CN111800582B - Frontal surface fog detection method and device, computer equipment and readable storage medium - Google Patents
Frontal surface fog detection method and device, computer equipment and readable storage medium Download PDFInfo
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Abstract
The application relates to a frontal fog detection method, a frontal fog detection device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog; if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area; and acquiring a frontal surface fog detection result of the target area according to each image. By adopting the method, the cost can be effectively controlled, and the accuracy of the frontal fog detection of the specific area can be improved.
Description
Technical Field
The application relates to the technical field of meteorological detection, in particular to a frontal surface fog detection method, a frontal surface fog detection device, computer equipment and a readable storage medium.
Background
Frontal fog refers to fog formed by the fact that water vapor condensate (cloud drops or rain drops) growing in a warm and humid air mass falls into a cooler air mass near the frontal surface of a cold-warm air boundary, and low-layer air near the ground is saturated through evaporation. The frontal fog often occurs near the frontal surface of the cold and warm air junction, the existence of the frontal fog seriously reduces the atmospheric visibility, and causes great influence on the traffic trip of people, such as causing road blockage, causing flight delay or cancellation, even causing traffic accidents and the like.
At present, a meteorological department mainly adopts a numerical prediction mode for detecting the fog, the numerical prediction needs a large amount of numerical calculation through a supercomputer, and the numerical prediction is generally used for simulating the atmospheric evolution process in a large area due to high cost and implementation difficulty, complex atmospheric evolution process and large data quantity and data area range required by the numerical prediction. For the frontal fog detection requirements of some small areas (such as airports, expressways and the like), at present, manual observation is mainly relied on.
However, in the above manual frontal fog observation mode, the number of artificial subjective factors is large in the observation result, and due to the fact that the sight line is blocked, the manual frontal fog cannot be observed at night, and the detection accuracy of the frontal fog is poor. Therefore, how to effectively control the cost and improve the accuracy of detecting the frontal fog in a specific area becomes a problem to be solved urgently at present.
Disclosure of Invention
In view of the above, there is a need to provide a frontal fog detection method, device, computer equipment and readable storage medium, which can effectively control the cost and improve the accuracy of frontal fog detection of a specific area.
In a first aspect, an embodiment of the present application provides a frontal fog detection method, including:
acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog;
if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area;
and acquiring a frontal surface fog detection result of the target area according to each image.
In one embodiment, the obtaining the front fog detection result of the target area according to each image includes:
detecting whether frontal fog exists in the target area or not according to each image;
and if the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear fog according to the historical temperature data and the current temperature data.
In one embodiment, the determining that the front fog detection result is front fog or rear fog according to the historical temperature data and the current temperature data includes:
acquiring a plurality of historical air temperature data and an air temperature change rule corresponding to the current air temperature data;
if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog;
and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
In one embodiment, the first environment data further includes a first precipitation amount of the target area, and the detecting whether the first environment data satisfies a first preset condition includes:
performing difference operation on the plurality of historical air temperature data and the current air temperature data, and determining air temperature change rules corresponding to the plurality of historical air temperature data and the current air temperature data according to operation results;
detecting whether the first precipitation is less than a first threshold;
and if the air temperature change rule is that the plurality of historical air temperature data and the current air temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than the first threshold, determining that the first environmental data meets the first preset condition.
In one embodiment, the detecting whether the frontal fog exists in the target area according to each image includes:
for each image, preprocessing the image to obtain a preprocessed image;
inputting the preprocessed image into a classification model to obtain a classification result, wherein the classification result is used for representing that the image comprises fog or the image does not comprise fog;
and if at least one image comprising fog exists in each image, determining that the frontal fog exists in the target area.
In one embodiment, the image includes an imaging area of a target object in the target area, and the preprocessing the image to obtain a preprocessed image includes:
acquiring the position frame coordinates of the imaging area, and intercepting a target object image corresponding to the imaging area from the image according to the position frame coordinates;
and carrying out scaling processing on the target object image according to a preset size to obtain the preprocessed image.
In one embodiment, after obtaining the front fog detection result of the target area, the method further includes:
acquiring second environment data of the target area, and detecting whether the second environment data meets a second preset condition, wherein the second preset condition is related to meteorological factors of frontal fog disappearance, and the second environment data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed and wind direction of the target area;
and if the second environment data meets the second preset condition, controlling the high-sensitivity imaging component to stop image acquisition.
In a second aspect, an embodiment of the present application provides a frontal fog detection device, the device includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first environment data of a target area and detecting whether the first environment data meets a first preset condition, and the first preset condition is related to meteorological factors formed by frontal fog;
the first control module is used for controlling the high-light-sensitivity imaging component to acquire at least one image corresponding to the target area if the first environment data meets the first preset condition;
and the detection module is used for acquiring the frontal fog detection result of the target area according to each image.
In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method according to the first aspect as described above.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
the method comprises the steps of acquiring first environment data of a target area, and detecting whether the first environment data meet a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog; if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area; acquiring a frontal surface fog detection result of the target area according to each image; therefore, the first preset condition is related to meteorological factors formed by the frontal fog, if the first environment data meet the first preset condition, the current environment of the target area is represented to easily form the frontal fog, and under the condition, the frontal fog detection result of the target area is obtained according to at least one image corresponding to the target area, so that the accuracy of the frontal fog detection can be improved; in addition, the image corresponding to the target area is acquired through the high-light-sensitivity imaging assembly, and due to the high-light-sensitivity characteristic of the high-light-sensitivity imaging assembly, the image with the similar imaging effect in the daytime can be acquired even at night or in an environment with poor light, so that the problem of poor accuracy of frontal fog detection caused by poor light or night can be avoided, and the accuracy of frontal fog detection can be further improved; in addition, because high sensitization formation of image subassembly is accurate instrument, under the condition that first environmental data satisfies first preset condition, control high sensitization formation of image subassembly collection image again, be favorable to prolonging the life-span of high sensitization formation of image subassembly to be favorable to controlling the cost that frontal surface fog detected.
Drawings
FIG. 1 is a schematic flow chart of a frontal haze detection method in one embodiment;
FIG. 2 is a schematic flow chart of a frontal fog detection method in another embodiment;
FIG. 3 is a diagram illustrating a detailed step of step S302 in another embodiment;
FIG. 4 is a schematic diagram of a partial refinement of step S100 in another embodiment;
FIG. 5 is a diagram illustrating a step of refining step S301 in another embodiment;
FIG. 6 is a schematic flow chart of a frontal fog detection method in another embodiment;
FIG. 7 is a block diagram of the frontal fog detection device in one embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The frontal fog detection method, the frontal fog detection device, the computer equipment and the readable storage medium aim to improve accuracy of frontal fog detection of a specific area while controlling frontal fog detection cost. The technical solution of the present application will be specifically described below by way of examples with reference to the accompanying drawings. The following specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
It should be noted that in the frontal fog detection method provided in the embodiment of the present application, an execution main body may be a frontal fog detection device, the frontal fog detection device may be implemented as part or all of a computer device in a software, hardware, or a combination of the software and the hardware, and the computer device may be a server. In the following method embodiments, the execution subject is a computer device as an example. It can be understood that the front fog detection method provided by the following method embodiments may also be applied to a terminal, may also be applied to a system including the terminal and a server, and is implemented through interaction between the terminal and the server.
In one embodiment, as shown in fig. 1, there is provided a frontal fog detection method, comprising the steps of:
step S100, acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition.
In the embodiment of the present application, the target area may be a specific area where frontal fog needs to be detected, for example, an airport or a highway. The computer device obtains the first environmental data of the target area, and may be obtained through related sensors, for example, the computer device may collect a first precipitation amount of the target area through a rainfall sensor disposed in the target area, collect air temperature data of the target area through a thermo-hygrometer, and so on.
In the embodiment of the application, the first preset condition is related to meteorological factors for frontal fog formation, and the first preset condition may include meteorological factors for frontal fog formation. If frontal fog often occurs near the frontal surface of the cold and warm air boundary, the air temperature needs to have an obvious change process, and the air temperature is accompanied by light rain or weak precipitation with precipitation below 5 mm. The computer device may then set the first preset condition as the temperature change law and precipitation threshold, etc., which are prone to the formation of frontal fog.
The computer device detects whether the first environment data meets a first preset condition, such as whether a first precipitation included in the first environment data is smaller than a precipitation threshold value. Thus, the computer device can determine whether the frontal fog is easily formed in the current environment of the target area represented by the first environment data by detecting whether the first environment data meets the first preset condition.
In the traditional technology, the cost and the implementation difficulty of numerical prediction are high, so that the frontal fog detection of small areas such as airports, expressways and the like mainly depends on manual observation, but the accuracy of the manual observation of the frontal fog is low. In the embodiment of the application, the computer device acquires the frontal surface fog detection result of the target area according to at least one image corresponding to the target area when detecting that the first environmental data meets the first preset condition, namely that the current environment of the target area easily forms frontal surface fog, so that the accuracy of the frontal surface fog detection can be improved.
Step S200, if the first environment data meets a first preset condition, controlling the high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area.
In one possible embodiment, multiple sets of sensors for acquiring first environmental data may be deployed in the target area. For example, four sets of sensors may be deployed in the target area, wherein one set of sensors is deployed on a landmark building at one geographic location of the target area, such as north, south, etc. of the target area, and one set of sensors may include a rainfall sensor, a hygrothermograph, etc. The computer device can detect whether the first environment data collected by each group of sensors meet a first preset condition, and if the first environment data collected by at least one group of sensors meet the first preset condition, the computer device controls the high-light-sensitive imaging assembly to collect at least one image corresponding to the target area.
As an implementation manner, taking the sampling frequency of the high-sensitivity imaging component as one acquisition per minute as an example, in each acquisition process, the computer device may control the high-sensitivity imaging component to acquire a plurality of images in different geographic orientations, and each geographic orientation acquires one image, so that a plurality of images corresponding to the target area may be obtained. The computer device is based on the plurality of images, so that more accurate front fog detection results of the target area can be obtained. It is to be understood that, in the case that the first environment data satisfies the first preset condition, the computer device may also control the high-light-sensitivity imaging component to acquire an image corresponding to the target area, which is not limited in this respect.
In the embodiment of the application, at least one image corresponding to the target area is acquired by the computer equipment through the high-light-sensitive imaging component, the high-light-sensitive imaging component can be a high-light-sensitive imager, and the high-light-sensitive imager can be arranged on a rotatable holder in the target area and is used for acquiring images in different geographic directions. Because of the performance limitation of the traditional camera, the computer equipment cannot identify the content in the pictures acquired by the traditional camera at night, so that the computer equipment cannot realize night detection of the front fog based on the pictures acquired by the traditional camera; and this application embodiment adopts high sensitization formation of image subassembly to acquire the image, and computer equipment is through the operating parameter who adjusts high sensitization formation of image subassembly for high sensitization formation of image subassembly also can gather the image similar with formation of image effect daytime under night or the relatively poor environment of light, thereby can realize the all-weather detection of frontal surface fog, has enlarged the detection range of frontal surface fog, and then has promoted the detection reliability of frontal surface fog.
In the embodiment of the application, because high sensitization formation of image subassembly is precision instrument, under the condition that first environmental data satisfies first preset condition, the high sensitization formation of image subassembly of computer equipment control just gathers the image, is favorable to prolonging the life-span of high sensitization formation of image subassembly to be favorable to controlling the cost that the frontal fog detected.
And step S300, acquiring a frontal fog detection result of the target area according to each image.
And the computer equipment acquires the current frontal surface fog detection result of the target area according to at least one image corresponding to the target area currently acquired by the high-light-sensitive imaging component. As an implementation manner, the computer device detects each image respectively, if fog exists in at least one image, the computer device determines that the frontal fog exists in the target area, and if fog does not exist in each image, the computer device determines that the frontal fog does not exist in the target area.
In a possible implementation manner, the computer device detects each image separately, and may classify whether each image includes fog or not by using a classification model trained in advance to obtain a classification result. And if the classification result of at least one image in the images is fog, the computer equipment determines that the frontal fog exists in the target area. Therefore, accurate detection of the frontal fog is achieved by combining meteorological factors and a depth learning algorithm formed by the frontal fog, timely early warning of the frontal fog can be achieved for a target area (such as an airport or a highway), and loss caused by the frontal fog is avoided.
In another possible implementation, the computer device detects each image separately, or clusters each image separately by using a clustering algorithm, and determines whether each image includes fog according to the clustering result, and if at least one image including fog exists in each image, the computer device determines that the frontal fog exists in the target area, which is not limited specifically herein.
In the embodiment, first environmental data of a target area is acquired, and whether the first environmental data meets a first preset condition is detected, wherein the first preset condition is related to meteorological factors formed by frontal fog; if the first environmental data meet a first preset condition, controlling the high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area; acquiring a frontal surface fog detection result of the target area according to each image; therefore, the first preset condition is related to meteorological factors formed by the frontal fog, if the first environment data meet the first preset condition, the current environment of the target area is represented to easily form the frontal fog, and under the condition, the frontal fog detection result of the target area is obtained according to at least one image corresponding to the target area, so that the accuracy of the frontal fog detection can be improved; in addition, the image corresponding to the target area is acquired through the high-light-sensitivity imaging assembly, and due to the high-light-sensitivity characteristic of the high-light-sensitivity imaging assembly, the image with the similar imaging effect in the daytime can be acquired even at night or in an environment with poor light, so that the problem of poor accuracy of frontal fog detection caused by poor light or night can be avoided, and the accuracy of frontal fog detection can be further improved; in addition, because high sensitization formation of image subassembly is accurate instrument, under the condition that first environmental data satisfies first preset condition, control high sensitization formation of image subassembly collection image again, be favorable to prolonging the life-span of high sensitization formation of image subassembly to be favorable to controlling the cost that frontal surface fog detected.
In one embodiment, referring to fig. 2, the embodiment is related to a process of how the computer device acquires the detection result of the front fog of the target area according to each image based on the embodiment shown in fig. 1. As shown in fig. 2, step S300 of the present embodiment may include step S301 and step S302:
and S301, detecting whether the frontal fog exists in the target area or not according to the images.
As an embodiment, the computer device may classify whether each image includes fog or not by using a classification model trained in advance, so as to obtain a classification result; if at least one image classification result in each image is fog, determining that frontal fog exists in the target area by the computer equipment; and if the fog is not included in each image, the computer equipment determines that no frontal fog exists in the target area.
And step S302, if the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear fog according to the historical air temperature data and the current air temperature data.
In the embodiment of the present application, the first environment data includes a plurality of historical temperature data and current temperature data, and the plurality of historical temperature data and the current temperature data are sequentially adjacent to each other in time series. The first environment data may be environment data of the target area in a target time period, and include 9 historical air temperature data collected by the sensors in the previous 9 minutes in the target time period and current air temperature data collected by the sensors at the current time, for example, the target time period is 10 minutes, and the sampling frequency of the sensor for collecting the first environment data is once per minute.
And if the computer equipment determines that the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear frontal fog according to the plurality of historical temperature data and the current temperature data. The front fog is divided into front fog and rear fog, wherein the front fog means that the front fog appears in front of the warm front close to the ground, and the rear fog means that the front fog appears behind the cold front close to the ground.
In the embodiment of the application, the computer equipment can determine that the front fog detection result is front fog or rear fog according to the air temperature change rule corresponding to the historical air temperature data and the current air temperature data. In one possible embodiment, referring to fig. 3, in the case of a frontal fog in a target area, the process of determining, by the computer device, a frontal fog detection as a frontal fog or a rear fog based on the plurality of historical temperature data and the current temperature data may include the steps of:
in step S3021, temperature change rules corresponding to a plurality of historical temperature data and current temperature data are obtained.
The computer equipment acquires a plurality of temperature change rules corresponding to the historical temperature data and the current temperature data, wherein the temperature change rules can be that the historical temperature data and the current temperature data sequentially increase in time sequence, or the historical temperature data and the current temperature data sequentially decrease in time sequence.
In a possible implementation, the computer device may perform difference operations on a plurality of historical temperature data and current temperature data in sequence, that is, subtracting the temperature data of the next minute from the temperature data of the previous minute to obtain a difference result; if all the difference results are less than 0, the computer equipment determines that the temperature change rule is that the plurality of historical temperature data and the current temperature data are sequentially decreased in time sequence; and if the difference results are all larger than 0, the computer equipment determines that the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence.
In another possible implementation, the computer device may also directly obtain the temperature change rules corresponding to the plurality of historical temperature data and the current temperature data from the database. The air temperature change rule may be determined in a process of detecting whether the first environment data satisfies a first preset condition after the computer device acquires the first environment data of the target area, and the computer device stores the determined air temperature change rule. The embodiment of the application does not specifically limit the way in which the computer equipment acquires the temperature change rules corresponding to the plurality of historical temperature data and the current temperature data.
And step S3022, if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog.
After the computer equipment acquires the temperature change rule, if the temperature change rule is that a plurality of historical temperature data and current temperature data are sequentially increased in time sequence, the computer equipment determines that the frontal fog detection result is frontal fog.
And step S3023, if the temperature change rule is that the historical temperature data and the current temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
And after the computer equipment acquires the temperature change rule, if the temperature change rule is that the historical temperature data and the current temperature data are sequentially decreased in time sequence, the computer equipment determines that the frontal fog detection result is the frontal fog.
This embodiment computer equipment is through obtaining the temperature change law that a plurality of historical temperature data and current temperature data correspond, and the frontal fog that exists is the front fog specifically or the rear fog based on this temperature change law determination target area, from this, has further promoted the detection accuracy of frontal fog.
In one embodiment, on the basis of the embodiment shown in fig. 2, referring to fig. 4, the present embodiment relates to a process of how the computer device detects whether the first environment data satisfies the first preset condition. In this embodiment, the first environment data further includes a first precipitation amount of the target area, and as shown in fig. 4, the process may include step S101, step S102, and step S103:
and step S101, carrying out difference operation on the plurality of historical temperature data and the current temperature data, and determining temperature change rules corresponding to the plurality of historical temperature data and the current temperature data according to the operation result.
For example, the plurality of historical air temperature data are Temp1 and Temp2.. Temp9, the current air temperature data are Temp10, and the computer device may perform a difference operation on the plurality of historical air temperature data and the current air temperature data by using Temp10-Temp9, Temp9-Temp8,. the Temp3-Temp2, and Temp2-Temp1, thereby obtaining a plurality of operation results. If the operation results are all larger than 0, the computer equipment determines that the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence; and if all the operation results are less than 0, determining that the temperature change rule is that the historical temperature data and the current temperature data are sequentially decreased in time sequence.
Step S102, whether the first precipitation is smaller than a first threshold value is detected.
In the embodiment of the application, the first threshold may be a precipitation amount value which is easy to form frontal fog, if the frontal fog is often generated along with light rain or weak precipitation with the precipitation amount being less than 5mm, the first threshold may be set to 5mm, and the computer device detects whether the current first precipitation amount of the target area is less than the first threshold.
And S103, if the temperature change rule is that the historical temperature data and the current temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than a first threshold, determining that the first environmental data meets a first preset condition.
If the computer equipment detects that the plurality of historical air temperature data and the current air temperature data sequentially increase in time sequence or sequentially decrease in time sequence, namely the air temperature rises or the air temperature falls, and the first precipitation is smaller than a first threshold value, it is determined that the first environment data meets a first preset condition, namely the current environment of the target area is easy to form frontal fog.
Under the condition that the first environmental data meet a first preset condition, the computer equipment controls the high-light-sensitivity imaging assembly to be started to acquire at least one image corresponding to the target area, so that the loss of the high-light-sensitivity imaging assembly can be reduced, and the service life of the high-light-sensitivity imaging assembly is prolonged; the embodiment of the application carries out the frontal surface fog to the target area through the meteorological factor that combines the frontal surface fog to form and detects, can promote the detection accuracy.
In one embodiment, on the basis of the embodiment shown in fig. 2, referring to fig. 5, the present embodiment relates to a process of how the computer device detects whether or not there is a frontal fog in the target area according to each image. As shown in fig. 5, step S301 may include step S3011, step S3012, and step S3013:
step S3011, preprocess the image for each image to obtain a preprocessed image.
In an embodiment of the present application, the image captured by the high-sensitivity imaging assembly includes an imaging area of a target object in the target area, and the target object may be a landmark building. As one embodiment, when the high-sensitivity imaging assembly is deployed, corresponding landmark buildings exist in pictures with different geographic positions, and the distance between each landmark building and the high-sensitivity imaging assembly can be distributed in a range of 50m-2 km. The computer device preprocesses the image, and the preprocessed image can be obtained by executing the following steps A1 and A2:
and A1, acquiring the position frame coordinates of the imaging area, and intercepting the target object image corresponding to the imaging area from the image according to the position frame coordinates.
The computer device obtains the coordinates of the position frame of the imaging area, and the coordinates of the position frame may be manually input into the computer device, or may be detected by the computer device by using a target detection algorithm, which is not specifically limited herein. And the computer device intercepts the target object image matched with the position frame coordinates from the image.
And step A2, zooming the target object image according to the preset size to obtain a preprocessed image.
In this embodiment, the preset size may be a required input size of the classification model, and the computer device scales the target image to the preset size to obtain the preprocessed image.
And step S3012, inputting the preprocessed image into a classification model to obtain a classification result.
In the embodiment of the application, the computer equipment can collect sample images under different weather conditions at different times such as day, night, sunny days, rainy days and foggy days through the high-light-sensitivity imaging assembly, and obtains a training sample set by manually marking the coordinates of the position frame of the landmark building in each sample image and adding a label for judging whether fog exists or not.
For each sample image in the training sample set, the computer device intercepts a corresponding picture from the sample image by using the position frame coordinates of the corresponding landmark building, and after the intercepted picture is scaled to a preset size, the picture and the label are input into an initial classification model together for model training, and a loss value L is calculated, wherein a calculation formula of the loss value can be as shown in formula 1:
wherein C represents a preset number of categories, and if the preset categories are 0 and 1, C is 2; si represents the prediction probability that a sample image belongs to the ith class, and pi represents the actual probability that the sample image belongs to the ith class.
Therefore, the computer equipment calculates the back propagation gradient according to the loss value L, iteratively updates the network parameters of the initial classification model, and determines the convergence of the initial classification model when the L tends to be stabilized to a value of about 0.01 or less to obtain the classification model.
In the embodiment of the present application, the initial classification model may be any classification network framework, for example, an AlexNet neural network, and the AlexNet may be built from 5 convolutional layers, 5 relu active layers, 3 pooling layers, and 3 full connection layers.
The computer equipment inputs the preprocessed image into the classification model to obtain a classification result, and the classification result is used for representing that the image comprises fog or the image does not comprise fog, for example, the classification result "0" represents that the image does not comprise fog, and the classification result "1" represents that the image comprises fog.
Step S3013, if at least one image including fog exists in each image, it is determined that there is frontal fog in the target area.
If at least one image comprising fog exists in at least one image corresponding to the target area, the computer equipment determines that the frontal fog exists in the target area, and if all the images do not comprise the fog, the computer equipment determines that the frontal fog does not exist in the target area.
The first environmental data of real time monitoring target area is passed through to this embodiment, combines the meteorological characteristic that frontal surface fog formed, realizes the accurate detection of target area frontal surface fog based on target area's image and classification model to can realize the timely early warning of frontal surface fog to target area (like airport or highway etc.), avoid because of the loss that the frontal surface fog caused.
On the basis of the embodiment shown in fig. 1, referring to fig. 6, fig. 6 is a schematic flow chart of a front fog detection method according to another embodiment. As shown in fig. 6, after step S300, the method for detecting frontal fog in this embodiment further includes:
step S401, obtaining second environment data of the target area, and detecting whether the second environment data meets a second preset condition.
The second preset condition is related to meteorological factors of frontal fog disappearance, and the second environmental data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed of the target area and wind direction.
In this embodiment of the application, the sensor deployed in the target area may further include an anemorumbometer, and taking the example that the second environmental data includes a second precipitation amount of the target area, a relative humidity of the target area, a wind speed of the target area, and a wind direction, the computer device acquires the second precipitation amount of the target area through the precipitation sensor deployed in the target area, acquires the relative humidity of the target area through the hygrothermograph, and acquires the wind speed and the wind direction of the target area through the anemorumbometer.
Weather factors of frontal fog disappearance, such as precipitation ending, relative humidity falling below 85%, wind speed first becoming small to be close to calm wind and then becoming large, and wind direction becoming close to reverse when wind speed is close to calm wind. Therefore, the computer device detects whether the second environment data satisfies the second preset condition, which may be whether the second precipitation amount of the target area is 0, whether the relative humidity of the target area is less than 85%, whether the wind speed of the target area undergoes a first decrease to approach a calm wind and then increases, and whether the wind direction of the target area becomes to approach a reverse direction when the wind speed approaches the calm wind.
In one possible embodiment, the computer device may further acquire wind speeds and wind directions at a plurality of historical times before the current time, fit a variation curve of the wind speeds and the wind directions according to the acquired historical data, determine whether the wind speed of the target area undergoes first decreasing to approach a calm wind and then increasing according to the variation curve, and determine whether the wind direction of the target area becomes close to reverse when the wind speed approaches the calm wind.
And S402, if the second environment data meets a second preset condition, controlling the high-sensitivity imaging component to stop image acquisition.
If the computer equipment detects that the second environmental data meets the second preset condition, the representation frontal surface passes the boundary, the computer equipment no longer has the condition of generating frontal surface fog, and the computer equipment controls the high-photosensitive imaging assembly to stop image acquisition, namely closes the high-photosensitive imaging assembly, so that the loss of the high-photosensitive imaging assembly is reduced, the service life of the high-photosensitive imaging assembly is prolonged, and the frontal surface fog detection cost is reduced.
It should be understood that although the various steps in the flow charts of fig. 1-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-6 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 7, there is provided a frontal fog detecting device, including:
the system comprises a first acquisition module 100, a second acquisition module, a third acquisition module and a fourth acquisition module, wherein the first acquisition module is used for acquiring first environment data of a target area and detecting whether the first environment data meets a first preset condition, and the first preset condition is related to meteorological factors formed by frontal fog;
the first control module 200 is configured to control the high-sensitivity imaging component to acquire at least one image corresponding to the target area if the first environment data meets the first preset condition;
the detection module 300 is configured to obtain a frontal fog detection result of the target area according to each image.
In one embodiment, the first environment data includes a plurality of historical temperature data and current temperature data, the plurality of historical temperature data and the current temperature data are sequentially adjacent in time sequence, and the detection module 300 includes:
the first detection unit is used for detecting whether frontal fog exists in the target area or not according to each image;
and the first determining unit is used for determining that the front fog detection result is front fog or rear fog according to the historical air temperature data and the current air temperature data if the front fog exists in the target area.
In one embodiment, the first determining unit is specifically configured to, if a front fog exists in the target area, obtain temperature change rules corresponding to the plurality of historical temperature data and the current temperature data; if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog; and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
In one embodiment, the first environment data further includes a first precipitation amount of the target area, and the first obtaining module 100 includes:
the difference unit is used for carrying out difference operation on the plurality of historical air temperature data and the current air temperature data and determining air temperature change rules corresponding to the plurality of historical air temperature data and the current air temperature data according to an operation result;
the second detection unit is used for detecting whether the first precipitation is smaller than a first threshold value or not;
and the second determining unit is used for determining that the first environment data meets the first preset condition if the temperature change rule is that the plurality of historical temperature data and the current temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than the first threshold.
In an embodiment, the first detecting unit is specifically configured to perform preprocessing on the image for each image to obtain a preprocessed image; inputting the preprocessed image into a classification model to obtain a classification result, wherein the classification result is used for representing that the image comprises fog or the image does not comprise fog; and if at least one image comprising fog exists in each image, determining that the frontal fog exists in the target area.
In one embodiment, the first detection unit is further specifically configured to acquire a position frame coordinate of the imaging area, and intercept, from the image, a target object image corresponding to the imaging area according to the position frame coordinate; and carrying out scaling processing on the target object image according to a preset size to obtain the preprocessed image.
In one embodiment, the apparatus further comprises:
the second acquiring module is used for acquiring second environment data of the target area and detecting whether the second environment data meets a second preset condition, wherein the second preset condition is related to meteorological factors of frontal fog disappearance, and the second environment data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed and wind direction of the target area;
and the second control module is used for controlling the high-light-sensitivity imaging component to stop image acquisition if the second environmental data meets the second preset condition.
For specific definition of the frontal fog detection device, reference may be made to the definition of the frontal fog detection method above, and details are not repeated here. All or part of the modules in the frontal surface fog detection device can be realized by software, hardware and the combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data of the frontal fog detection method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a front fog detection method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog;
if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area;
and acquiring a frontal surface fog detection result of the target area according to each image.
In one embodiment, the first environmental data comprises a plurality of historical air temperature data and current air temperature data, the plurality of historical air temperature data and the current air temperature data being chronologically adjacent, the processor when executing the computer program further performing the steps of:
detecting whether frontal fog exists in the target area or not according to each image;
and if the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear fog according to the historical temperature data and the current temperature data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a plurality of historical air temperature data and an air temperature change rule corresponding to the current air temperature data;
if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog;
and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
In one embodiment, the first environmental data further comprises a first precipitation for the target area, the processor when executing the computer program further performs the steps of:
performing difference operation on the plurality of historical air temperature data and the current air temperature data, and determining air temperature change rules corresponding to the plurality of historical air temperature data and the current air temperature data according to operation results;
detecting whether the first precipitation is less than a first threshold;
and if the air temperature change rule is that the plurality of historical air temperature data and the current air temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than the first threshold, determining that the first environmental data meets the first preset condition.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
for each image, preprocessing the image to obtain a preprocessed image;
inputting the preprocessed image into a classification model to obtain a classification result, wherein the classification result is used for representing that the image comprises fog or the image does not comprise fog;
and if at least one image comprising fog exists in each image, determining that the frontal fog exists in the target area.
In one embodiment, the image comprises an imaged area of a target object in the target area, the processor when executing the computer program further performs the steps of:
acquiring the position frame coordinates of the imaging area, and intercepting a target object image corresponding to the imaging area from the image according to the position frame coordinates;
and carrying out scaling processing on the target object image according to a preset size to obtain the preprocessed image.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring second environment data of the target area, and detecting whether the second environment data meets a second preset condition, wherein the second preset condition is related to meteorological factors of frontal fog disappearance, and the second environment data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed and wind direction of the target area;
and if the second environment data meets the second preset condition, controlling the high-sensitivity imaging component to stop image acquisition.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog;
if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area;
and acquiring a frontal surface fog detection result of the target area according to each image.
In one embodiment, the first environmental data comprises a plurality of historical air temperature data and current air temperature data, the plurality of historical air temperature data and the current air temperature data being chronologically adjacent, the computer program when executed by the processor further implementing the steps of:
detecting whether frontal fog exists in the target area or not according to each image;
and if the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear fog according to the historical temperature data and the current temperature data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a plurality of historical air temperature data and an air temperature change rule corresponding to the current air temperature data;
if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog;
and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
In an embodiment, the first environmental data further comprises a first precipitation amount for the target area, the computer program when executed by the processor further realizing the steps of:
performing difference operation on the plurality of historical air temperature data and the current air temperature data, and determining air temperature change rules corresponding to the plurality of historical air temperature data and the current air temperature data according to operation results;
detecting whether the first precipitation is less than a first threshold;
and if the air temperature change rule is that the plurality of historical air temperature data and the current air temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than the first threshold, determining that the first environmental data meets the first preset condition.
In one embodiment, the computer program when executed by the processor further performs the steps of:
for each image, preprocessing the image to obtain a preprocessed image;
inputting the preprocessed image into a classification model to obtain a classification result, wherein the classification result is used for representing that the image comprises fog or the image does not comprise fog;
and if at least one image comprising fog exists in each image, determining that the frontal fog exists in the target area.
In an embodiment, the image comprises an imaged area of an object in the target area, the computer program, when executed by the processor, further realizes the steps of:
acquiring the position frame coordinates of the imaging area, and intercepting a target object image corresponding to the imaging area from the image according to the position frame coordinates;
and carrying out scaling processing on the target object image according to a preset size to obtain the preprocessed image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring second environment data of the target area, and detecting whether the second environment data meets a second preset condition, wherein the second preset condition is related to meteorological factors of frontal fog disappearance, and the second environment data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed and wind direction of the target area;
and if the second environment data meets the second preset condition, controlling the high-sensitivity imaging component to stop image acquisition.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A front fog detection method, comprising:
acquiring first environment data of a target area, and detecting whether the first environment data meets a first preset condition, wherein the first preset condition is related to meteorological factors formed by frontal fog, the first environment data comprises a plurality of historical air temperature data and current air temperature data, and the plurality of historical air temperature data and the current air temperature data are sequentially adjacent in time sequence;
if the first environmental data meet the first preset condition, controlling a high-light-sensitivity imaging assembly to acquire at least one image corresponding to the target area;
detecting whether the frontal fog exists in the target area or not according to each image, and if at least one image in each image comprises fog, determining that the frontal fog exists in the target area;
and if the frontal fog exists in the target area, determining that the frontal fog detection result is the frontal fog or the rear fog according to the historical temperature data and the current temperature data.
2. The method according to claim 1, wherein said determining that the front fog detection is front fog or rear fog based on the plurality of historical air temperature data and the current air temperature data comprises:
acquiring a plurality of historical air temperature data and an air temperature change rule corresponding to the current air temperature data;
if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog;
and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
3. The method according to claim 1 or 2, wherein the first environment data further comprises a first precipitation amount of the target area, and the detecting whether the first environment data satisfies a first preset condition comprises:
performing difference operation on the plurality of historical air temperature data and the current air temperature data, and determining air temperature change rules corresponding to the plurality of historical air temperature data and the current air temperature data according to operation results;
detecting whether the first precipitation is less than a first threshold;
and if the air temperature change rule is that the plurality of historical air temperature data and the current air temperature data sequentially increase in time sequence or sequentially decrease in time sequence, and the first precipitation is smaller than the first threshold, determining that the first environmental data meets the first preset condition.
4. The method according to claim 1, wherein the detecting whether the target area is frontal fog from each of the images comprises:
for each image, preprocessing the image to obtain a preprocessed image;
inputting the preprocessed image into a classification model to obtain a classification result, wherein the classification result is used for representing that the image comprises fog or the image does not comprise fog;
and if at least one image comprising fog exists in each image, determining that the frontal fog exists in the target area.
5. The method of claim 4, wherein the image comprises an imaged region of a target object in the target region, and wherein pre-processing the image to obtain a pre-processed image comprises:
acquiring the position frame coordinates of the imaging area, and intercepting a target object image corresponding to the imaging area from the image according to the position frame coordinates;
and carrying out scaling processing on the target object image according to a preset size to obtain the preprocessed image.
6. The method according to claim 1, wherein after the obtaining the front fog detection result of the target area, the method further comprises:
acquiring second environment data of the target area, and detecting whether the second environment data meets a second preset condition, wherein the second preset condition is related to meteorological factors of frontal fog disappearance, and the second environment data comprises at least one of second precipitation of the target area, relative humidity of the target area, wind speed and wind direction of the target area;
and if the second environment data meets the second preset condition, controlling the high-sensitivity imaging component to stop image acquisition.
7. A frontal fog detection device, characterized in that the device comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first environment data of a target area and detecting whether the first environment data meets a first preset condition, the first preset condition is related to meteorological factors formed by frontal fog, the first environment data comprises a plurality of historical air temperature data and current air temperature data, and the historical air temperature data and the current air temperature data are sequentially adjacent in time sequence;
the first control module is used for controlling the high-light-sensitivity imaging component to acquire at least one image corresponding to the target area if the first environment data meets the first preset condition;
the detection module is used for acquiring a frontal fog detection result of the target area according to each image;
the detection module comprises: the first detection unit is used for detecting whether the frontal fog exists in the target area according to each image, and if at least one image in each image comprises fog, determining that the frontal fog exists in the target area; and the first determining unit is used for determining that the front fog detection result is front fog or rear fog according to the historical air temperature data and the current air temperature data if the front fog exists in the target area.
8. The device according to claim 7, wherein the first determining unit is specifically configured to, if a frontal fog exists in the target area, obtain an air temperature change rule corresponding to the plurality of historical air temperature data and the current air temperature data; if the temperature change rule is that the historical temperature data and the current temperature data are sequentially increased in time sequence, determining that the frontal fog detection result is frontal fog; and if the air temperature change rule is that the historical air temperature data and the current air temperature data are sequentially decreased in time sequence, determining that the front fog detection result is front fog.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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