WO2023123869A1 - Procédé et appareil de mesure de valeur de visibilité, dispositif et support de stockage - Google Patents

Procédé et appareil de mesure de valeur de visibilité, dispositif et support de stockage Download PDF

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WO2023123869A1
WO2023123869A1 PCT/CN2022/097325 CN2022097325W WO2023123869A1 WO 2023123869 A1 WO2023123869 A1 WO 2023123869A1 CN 2022097325 W CN2022097325 W CN 2022097325W WO 2023123869 A1 WO2023123869 A1 WO 2023123869A1
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image
detected
visibility
characterization
visibility value
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PCT/CN2022/097325
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Chinese (zh)
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陈康
谭发兵
朱铖恺
武伟
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上海商汤智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present disclosure relates to the technical field of artificial intelligence, and in particular to a visibility value detection method, device, equipment and storage medium.
  • Fog, smog, dust and other climates will lead to low visibility in the environment and affect traffic travel.
  • the common foggy climate is affected by the micro-climate environment in local areas.
  • Fog with lower visibility appears in the local area of heavy fog. It is very harmful to road traffic safety, especially on high-speed expressways, and it is easy to cause major traffic accidents. Therefore, by identifying the visibility value of the area to be detected, it is possible to determine the influence of fog, sand and dust, etc., to guide traffic. Therefore, it is necessary to provide a scheme that can conveniently and accurately determine the visibility value of the area to be detected.
  • a visibility value detection method comprising: acquiring an image to be detected; performing feature extraction on the image to be detected to obtain a characterization quantity of the image to be detected; The mapping relationship between the calibrated characteristic quantity and the visibility value, and the characteristic quantity of the image to be detected determine the visibility value of the image to be detected; wherein, the characteristic quantity can reflect the magnitude of the visibility value of the scene contained in the image.
  • a visibility value detection device comprising: an acquisition module, used to acquire an image to be detected; a characterization module, used to analyze the image to be detected through a pre-trained neural network performing feature extraction to obtain the characterization quantity of the image to be detected; a visibility value determination module, configured to determine the corresponding The visibility value of , wherein the characterization quantity can reflect the magnitude of the visibility value of the scene contained in the image.
  • an electronic device the electronic device includes a processor, a memory, and computer instructions stored in the memory that can be executed by the processor, and the processor executes the computer Instructions, the method mentioned in the first aspect above can be implemented.
  • a computer-readable storage medium on which computer instructions are stored, and when the computer instructions are executed, the method mentioned in the above-mentioned first aspect is implemented.
  • a computer program product includes a computer program, and when the computer program is executed by a processor, the method mentioned in the above first aspect is implemented.
  • Fig. 1 is a flow chart of a method for detecting a visibility value according to an embodiment of the disclosure.
  • Fig. 2 is a schematic diagram of a detection method for determining a visibility value according to an embodiment of the present disclosure.
  • Fig. 3 is a schematic diagram of an application scenario according to an embodiment of the present disclosure.
  • Fig. 4 is a schematic diagram of a neural network structure according to an embodiment of the disclosure.
  • Fig. 5 is a schematic diagram of a logic structure of a road visibility detection device according to an embodiment of the disclosure.
  • Fig. 6 is a schematic diagram of a logic structure of an electronic device according to an embodiment of the present disclosure.
  • first, second, third, etc. may be used in the present disclosure to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of the present disclosure, first information may also be called second information, and similarly, second information may also be called first information. Depending on the context, the word “if” as used herein may be interpreted as “at” or “when” or “in response to a determination.”
  • the amount of haze or dust content in the area to be detected can be determined by identifying the visibility value of the area to be detected, so as to guide traffic travel.
  • some methods are to directly detect the visibility value of the area to be inspected through the visibility meter. This method requires the deployment of special inspection equipment in the area to be inspected, which is costly and difficult to implement.
  • the image of the area to be detected can be input into a pre-trained neural network to predict the visibility of the area to be detected.
  • a pre-trained neural network to predict the visibility of the area to be detected.
  • the visibility value range of the area for example, less than 50m, less than 100m, greater than 1000m, etc., so that when the neural network predicts the image of the area to be detected, it only predicts a rough visibility value range, and cannot obtain accurate visibility values, and Because the visibility value range of the sample image estimated by human experience will be affected by subjective factors, the prediction result is not very accurate.
  • an embodiment of the present disclosure provides a visibility value detection method, which can pre-calibrate the mapping relationship between the characterization quantity that can reflect the visibility value of the scene contained in the image and the visibility value.
  • the feature extraction of the image to be detected can be performed to obtain the representation of the image to be detected, and then based on the pre-calibrated mapping relationship between the representation and the visibility value, and the representation of the image to be detected, the visibility value of the scene contained in the image to be detected can be determined.
  • the visibility value of the image to be detected can also be determined without using a special visibility value tester.
  • the embodiment of the present disclosure can also obtain a finer-grained visibility value, which is convenient for subsequent applications.
  • the methods provided by the embodiments of the present disclosure can be executed by various electronic devices, such as cloud servers, user terminals, etc., and the methods can be used to predict visibility values in cloudy fog, haze, sand and other climates.
  • the method for determining the visibility value in the embodiment of the present disclosure may include steps S102 to S106.
  • the image to be detected can be obtained, the image to be detected can be an image of an area where the visibility value needs to be detected, collected by an image acquisition device, and the image to be detected can include scenes such as fog, sand and haze.
  • step S104 feature extraction may be performed on the image to be detected to obtain a characterization quantity representing the visibility value of the scene contained in the image to be detected.
  • a pre-trained neural network can be used to extract features from the image to be detected, or other methods can be used to extract features from the image to be detected, as long as it can be determined that it can reflect the visibility value of the scene contained in the image to be detected
  • the characterization quantity of size is enough.
  • the neural network can be trained through sample images, so that the trained neural network can accurately extract features related to the visibility value of the image, and output a representation quantity that can represent the visibility value.
  • the representation quantity can be a representation of the visibility value
  • Various information of size for example, can be vectors, matrices, specific values, etc.
  • the representation quantity may be a one-dimensional scalar quantity.
  • S106 Determine the visibility value of the image to be detected based on the pre-calibrated mapping relationship between the characterization quantity and the visibility value, and the characterization quantity of the image to be detected, wherein the characterization quantity can reflect the visibility value of the scene contained in the image the size of.
  • step S106 after obtaining the characterization quantity of the image to be detected, the characterization quantity of the image to be detected can be mapped into a visibility value based on the pre-calibrated mapping relationship between the characterization quantity and the visibility value, and the scene contained in the image to be detected can be obtained. visibility value.
  • the characterization quantity mentioned in the embodiments of the present disclosure can reflect the magnitude of the visibility value of the scene contained in the image.
  • feature extraction can be performed on some images with known visibility values to obtain the characterization quantities of these images, and the mapping relationship is constructed according to the correspondence between the characterization quantities of these images and the visibility values.
  • mapping relationship for the range of visibility values.
  • the accurate visibility value of the scene contained in the image can be predicted, and the visibility of the image can be represented in a more fine-grained manner, which is convenient for subsequent applications.
  • the feature extraction of the image to be detected may be performed through a pre-trained neural network to obtain the characterization of the image to be detected.
  • the mapping relationship between the representation quantity and the visibility value can be constructed based on the pre-trained neural network, and then the neural network is used to determine the representation quantity of the image to be detected, and then based on the representation quantity and the mapping relationship of the image to be detected to determine the Detects the visibility value of an image. Since it is difficult to calibrate the accurate visibility value of the sample image, it is difficult to obtain a large number of samples with accurate visibility value labels for training the neural network when training the neural network.
  • the trained neural network In order for the trained neural network to accurately extract features from the image and output a characterization quantity that can reflect the scene visibility value contained in the image, in some embodiments, when the neural network is trained, a pair of sample images carrying label information can be obtained , the label information is used to indicate the relative size relationship of the visibility values corresponding to the two frames of sample images in the sample image pair, and then the two frames of sample images in the sample image pair are input into the preset initial neural network, using the preset
  • the initial neural network is assumed to output the prediction result of the relative size relationship between the visibility values of the two frames of sample images in the sample image pair, and then based on the difference between the prediction result and the real result indicated by the label information, the network parameters of the initial neural network are continuously adjusted to Train the neural network.
  • sample image A and sample image B can be obtained, wherein the visibility value of sample image A is greater than the visibility value of sample image B, and then the initial neural network is used to output the probability that sample image A is an image with a larger visibility value, and then with The sample image A is compared with the real probability of the image with a large visibility value, and the deviation is determined. Based on the deviation, the initial neural network parameters are continuously adjusted until convergence, and a trained neural network is obtained. In this way, without obtaining sample images carrying visibility value labels, a neural network that can accurately extract representations reflecting the magnitude of the visibility value of an image can also be trained.
  • the preset initial neural network may include a feature extraction network, a representation quantity determination network, and an output layer.
  • the feature extraction network can be used to extract the features of the two frames of images in the sample image pair first, and then through the representation quantity
  • the determination network determines the respective representations of the two frames of images in the sample image pair, and then the visibility of the two frames of images can be determined through the output layer based on the respective representations of the two frames of images in the sample image pair The relative size relationship of the values.
  • the characterization quantity output by the neural network needs to reflect the visibility value of the scene contained in the image as truly as possible, the visibility value obtained based on the characterization quantity mapping will be accurate.
  • the applicant has optimized the structure of the neural network to improve its performance.
  • the characterization quantity determination network may include two network branches, that is, the first network branch and the second network branch, and the relative magnitudes of the visibility values of the two frame sample images in the output sample image pair according to the preset initial neural network
  • the representation of the second image and then determine the prediction result based on the representation of the first image and the representation of the second image.
  • the features of the extracted two frames of images are extracted by using two network branches, so as to obtain the first image and the second image respectively. Compared with only using one network branch to extract the features of the extracted two frames of images, the result obtained will be more accurate.
  • the characterization quantity determination network may also only include one network branch, and the features extracted by the feature extraction network of the two frames of images in the sample image pair can all be input into the network branch, through which the network branch respectively Determining the characterization quantities corresponding to the two frames of images.
  • one of the network branches may be randomly selected for the sample image input first, and another network branch is selected for the sample image input later.
  • the two network branches can also correspond to the visibility values of the sample images. For example, for the first network branch, it can be specially used for feature extraction of sample images with larger visibility values, and for the second network branch , which can be specially used for feature extraction of sample images with small visibility values, etc.
  • At least two first image sets can be acquired, wherein the visibility value range of each image in each first image set of the at least two first image sets is the same, and any two images in the first image set The visibility value ranges for do not overlap. Then one frame of image is respectively acquired from any two first image sets to form a sample image pair. Since the visibility ranges of the images in any two sample image sets are different, a large number of sample image pairs with different visibility values can be obtained by randomly combining each image in any two first image sets. Train the neural network.
  • the characterization quantity representing the visibility value of the scene included in the image may be a one-dimensional scalar, such as a specific value, so as to facilitate the establishment of a mapping relationship between the characterization quantity and the visibility value.
  • the specific visibility values of some images can also be determined first through a visibility meter or other means, and then based on the characterization quantities of these images and the visibility values of these images Build a mapping relationship.
  • a third image set may be obtained, wherein the third image set may include multiple frames of images with known visibility values, and then feature extraction may be performed on the multiple frames of images in the third image set to determine the representation of the multiple frames of images,
  • a mapping relationship may be constructed based on the representation quantities of the multiple frames of images and the visibility values of the multiple frames of images.
  • the visibility values of the multiple frames of images in the third image set can be evenly distributed within a specified visibility range , where the specified visibility range can be determined based on the approximate distribution range of the visibility values of the image to be tested. For example, assuming that the visibility values of the image to be detected are generally distributed within 1000m, it is necessary to accurately calibrate the visibility values and representations within the visibility value range Therefore, the specified visibility range is 0-1000m. In order to make the calibrated mapping relationship as accurate as possible, the visibility values of the multi-frame images in the third image set can be distributed as evenly as possible within this range to cover Individual visibility gradients within the range.
  • each second image set when establishing the mapping relationship between the characterization quantity and the visibility value, at least one second image set can be obtained, and each second image set includes multiple frames of images , the visibility value range of the images in each second image set is the same, for example, both are 0-50m, or all are 50-100m.
  • each second image set feature extraction can be performed on each frame of the image in the second image set to obtain the representation of each frame of the image in the second image set, for example, each frame of image can be input into the trained neural network, Utilize the neural network to output the characterization quantity of the image, so that the distribution range of the characterization quantity of each image in the second image set can be obtained, and then the characterization quantity can be determined according to the visibility value range and the characterization quantity distribution range of each image in each second image set The mapping relationship with the visibility value.
  • each second image set may include a large number of sample images, and the more images there are, the more the visibility values of the images can cover each visibility value within the visibility value range, and the more accurate the established mapping relationship is.
  • the visibility values of these images are in the range of 0-50m. Since there are a large number of images, these images can basically cover each visibility value between 0-50m. Then the representations of these images can be output through the neural network, wherein the representations and visibility values can be positively or negatively correlated, for example, the greater the visibility, the greater the representation.
  • the mapping relationship may be determined according to the distribution range of the characteristic quantities and the visibility value range of these images. For example, the distribution range of the token is A-B, assuming that the token is positively correlated with the visibility value, then the visibility value corresponding to token A is 0, and the visibility value corresponding to token B is 50, and then the mapping relationship can be established.
  • the above is just a simple example, and it may be more complicated when actually constructing the mapping relationship.
  • the visibility value ranges of the images in two adjacent second image sets are continuous.
  • the ranges of visibility values corresponding to these six second image sets are (0m,50m], (50m,100m], (100m,200m], (200m,500m], (500m, 1000m], (1000m, ⁇ )
  • the ranges of visibility values corresponding to two adjacent second image sets are continuous and separated by target visibility values.
  • the visibility ranges are (0m, 50m] and ( 50m, 100m], the two second image sets are separated by the target visibility value 50m.
  • the mapping relationship is determined based on the visibility value range and characterization quantity distribution range corresponding to each second image set
  • the mapping relationship determines the representation quantity corresponding to the target visibility value, and then Determine the mapping relationship based on the characterization of the target visibility value. Since the target visibility value is the boundary between two adjacent visibility value ranges, when determining the mapping relationship, it is mainly determined based on the boundary of the visibility value range and the boundary of the distribution range of the characterization quantity , so accurately determining the characterization of the boundary is the key to accurately determining the mapping relationship.
  • the target visibility value 50m is the boundary of the two visibility value ranges.
  • the characterization quantity corresponding to the target visibility value of 50m can be determined. For example, taking the positive correlation between the characterization quantity and the visibility value as an example, the characterization quantity corresponding to the target visibility value of 50m is the largest characterization quantity in the previous second image set, and the next second image set.
  • the minimum characterization quantity of the target visibility value 50m determined based on the two second image sets may be inconsistent, so the final target visibility value can be obtained based on the characterization quantities of the target visibility value 50m respectively determined by the two second image sets the amount of representation.
  • the characterization quantity of the target visibility value when determining the characterization quantity of the target visibility value based on the visibility value range and the distribution range of the characterization quantity corresponding to two adjacent second image sets, it may be based on the visibility value range of the two adjacent second image sets and the distribution range of the characterization quantity, determine the initial characterization quantity corresponding to the target visibility value, and then continuously adjust the initial characterization quantity to obtain the adjusted characterization quantity, based on the adjusted characterization quantity, the images in the two adjacent second image sets are Classify until the accuracy of the classification results of the two adjacent second image sets reaches the maximum value, and then use the adjusted characterization as the characterization corresponding to the target visibility value.
  • the output characterization value is between 0-20.
  • the output of the characterization quantity is between 18-40, therefore, based on the latter second image set, it can be preliminarily determined that the characterization quantity corresponding to 50m is 18, and then, one of the characterization quantities (18 or 20) or the average value of the two can be taken as 19
  • the initial characterization quantity of the target visibility value of 50m and then adjust the initial characterization quantity within a certain range (for example, increase or decrease by a certain step size)
  • the initial characterization quantity is adjusted, based on the adjusted characterization quantity, the All the images in these two image sets are classified to obtain classification results bounded by the adjusted characterization amount, and determine the accuracy of each classification result.
  • the adjusted characterization amount is 19, that is, 50m corresponds to If the representation value is 19, then according to the classification according to the representation value, the image whose representation value is in the range of 19-20 in the previous sample image set will be misclassified, so that a classification accuracy can be calculated. Similarly, the representation value in the latter sample image set is in the range of 18 The image of -19 will also be misclassified, so a classification accuracy can also be calculated.
  • the characterization quantity when the classification accuracy of the two sample image sets is the highest can be taken as the characterization quantity corresponding to the target visibility value of 50m. For other target visibility Values (for example, 100m, 200m, 500, 1000m in the above six second image sets), a similar method can also be used.
  • the pre-trained neural network may include a feature extraction network, a first network branch, and a second network branch.
  • feature extraction can be performed on the image to be detected through the feature extraction network, and the extracted features can be input to any one of the first network branch or the second network branch to obtain the characterization quantity of the image to be detected, that is, when extracting the feature of the image to be detected
  • one of the network branches can be used to output the characterization of the image to be detected.
  • the final representations may also be obtained by combining the representations of the images to be detected output by the two network branches.
  • the feature extraction network of the neural network can be used to perform feature extraction on the image to be detected, and the extracted features can be input to the first network branch to obtain the first representation, and the extracted features can be input to the second network branch.
  • a second characterization quantity is obtained, and a characterization quantity of the image to be detected is obtained based on the first characterization quantity and the second characterization quantity.
  • the representation quantity of the image to be detected can be obtained by weighting the first representation quantity and the second representation quantity, wherein the weights can be the same, or can be set differently according to actual requirements.
  • the image to be detected can be an image of a road area, and the image to be detected can be collected by an image acquisition device installed on the road. After determining the visibility value of the scene contained in the image to be detected, it can also be based on the visibility value.
  • Determine the hazard level of the current specific climate for example, foggy weather, sandy weather, haze climate, etc.
  • select a control strategy corresponding to the hazard level to control the traffic on the road For example, multiple levels for evaluating the degree of fog weather hazard can be preset. Different levels correspond to different visibility value ranges. Each hazard level can be preset with corresponding control strategies to control traffic on the road.
  • the management and control strategy may include early warning of foggy weather for vehicles on the road, the management and control strategy may also include prompting the speed of vehicles on the road to be controlled below a certain speed, and the management and control strategy may also include closing road intersections and prohibiting vehicles from passing.
  • the image to be detected may be an image of a road area
  • the image to be detected may be a multi-frame image collected at different times by an image acquisition device set in the road.
  • a frame of image of the road area can be obtained every 30 minutes, and the corresponding visibility value of the image can be determined to obtain the change trend of the road visibility value within a period of time, so that it can be predicted that the fog concentration in the road is gradually increasing , or decrease, so as to predict the change trend of foggy climate.
  • the image to be detected may be an image of a road area
  • the image to be detected may be an image collected by an image acquisition device installed on the road at preset time intervals, for example, one or more frames of images are collected every hour , and then determine the visibility value corresponding to this frame or multiple frames of images through the neural network to determine whether a specific climate (for example, cloudy weather, sandy weather, haze weather, etc.) phenomenon is currently occurring.
  • a specific climate for example, cloudy weather, sandy weather, haze weather, etc.
  • the visibility value corresponding to the image exceeds a preset threshold, if the visibility values corresponding to each frame of image exceed the preset threshold, or the corresponding visibility values of a certain number of images exceed the preset threshold, Then it is determined that a specific weather phenomenon is currently occurring in the road area.
  • the average value of the visibility values of the frame or multiple frames of images may be determined, and if the average value exceeds a preset threshold, it is determined that a specific weather phenomenon is currently occurring in the road area.
  • one or more frames of road area images can be collected every 2 hours, and the visibility value corresponding to this frame or multiple frames of road area images can be predicted by the neural network. If the visibility values of these images exceed the preset threshold, then It is determined that clusters of fog have occurred in the road area, and the frequency of daily clusters of fog can be determined by counting the total number of clusters of fog in a day.
  • the images of the road area are collected by cameras set on the road, and the visibility of the road area contained in the image is predicted based on the neural network.
  • the neural network is only trained by sample images carrying visibility value range labels, and the output of the neural network is only the classification result of the visibility value range of the image, and the exact specific value of visibility cannot be obtained.
  • This embodiment provides a method that can detect specific values of visibility in an image
  • Figure 3 is a schematic diagram of an application scenario of the method, the method can be used to detect specific values of visibility in a road area, and then carry out road traffic monitoring Control.
  • the method includes a neural network training stage, a calibration of the mapping relationship between a one-dimensional scalar output by the neural network and a concrete numerical value of visibility, and a neural network reasoning stage.
  • a large number of sample images can be collected. For each sample image, there is no need to calibrate its specific visibility value. It is only necessary to determine the category of the visibility value range to which it belongs. For example, the sample images can be divided into 6 categories, and the corresponding 6 visibility value ranges are : (0m,50m], (50m,100m], (100m,200m], (200m,500m], (500m,1000m], (1000m, ⁇ ). Since only the visibility value range of the image needs to be calibrated, it is easy to implement, Thus, a large number of sample images can be acquired.
  • sample images in any two categories of the above six types of sample image sets can be combined in pairs to obtain a large number of sample image pairs, wherein the relative magnitude relationship of the visibility value of each sample image pair is known.
  • the sample image pair can be used as a training sample, and the relative size relationship of the visibility value of the sample image pair can be used as a label to train the initial neural network to obtain a trained neural network.
  • the structure of the neural network is shown in FIG. 4 , including a feature extraction network, a first network branch, a second network branch, and an output layer.
  • the feature extraction network for example, can choose the resnet18 network, which includes 5 convolutional layers (such as conv1-conv5 in Figure 4), and accesses 2 network branches after conv5, namely the first network branch and the second network branch , each network branch contains 3 layers of fully connected layers.
  • the output dimension of the fully connected layer fc1 is 256, and the activation function is selected as prelu; the output dimension of the fully connected layer fc2 is 128, and the activation function is selected as prelu; the output dimension of the fully connected layer fc3 is 1, and the activation function is selected as liner.
  • sample image A and sample image B When a sample image pair (sample image A and sample image B) is input into the neural network, the sample image A can extract features through the feature extraction network, and then further input the extracted features to the first network branch for feature extraction, and finally A one-dimensional scalar RA is output, which can reflect the magnitude of the visibility value of the scene included in the sample image A.
  • the sample image B can extract features through the feature extraction network, and then further input the extracted features to the second network branch for feature extraction, and finally output a one-dimensional scalar RB, which can reflect the visibility value of the scene included in the sample image B.
  • the one-dimensional scalar output by the neural network may be positively correlated with the specific value of the visibility, and the larger the one-dimensional scalar, the greater the specific value of the visibility.
  • the one-dimensional scalars RA and RB can be input into the output layer, and the output layer can determine the probability p that the sample image A is an image with a higher specific value of visibility based on the one-dimensional scalars RA and RB, and that the sample image B is an image with a higher specific value of visibility.
  • the loss function can use cross-entropy loss.
  • the real probability that the sample image A is an image with a larger specific value of the visibility is y (y is 1 or is preset A value close to 1, such as 0.8), the real probability that the sample image B is an image with a larger specific value of visibility is 1-y.
  • the parameters of the neural network can be continuously adjusted based on the cross-entropy loss until the loss function converges to obtain a trained neural network.
  • Each frame image in the above six classifications can be input into the neural network, and the neural network is used to output a one-dimensional scalar representing the specific value of the visibility corresponding to each frame image. Due to the large number of sample images, it can be considered that the output one-dimensional scalar is basically It can reflect various specific values of visibility. For example, there are 10,000 sample images in the category (0,50], which has a high probability of reflecting each visibility value between 0m and 50m, and then based on the labeling in each category The image visibility value range and the one-dimensional scalar range construct the mapping relationship.
  • the one-dimensional scalar corresponding to the boundary value of each visibility value range for example, 0m, 50m, 100m, 200m, 500m , 1000m.
  • the one-dimensional scalar is positively correlated with the specific value of visibility, the minimum value of the one-dimensional scalar in the image whose visibility value range is (0,50] corresponds to the boundary value 0m.
  • the maximum value of the one-dimensional scalar in the image set whose visibility value range is (0,50] corresponds to 50m
  • the minimum value of the one-dimensional scalar value in the image set whose visibility value range is (50,100] is also Corresponding to 50m
  • the one-dimensional scalar corresponding to 50m determined based on the two classified image sets may not be equal.
  • the one-dimensional scalar corresponding to 50m in the image set whose visibility value range is in (0,50] is 10
  • the visibility value range is in ( 50,100] in the image set
  • the one-dimensional scalar corresponding to 50m is 15.
  • the one-dimensional scalar corresponding to the boundary value 50m can be continuously adjusted (for example, take the one-dimensional scalar between 10-15 value), and then determine the accuracy of classifying the images in these two classifications based on the adjusted one-dimensional scalar. For example, when the one-dimensional scalar corresponding to the boundary value 50m is 12, the visibility value range is located at ( 0,50], the accuracy is only 80%.
  • the accuracy based on the one-dimensional scalar 12 is 80%; similarly, based on This one-dimensional scalar classifies all images whose visibility values range from (50,100], the accuracy is only 90%, and then continuously adjusts the one-dimensional scalar value corresponding to the boundary value 50m until the classification accuracy of the two classified images When the maximum value is reached, the one-dimensional scalar is determined as the one-dimensional scalar corresponding to the visibility value 50m.
  • mapping relationship can be constructed based on the one-dimensional scalar.
  • the specific value of the visibility of multiple frames of images it is also possible to first determine the specific value of the visibility of multiple frames of images through the visibility meter, and then determine the one-dimensional scalar of the multiple frames of images through the pre-trained neural network, based on the one-dimensional scalar sum of the multiple frames of images
  • the specific numerical value of the visibility constructs the mapping relationship.
  • the specific values of the visibility of the multiple frames of images should cover the gradients of each visibility value as much as possible.
  • the image to be detected When you want to detect the specific value of the visibility of the image to be detected, you can obtain the image to be detected, input the image to be detected into the neural network, and output the one-dimensional scalar of the image to be detected through the neural network (it can be determined by using a network branch, or can be selected The average of the output results of the two network branches). Then, the specific value of the visibility of the image to be detected is determined according to the one-dimensional scalar of the image to be detected and the pre-calibrated mapping relationship. After determining the specific value of the visibility of the image to be detected, the traffic on the road can be controlled based on the specific value of the visibility.
  • the neural network can be trained without calibrating the specific value of the visibility of the sample image, and the specific value of the visibility of the image to be detected can be determined based on the trained neural network. Compared with determining the range of visibility values, more accurate visibility results can be obtained.
  • the embodiment of the present disclosure also provides a visibility value detection device.
  • the device 50 includes: an acquisition module 51 for acquiring an image to be detected;
  • the pre-trained neural network performs feature extraction on the image to be detected to obtain the characterization quantity of the image to be detected;
  • the visibility value determination module 53 is used to determine the mapping relationship based on the pre-calibrated characterization quantity and visibility value, and the to-be-detected image.
  • the characterization quantity of the detected image determines the visibility value corresponding to the image to be detected, wherein the characterization quantity can reflect the magnitude of the visibility value of the scene contained in the image.
  • an embodiment of the present disclosure is also an electronic device.
  • the electronic device includes a processor 61, a memory 62, and computer instructions stored in the memory 62 for execution by the processor 61.
  • the processor executes the computer instructions, the method described in any of the foregoing embodiments can be implemented.
  • An embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in any one of the foregoing embodiments is implemented.
  • Computer-readable media including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information.
  • Information may be computer readable instructions, data structures, modules of a program, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridge, tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
  • computer-readable media excludes transitory computer-readable media, such as modulated data signals and carrier waves.
  • a typical implementing device is a computer, which may take the form of a personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media player, navigation device, e-mail device, game control device, etc. desktops, tablets, wearables, or any combination of these.
  • each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments.
  • the description is relatively simple, and for relevant parts, please refer to part of the description of the method embodiment.
  • the device embodiments described above are only illustrative, and the modules described as separate components may or may not be physically separated, and the functions of each module may be integrated in the same or multiple software and/or hardware implementations. Part or all of the modules can also be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without creative effort.

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Abstract

L'invention concerne un procédé et un appareil de mesure de valeur de visibilité, un dispositif et un support de stockage. Le procédé consiste à : acquérir une image à mesurer ; mettre en oeuvre une extraction de caractéristiques sur ladite image afin d'obtenir une valeur de représentation de ladite image ; et déterminer une valeur de visibilité correspondant à ladite image sur la base d'une relation de mappage pré-étalonnée entre la valeur de représentation et la valeur de visibilité, et de la valeur de représentation de ladite image, la valeur de représentation pouvant refléter la taille de la valeur de visibilité d'une scène contenue dans l'image.
PCT/CN2022/097325 2021-12-30 2022-06-07 Procédé et appareil de mesure de valeur de visibilité, dispositif et support de stockage WO2023123869A1 (fr)

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CN114359211A (zh) * 2021-12-30 2022-04-15 上海商汤智能科技有限公司 能见度值检测方法、装置、设备及存储介质
CN115116252A (zh) * 2022-08-30 2022-09-27 四川九通智路科技有限公司 一种对车辆安全引导的方法、装置及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110910378A (zh) * 2019-11-29 2020-03-24 南通大学 一种基于深度融合网络的双模态图像能见度检测方法
CN111145120A (zh) * 2019-12-26 2020-05-12 上海眼控科技股份有限公司 能见度检测方法、装置、计算机设备和存储介质
CN111191629A (zh) * 2020-01-07 2020-05-22 中国人民解放军国防科技大学 一种基于多目标的图像能见度检测方法
CN113658275A (zh) * 2021-08-23 2021-11-16 深圳市商汤科技有限公司 能见度值的检测方法、装置、设备及存储介质
CN114359211A (zh) * 2021-12-30 2022-04-15 上海商汤智能科技有限公司 能见度值检测方法、装置、设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110910378A (zh) * 2019-11-29 2020-03-24 南通大学 一种基于深度融合网络的双模态图像能见度检测方法
CN111145120A (zh) * 2019-12-26 2020-05-12 上海眼控科技股份有限公司 能见度检测方法、装置、计算机设备和存储介质
CN111191629A (zh) * 2020-01-07 2020-05-22 中国人民解放军国防科技大学 一种基于多目标的图像能见度检测方法
CN113658275A (zh) * 2021-08-23 2021-11-16 深圳市商汤科技有限公司 能见度值的检测方法、装置、设备及存储介质
CN114359211A (zh) * 2021-12-30 2022-04-15 上海商汤智能科技有限公司 能见度值检测方法、装置、设备及存储介质

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