WO2022012149A1

WO2022012149A1 - Agglomerate fog recognition method and apparatus, electronic device, storage medium, and computer program product

Info

Publication number: WO2022012149A1
Application number: PCT/CN2021/094385
Authority: WO
Inventors: 赵永磊; 朱铖恺; 洪依君; 李军; 武伟
Original assignee: 上海商汤智能科技有限公司
Priority date: 2020-07-17
Filing date: 2021-05-18
Publication date: 2022-01-20
Also published as: KR20220041892A; CN111783732A; JP2022545962A

Abstract

An agglomerate fog recognition method and apparatus, an electronic device, a storage medium, and a computer program product. The method comprises: acquiring a scene image of a target scene (S101); performing feature extraction on the scene image to obtain global feature information (S102); and performing classification processing on the global feature information, and determining an agglomerate fog in the scene image and an agglomerate fog level corresponding to the agglomerate fog (S103).

Description

Mist identification method, device, electronic device, storage medium and computer program product

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on the Chinese patent application with the application number of 202010699922.1, the application date is July 17, 2020, and the application name is "Mist Recognition Method, Device, Electronic Device and Storage Medium", and claims the priority of the Chinese patent application , the entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present disclosure relates to the technical field of computer vision, and in particular, to a method, device, electronic device, storage medium, and computer program product for fog identification.

Background technique

"Mass fog", also known as Tuo Tuo fog, is affected by the local microclimate environment. In the local area of tens of meters to hundreds of meters in heavy fog, fog with lower visibility appears. Fog has the characteristics of strong regionality and difficulty in forecasting and forecasting. On expressways, fog can lead to sudden changes in visibility, which is extremely harmful to expressway traffic safety and can easily lead to major traffic accidents.

At present, special hardware such as photoelectric sensors or cameras can be used to identify fog clusters. Among them, compared with the high cost and difficult to large-scale promotion of the solution using special hardware, it is more feasible to use the camera to obtain image information to identify the fog.

However, the scheme of using the camera to realize the fog recognition scheme mostly adopts methods such as dark channel prior or manual selection of features to obtain the shallow features of the image from the image collected by the camera, which is easily affected by factors such as the angle of the light camera, resulting in a poor recognition effect. Difference.

SUMMARY OF THE INVENTION

Embodiments of the present disclosure are expected to provide a method, apparatus, electronic device, storage medium, and computer program product for fog identification.

The technical solutions of the embodiments of the present disclosure are implemented as follows:

An embodiment of the present disclosure provides a method for identifying a fog mass, and the method includes:

Get the scene image of the target scene;

Perform feature extraction on the scene image to obtain global feature information;

The global feature information is classified and processed to determine the fog in the scene image and the fog level corresponding to the fog.

In the above method for identifying fog clusters, before the feature extraction is performed on the scene image to obtain global feature information, the method further includes:

Perform image preprocessing on the scene image to obtain a processed image;

Correspondingly, performing feature extraction on the scene image to obtain global feature information, including:

Feature extraction is performed on the processed image to obtain the global feature information.

In the above method for identifying fog clusters, the image preprocessing is performed on the scene image to obtain a processed image, including:

performing pixel sampling on the scene image to obtain a sampled image of the target size;

Normalize the sampled image to obtain the processed image.

In the above method for identifying the fog, the classification processing of the global feature information to determine the fog in the scene image and the fog level corresponding to the fog includes:

performing average pooling on the global feature information to obtain global average feature information;

The global average feature information is classified to determine the fog and the fog level.

In the above method for identifying fog, after determining the fog in the scene image and the fog level corresponding to the fog, the method further includes:

obtaining a first visibility range corresponding to the fog mass level according to the corresponding relationship between the preset mass fog level and the visibility range;

In the case that the first visibility range is lower than the preset visibility range, acquiring first warning information corresponding to the fog mass level;

The first warning information is output.

In the above method for identifying fog, the scene image includes multiple images at multiple times, the level of fog includes multiple levels corresponding to the multiple images, and the determined image in the scene image includes multiple levels. After the fog and the fog level corresponding to the fog, the method further includes:

In the case that the multiple levels are positively correlated with the multiple times, acquiring second early warning information;

Obtain the early warning reminder device in the target scene;

The second early warning information is transmitted to the early warning reminder device.

Perform statistics on the multiple levels to obtain statistical results;

According to the statistical result, the occurrence frequency of the fog mass in the target scene is determined.

An embodiment of the present disclosure provides a device for identifying a cloud of fog, and the device for identifying a cloud of cloud includes:

an image acquisition module, configured to acquire a scene image of the target scene;

a feature extraction module, configured to perform feature extraction on the scene image to obtain global feature information;

The classification processing module is configured to perform classification processing on the global feature information, and determine the fog in the scene image and the fog level corresponding to the fog.

In the above-mentioned fog identification device, it also includes the image processing module,

The image processing module is configured to perform image preprocessing on the scene image to obtain a processed image;

Correspondingly, the feature extraction module is specifically configured to perform feature extraction on the processed image to obtain the global feature information.

In the above device for identifying fog, the image processing module is specifically configured to perform pixel sampling on the scene image to obtain a sampled image of a target size; perform normalization processing on the sampled image to obtain the processed image .

In the above-mentioned device for identifying the fog mass, the classification processing module is specifically configured to perform average pooling on the global feature information to obtain global average feature information; classify the global average feature information to determine the fog mass and the fog level.

In the above-mentioned fog identification device, it also includes an information output module,

The information output module is configured to obtain a first visibility range corresponding to the fog level according to the corresponding relationship between the preset fog level and the visibility range; when the first visibility range is lower than the preset visibility range , obtain the first warning information corresponding to the mass fog level; and output the first warning information.

In the above-mentioned fog identification device, it further includes an information output module, the scene image includes multiple images at multiple times, and the fog level includes multiple levels corresponding to the multiple images,

The information output module is configured to obtain second early warning information under the condition that the multiple levels are positively correlated with the multiple times; obtain the early warning reminder device in the target scene; transmitted to the pre-warning reminder device.

In the above-mentioned device for identifying fog, further comprising a statistical processing module, the scene image includes multiple images at multiple times, and the fog level includes multiple levels corresponding to the multiple images,

The statistical processing module is configured to perform statistics on the multiple levels to obtain a statistical result; and determine the occurrence frequency of fog in the target scene according to the statistical result.

An embodiment of the present disclosure provides an electronic device, the electronic device includes: a processor, a memory, and a communication bus; wherein,

the communication bus, configured to implement connection communication between the processor and the memory;

The processor is configured to execute the fog identification program stored in the memory, so as to implement the above method for fog identification.

An embodiment of the present disclosure provides a computer-readable storage medium, where one or more programs are stored in the computer-readable storage medium, and the one or more programs can be executed by one or more processors, so as to realize the above group Fog identification method.

An embodiment of the present disclosure provides a computer program product, the computer program product includes a computer program or an instruction, and when the computer program or instruction runs on a computer, the computer is made to execute the above method for fog identification.

Embodiments of the present disclosure provide a method, device, electronic device, storage medium, and computer program product for fog identification. The method includes: acquiring a scene image of a target scene, performing feature extraction on the scene image, and obtaining global feature information; The information is classified and processed to determine the fog in the scene image and the fog level corresponding to the fog. The technical solution provided by the embodiments of the present disclosure extracts global deep-level features of the image, improves the effectiveness of the fog information representation, reduces the interference of information irrelevant to the fog recognition in the image, and improves the performance of the fog recognition. accuracy.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the background technology, the accompanying drawings required in the embodiments or the background technology of the present disclosure will be described below.

The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the technical solutions of the present disclosure.

1 is a schematic flowchart of a method for identifying a fog mass according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of an exemplary image processing process provided by an embodiment of the present disclosure;

3 is a schematic structural diagram 1 of a device for identifying a fog mass according to an embodiment of the present disclosure;

FIG. 4 is a second schematic structural diagram of a device for identifying a fog mass according to an embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

detailed description

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure.

The embodiments of the present disclosure provide a fog identification method, the execution body of which may be a fog identification device. For example, the fog identification method may be executed by a terminal device or a server or other electronic device, where the terminal device may be a user equipment ( User Equipment, UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, Personal Digital Assistant (PDA), handheld devices, computing devices, in-vehicle devices, wearable devices, etc. In some possible implementations, the method for identifying the fog cloud may be implemented by the processor calling computer-readable instructions stored in the memory.

Embodiments of the present disclosure provide a method for identifying a fog mass. FIG. 1 is a schematic flowchart of a method for identifying a fog mass according to an embodiment of the present disclosure. As shown in Figure 1, the fog identification method mainly includes the following steps:

S101. Acquire a scene image of a target scene.

In the embodiment of the present disclosure, the apparatus for identifying the fog mass may acquire the scene image of the target scene.

It should be noted that, in the embodiment of the present disclosure, the scene image is the scene that needs to be identified by the fog, that is, the image corresponding to the target scene. Among them, when the fog recognition method is applied to the scene of expressway monitoring, the scene image is actually the expressway monitoring image, and the subsequent fog fog recognition is not limited by special hardware, which is convenient for large-scale application. The specific target scene and the scene image of the target scene are not limited in the embodiments of the present disclosure.

It should be noted that, in the embodiments of the present disclosure, the scene image may be collected by the fog identification device, or may be obtained by an independent camera, server, or cloud device, and transmitted to the fog identification device. The specific source of the scene image is not limited in this embodiment of the present disclosure.

S102. Perform feature extraction on the scene image to obtain global feature information.

In the embodiment of the present disclosure, after obtaining the scene image, the apparatus for fog identification can perform feature extraction on the scene image, thereby obtaining global feature information in the scene image.

It should be noted that, in the embodiment of the present disclosure, before performing feature extraction on the scene image to obtain the global feature information, the device for fog identification may further perform the following steps: performing image preprocessing on the scene image to obtain a processed image; Correspondingly, the fog identification device performs feature extraction on the scene image to obtain global feature information, including: performing feature extraction on the processed image to obtain global feature information.

Specifically, in the embodiment of the present disclosure, the fog identification device performs image preprocessing on a scene image to obtain a processed image, including: performing pixel sampling on the scene image to obtain a sampled image of a target size; normalizing the sampled image processing to obtain a processed image.

It should be noted that, in the embodiments of the present disclosure, the fog identification device performs pixel sampling on the scene image, which essentially reshapes the size of the scene image to a specific size, that is, the target size, so as to determine the obtained image as Sample image. Wherein, the fog identification device may use a bilinear interpolation algorithm to sample the pixels of the scene image. Of course, other sampling algorithms may also be used to sample pixels to obtain a sampled image. The specific target size and the pixel sampling method can be selected according to actual requirements, which are not limited in the embodiments of the present disclosure.

It should be noted that, in the embodiments of the present disclosure, the fog identification device performs normalization processing on the sampled image, which essentially converts the pixel value of each pixel in the sampled image into a value between 0 and 1. Thus, the pixel values of different pixel points are mapped to the same fixed range.

It can be understood that, in the embodiment of the present disclosure, the fog identification device performs pixel sampling and normalization processing on the scene image, and the obtained processed image can meet certain standards in terms of size and pixel representation. , so as to facilitate subsequent feature extraction and improve the efficiency of feature extraction.

It should be noted that, in the embodiment of the present disclosure, a preset deep neural network is stored in the fog identification device. The preset deep neural network can realize the extraction of global deep-level features in the image, and the specific preset deep neural network can be obtained by using a large number of sample images for feature extraction training, which is not limited in the embodiment of the present application.

It should be noted that, in the embodiment of the present disclosure, the preset deep neural network may include: multiple groups of convolutional layers connected in sequence from shallow to deep according to the depth of feature extraction, and the device for fog identification may use multiple groups of convolutional layers to The processed image is subjected to multiple iterative convolution processing to obtain feature images, and then the feature images are determined as global feature information.

FIG. 2 is a schematic diagram of an exemplary image processing process provided by an embodiment of the present disclosure. As shown in Figure 2, the preset deep neural network includes 5 groups of convolutional layers, namely the first group of convolutional layers, the second group of convolutional layers, the third group of convolutional layers, the fourth group of convolutional layers and the fifth group of convolutional layers. Group convolutional layers. Among them, the first group of convolution layers uses 7×7 filters, which can convolve the input 2-channel processed image to obtain a 64-channel image, and the image size is downsampled to 1/2; Five groups of convolution layers, each group contains 1-3 layers of convolution layers, the size of the convolution kernel is 3 × 3, and the number of convolution output channels is 64, 128, 256 and 512 in turn. The result of the product, that is, the output image can become 1/4, 1/8, 1/16 and 1/32 of the processed image. After the processed image is processed by 5 groups of convolutional layers, the image size is gradually reduced. The deeper, the more advanced the feature level.

It should be noted that, in the embodiments of the present disclosure, each group of convolutional layers of the multiple groups of convolutional layers may perform max-pooling on the images obtained by convolution processing before outputting the images to the next connected convolutional layer. In other words, downsampling, specifically, grouping every four pixels in the image into a group, and retaining only the pixel with the largest value, so that the length and width of the image obtained by downsampling are actually 1/2 of the input image.

It should be noted that, in the embodiments of the present disclosure, the number of groups of convolutional layers included in the preset deep neural network, and the related parameters in each group of convolutional layers, for example, the size of the convolution kernel, can be determined according to actual conditions. Requirement settings are not limited in the embodiments of the present disclosure.

It can be understood that, in the embodiments of the present disclosure, the global feature extraction of the processed image can be realized by using the preset deep neural network, which can more effectively characterize the fog in the image compared with the traditional solution of extracting the local features of the image. information, thereby improving the accuracy of fog identification.

S103 , classifying the global feature information, and determining the fog in the scene image and the fog level corresponding to the fog.

In the embodiment of the present disclosure, after the global feature information of the scene image, the device for identifying the fog may perform classification processing on the global feature information to determine the fog in the scene image and the fog level corresponding to the fog.

Specifically, in the embodiment of the present disclosure, the fog identification device performs classification processing on the global feature information, and determines the fog in the scene image and the fog level corresponding to the fog, including: performing average pooling on the global feature information , obtain the global average feature information; classify the global average feature information to determine the fog and the fog level.

It should be noted that, in the embodiment of the present disclosure, as shown in FIG. 2 , the fog identification device may store a global pooling layer and a preset classifier, and the fog identification device may utilize the global pooling layer to realize global features Average pooling of information. The global pooling layer can be set in the preset classifier, and in fact, it can also be set in the preset deep neural network, and its function is unchanged.

It should be noted that, in the embodiment of the present disclosure, as shown in FIG. 2 , the preset classifier may include a first fully connected layer, a second fully connected layer, and a normalization layer, which are used to realize the global average feature information Classification. Wherein, the number of fully connected layers may be two, of course, there may also be one or more than two layers. The specific number of fully connected layers can be set according to actual requirements, which is not limited in the embodiment of the present disclosure.

It can be understood that, in the embodiment of the present disclosure, since the global feature information is actually a feature image obtained by convolution processing, the fog identification device uses the global pooling layer to perform global average pooling on the global feature information, that is, the feature Each channel in the image is averaged, so as to avoid the influence of local area interference factors in the image on the fog recognition due to poor local light during imaging, which can improve the accuracy of fog recognition.

It should be noted that, in the embodiment of the present disclosure, the apparatus for identifying the fog may classify the global average feature information, thereby determining the fog in the scene image and the fog level corresponding to the fog. The global average feature information represents the average feature of the scene image, and the fog level actually represents the fog situation of the scene image.

It should be noted that, in the embodiment of the present disclosure, the fog identification device actually identifies the fog from the global average feature information, and classifies the fog level of the fog, so as to obtain different fog levels. As shown in Figure 2, the global average feature information matches the most information with the third level in the fog level. Therefore, the final determined fog level is the third level. In addition, the output fog level corresponds to the visibility. Compared with the current fog recognition scheme, the output is the presence or absence of fog, or the result of fog concentration, which can reflect the fog more clearly and flexibly. degree.

Exemplarily, in the embodiment of the present disclosure, in the corresponding relationship between the preset fog level and the visibility range, the visibility corresponding to the first fog level is 0-50 meters, and the visibility corresponding to the second fog level is 50-100 meters, the visibility corresponding to fog level 3 is 100-200 meters, the visibility corresponding to fog level 4 is 200-500 meters, and the visibility corresponding to fog level 5 is 500-1000 meters. The visibility corresponding to no fog is more than 1000 meters.

In the embodiment of the present disclosure, after determining the fog in the scene image and the fog level corresponding to the fog, the fog identification device may further perform the following steps: according to the preset correspondence between the fog grade and the visibility range, Obtain the first visibility range corresponding to the mass fog level; when the first visibility range is lower than the preset visibility range, obtain the first early warning information corresponding to the mass fog level; and output the first early warning information.

It should be noted that, in the embodiments of the present disclosure, the correspondence between the preset fog level and the visibility range is stored, different fog levels correspond to different visibility ranges, and the specific fog level corresponds to the visibility range. It may be preset according to the actual situation, which is not limited in the embodiment of the present disclosure.

It can be understood that, in the embodiment of the present disclosure, the device for identifying the fog mass may search for the corresponding visibility range from the correspondence between the preset fog mass level and the visibility range according to the obtained fog mass level, and use the obtained visibility range. The range is determined to be the first visibility range.

It should be noted that, in the embodiment of the present disclosure, a preset visibility range is stored in the fog identification device. For example, if the range is more than 500 meters, the fog level obtained by the fog identification device is between the fourth level and the first level. If the visibility is less than 500 meters, the corresponding early warning information needs to be determined. The specific early warning information may be determined according to the corresponding relationship between the preset fog mass level and the early warning information, which is not limited in the embodiment of the present disclosure.

It should be noted that, in the embodiments of the present disclosure, different fog mass levels correspond to different visibility degrees. Therefore, when the visibility range corresponding to the mass fog levels is lower than the preset visibility range, a corresponding early warning can be determined in time information, and output early warning information for prompting, providing early warning and scientific basis for the treatment of mass fog.

In the embodiment of the present disclosure, the scene image includes multiple images at multiple times, the fog level includes multiple levels corresponding to the multiple images, and the fog recognition device determines the fog in the scene image and the fog corresponding to the fog. After the fog level is determined, the following steps can also be performed: in the case that multiple levels are positively correlated with multiple times, obtain second warning information; obtain the warning reminder device in the target scene; transmit the second warning information to the warning reminder equipment.

It can be understood that, in the embodiment of the present disclosure, the fog identification device can obtain a large number of images of the target scene in a period of time, for example, multiple images on the highway in one day, and the obtained images are all scene images, that is, The scene image may include multiple images at multiple times. For each image, the fog recognition device can perform fog recognition respectively, so as to obtain the corresponding fog level, that is, multiple levels. If as the time increases, multiple levels also increase, that is, multiple levels are positively correlated with multiple times, then the fog that characterizes the target scene is increasing. The fog identification device can acquire the early warning and reminding equipment in the target scene, for example, a vehicle, so as to transmit the second early warning information indicating the increase of the fog to the early warning and reminding equipment.

In the embodiment of the present disclosure, the scene image includes multiple images at multiple times, the fog level includes multiple levels corresponding to the multiple images, and the fog recognition device determines the fog in the scene image and the fog corresponding to the fog. After the fog level of the target scene is determined, the following steps can also be performed: perform statistics on multiple levels to obtain statistical results; and determine the occurrence frequency of fog in the target scene according to the statistical results.

It can be understood that, in the embodiment of the present disclosure, the fog identification device performs statistical analysis on multiple levels corresponding to the obtained multiple images, and can determine the fog occurrence frequency of the target scene according to the statistical results. It analyzes the frequent occurrence points and periods of cluster fog, summarizes the law of occurrence of cluster mist, and generates a cluster mist prevention map, so that prevention can be carried out in advance at the frequent occurrence points and multiple occurrence periods, and the loss caused by cluster mist can be reduced.

An embodiment of the present disclosure provides a method for identifying fog clusters, including: acquiring a scene image of a target scene; performing feature extraction on the scene image to obtain global feature information; and classifying the global feature information to determine the cluster in the scene image Fog and fog fog level corresponding to fog fog. The fog fog identification method provided by the embodiments of the present disclosure adopts a deep neural network to extract global features of an image, which improves the effectiveness of fog fog information representation, reduces the interference of information irrelevant to fog fog recognition in the image, and improves the performance of the image. The accuracy of fog recognition.

An embodiment of the present disclosure provides a device for identifying a fog mass, and FIG. 3 is a schematic structural diagram 1 of a device for identifying a fog mass according to an embodiment of the present disclosure. As shown in Figure 3, the fog identification device includes:

an image acquisition module 201, configured to acquire a scene image of a target scene;

A feature extraction module 202, configured to perform feature extraction on the scene image to obtain global feature information;

The classification processing module 203 is configured to perform classification processing on the global feature information, and determine the fog in the scene image and the fog level corresponding to the fog.

FIG. 4 is a second structural schematic diagram of a device for identifying a fog mass according to an embodiment of the present application. As shown in FIG. 4 , in an embodiment of the present disclosure, the apparatus for identifying the fog mass further includes: an image processing module 204 ,

The image processing module 204 is configured to perform image preprocessing on the scene image to obtain a processed image;

Correspondingly, the feature extraction module 202 is specifically configured to perform feature extraction on the processed image to obtain the global feature information.

In an embodiment of the present disclosure, the image processing module 204 is specifically configured to perform pixel sampling on the scene image to obtain a sampled image of a target size; normalize the sampled image to obtain the processed image image.

In an embodiment of the present disclosure, the classification processing module 203 is specifically configured to perform average pooling on the global feature information to obtain global average feature information; classify the global average feature information to determine the cluster fog and the fog level.

In an embodiment of the present disclosure, as shown in FIG. 4 , the fog identification device further includes: an information output module 205. The information output module 205 is configured to, according to the corresponding relationship between the preset fog level and the visibility range, Acquire a first visibility range corresponding to the mass fog level; when the first visibility range is lower than a preset visibility range, acquire first warning information corresponding to the mass fog level; output the first early warning information .

In an embodiment of the present disclosure, the scene image includes multiple images at multiple times, and the mass fog level includes multiple levels corresponding to the multiple images,

The information output module 205 is configured to obtain second warning information under the condition that the multiple levels are positively correlated with the multiple times; obtain the warning reminding device in the target scene; The information is transmitted to the early warning reminder device.

In an embodiment of the present disclosure, as shown in FIG. 4 , the fog identification device further includes: a statistical processing module 206, the scene image includes multiple images at multiple times, and the fog level includes a multiple levels corresponding to the multiple images,

The statistics processing module 206 is configured to perform statistics on the multiple levels to obtain a statistical result; and determine the occurrence frequency of fog in the target scene according to the statistical result.

The embodiments of the present disclosure provide a fog identification device, which acquires a scene image of a target scene; extracts and extracts the scene image to obtain global feature information; performs classification processing on the global feature information to determine the fog in the scene image and the corresponding the fog level. The fog identification device provided by the embodiment of the present disclosure adopts a deep neural network to extract global features of an image, which improves the effectiveness of the information representation of the fog, reduces the interference of information irrelevant to the fog identification in the image, and improves the performance of the image. The accuracy of fog recognition.

Embodiments of the present disclosure provide an electronic device. FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 5 , the electronic device includes: a processor 301, a memory 302 and a communication bus 303; wherein,

The communication bus 303 is configured to implement connection communication between the processor 301 and the memory 302;

The processor 301 is configured to execute the mass fog identification program stored in the memory 302, so as to realize the above-mentioned mass fog identification method.

An embodiment of the present disclosure provides a computer-readable storage medium, where one or more programs are stored in the computer-readable storage medium, and the one or more programs can be executed by one or more processors, so as to realize the above group Fog identification method. The computer-readable storage medium may be a volatile memory (volatile memory), such as a random access memory (Random-Access Memory, RAM); or a non-volatile memory (non-volatile memory), such as a read-only memory (Read Only Memory) -Only Memory, ROM), flash memory (flash memory), hard disk (Hard Disk Drive, HDD) or solid-state drive (Solid-State Drive, SSD); it can also be a respective device including one or any combination of the above memories, Such as mobile phones, computers, tablet devices, personal digital assistants, etc.

As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including but not limited to disk storage, optical storage, and the like.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable signal processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable signal processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable signal processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded onto a computer or other programmable signal processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The above descriptions are merely preferred embodiments of the present disclosure, and are not intended to limit the protection scope of the present disclosure.

Industrial Applicability

Embodiments of the present disclosure provide a method, device, electronic device, storage medium, and computer program product for fog identification, wherein the method includes: acquiring a scene image of a target scene, performing feature extraction on the scene image, and obtaining global feature information; The global feature information is classified and processed to determine the fog in the scene image and the fog level corresponding to the fog. The technical solution provided by the embodiments of the present disclosure extracts global deep-level features of the image, improves the effectiveness of the fog information representation, reduces the interference of information irrelevant to the fog recognition in the image, and improves the performance of the fog recognition. accuracy.

Claims

A method for identifying a mass of fog, the method comprising:

Get the scene image of the target scene;

Perform feature extraction on the scene image to obtain global feature information;

The global feature information is classified and processed to determine the fog in the scene image and the fog level corresponding to the fog.
The method according to claim 1, wherein, before the feature extraction is performed on the scene image to obtain global feature information, the method further comprises:

Perform image preprocessing on the scene image to obtain a processed image;

Correspondingly, performing feature extraction on the scene image to obtain global feature information, including:

Feature extraction is performed on the processed image to obtain the global feature information.
The method for identifying fog clusters according to claim 2, wherein the performing image preprocessing on the scene image to obtain a processed image comprises:

performing pixel sampling on the scene image to obtain a sampled image of the target size;

Normalize the sampled image to obtain the processed image.
The method for identifying a fog cloud according to any one of claims 1 to 3, wherein the classification processing is performed on the global feature information to determine the fog in the scene image and the fog corresponding to the fog. levels, including:

performing average pooling on the global feature information to obtain global average feature information;

The global average feature information is classified to determine the fog and the fog level.
The method for identifying a fog cloud according to any one of claims 1 to 4, wherein after the fog in the scene image and the fog level corresponding to the fog are determined, the method further comprises:

obtaining a first visibility range corresponding to the fog mass level according to the corresponding relationship between the preset mass fog level and the visibility range;

In the case that the first visibility range is lower than the preset visibility range, acquiring first warning information corresponding to the fog mass level;

The first warning information is output.
The fog identification method according to any one of claims 1 to 5, wherein the scene image comprises a plurality of images at a plurality of times, and the fog level comprises a plurality of levels corresponding to the plurality of images , after determining the fog in the scene image and the fog level corresponding to the fog, the method further includes:

In the case that the multiple levels are positively correlated with the multiple times, acquiring second early warning information;

Obtain the early warning reminder device in the target scene;

The second early warning information is transmitted to the early warning reminder device.
The fog identification method according to any one of claims 1 to 5, wherein the scene image comprises a plurality of images at a plurality of times, and the fog level comprises a plurality of levels corresponding to the plurality of images , after determining the fog in the scene image and the fog level corresponding to the fog, the method further includes:

Perform statistics on the multiple levels to obtain statistical results;

According to the statistical result, the occurrence frequency of the fog mass in the target scene is determined.
A fog identification device, the fog identification device comprising:

an image acquisition module, configured to acquire a scene image of the target scene;

a feature extraction module, configured to perform feature extraction on the scene image to obtain global feature information;

The classification processing module is configured to perform classification processing on the global feature information, and determine the fog in the scene image and the fog level corresponding to the fog.
The fog identification device according to claim 8, further comprising: an image processing module,

The image processing module is configured to perform image preprocessing on the scene image to obtain a processed image;

Correspondingly, the feature extraction module is specifically configured to perform feature extraction on the processed image to obtain the global feature information.
The fog identification device according to claim 9, wherein:

The image processing module is specifically configured to perform pixel sampling on the scene image to obtain a sampled image of a target size; and perform normalization processing on the sampled image to obtain the processed image.
The fog identification device according to any one of claims 8 to 10, wherein,

The classification processing module is specifically configured to perform average pooling on the global feature information to obtain global average feature information; classify the global average feature information to determine the fog and the fog level.
The fog identification device according to any one of claims 8 to 11, further comprising: an information output module,

The information output module is configured to obtain a first visibility range corresponding to the fog level according to the corresponding relationship between the preset fog level and the visibility range; when the first visibility range is lower than the preset visibility range , obtain the first warning information corresponding to the mass fog level; and output the first warning information.
The fog identification device according to any one of claims 8 to 12, wherein the scene image comprises a plurality of images at a plurality of times, and the fog level comprises a plurality of levels corresponding to the plurality of images ,

The information output module is configured to obtain second early warning information under the condition that the multiple levels are positively correlated with the multiple times; obtain the early warning reminder device in the target scene; transmitted to the pre-warning reminder device.
The fog identification device according to any one of claims 8 to 12, further comprising: a statistical processing module, the scene image includes a plurality of images at a plurality of times, and the fog level includes a Each image corresponds to multiple levels,

The statistical processing module is configured to perform statistics on the multiple levels to obtain a statistical result; and determine the occurrence frequency of fog in the target scene according to the statistical result.
An electronic device comprising: a processor, a memory and a communication bus; wherein,

the communication bus, configured to implement connection communication between the processor and the memory;

The processor is configured to execute the fog identification program stored in the memory, so as to implement the fog identification method according to any one of claims 1-7.
A computer-readable storage medium storing one or more programs that can be executed by one or more processors to realize any one of claims 1-7 The described method for identifying the fog.
A computer program product comprising computer programs or instructions that, when the computer program or instructions are run on a computer, cause the computer to perform the method of any one of claims 1-7 .