

3. A traffic load prediction device based on image enhancement, characterized in that: The device comprises: an acquisition unit, configured to acquire traffic images of the road area to be detected within a traffic load information collection period to obtain a first traffic image set; an enhancement unit, configured to perform defogging and enhancement processing on the first traffic image in the first traffic image set to obtain a second traffic image set; an extraction unit, configured to perform target extraction on the second traffic image in the second traffic image set to obtain vehicle information of the road area to be detected; a determining unit, configured to determine the traffic load information of the road area to be detected based on the vehicle information and the traffic load information collection time; The enhancement unit is specifically used for: determining a first transmittance correction value according to environmental information of the road area to be detected; Obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set; Performing transmittance calculation based on the target background light intensity value and the first transmittance correction value to obtain a target transmittance; performing defogging processing on the target traffic image using the target transmittance to obtain a second traffic image; Repeating the steps of obtaining a background light intensity value corresponding to a target traffic image to obtain a target background light intensity value, wherein the target traffic image is any first traffic image in the first traffic image set, and performing defogging on the target traffic image using the target transmittance to obtain a second traffic image, until a second traffic image corresponding to each first traffic image is obtained, and obtaining the second traffic image set based on each second traffic image; In determining the first transmittance correction value according to the environmental information of the road area to be detected, the enhancing unit is specifically configured to: extracting wind speed information, solid particulate matter information, and humidity information from the environmental information; Obtaining a combined influence degree between the humidity information and the solid particulate matter information, where the combined influence degree between the humidity information and the solid particulate matter information is a degree of visual impact of solid particulate matter in air with different air humidity; determining a first reference transmittance correction value according to the combined influence degree, the humidity information, and the solid particulate matter information; determining haze dispersion trend information based on the wind speed information; determining a second reference transmittance correction value according to a correlation between the dispersion strength and transmittance in the haze dispersion trend information; Fusing the first reference transmittance correction value and the second reference transmittance correction value to obtain the first transmittance correction value; In terms of acquiring the background light intensity value corresponding to the target traffic image to obtain the target background light intensity value, the enhancement unit is specifically configured to: Acquire an associated image of the target traffic image to obtain an associated image set, wherein the associated image is a first traffic image captured within a preset time interval with the target traffic image; Obtaining background light intensity values ​​of associated images in the associated image set to obtain a reference background light intensity value set; Determining a background light intensity correction value based on reference background light intensity values ​​and corresponding variances in the reference background light intensity value set; Acquire a first background light intensity value of the target traffic image; Correcting the first background light intensity value using the background light intensity correction value to obtain the target background light intensity value; The enhancement unit is specifically configured to calculate the transmittance according to the target background light intensity value and the first transmittance correction value using the method shown in the following formula to obtain the target transmittance: ， in, is the target transmittance, is the first transmittance correction value, is the pixel value of the first sub-image block in the image block group, A is the target background light intensity value, c is the three color channels, is the minimum filter window, is the pixel point within the filter window, To obtain the minimum value, It is the value of the target background light intensity in three color channels.

4. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the traffic load prediction method based on image enhancement according to any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the traffic load prediction method based on image enhancement according to any one of claims 1 to 2.

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