CN112396020A - Snow depth monitoring system based on artificial intelligence algorithm - Google Patents
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Abstract
The invention provides a snow depth monitoring system based on an artificial intelligence algorithm. The method comprises the following steps: the snow depth sensor collects snow depth data values in a monitoring scene; the controller receives the snow depth data value transmitted by the snow depth sensor, judges whether the snow depth data value is larger than a set snowing threshold value or not, and if so, the controller sends an acquisition signal to the camera and forwards a field outdoor scene image returned by the camera to the processor; after receiving an acquisition signal sent by the controller, the camera acquires an off-site scene image of a monitored scene and transmits the acquired off-site scene image to the controller; the processor calculates the scene external scene image by using the artificial intelligent snow depth monitoring model to obtain the snow depth value of the monitoring scene, and judges whether the monitoring scene snows according to the snow depth value. The invention adopts the deep learning artificial intelligence algorithm to judge whether snow falls or not, reduces the influence of interference factors on snow depth monitoring, can effectively monitor the snow depth and effectively monitor the snow blowing disaster.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a snow depth monitoring system based on an artificial intelligence algorithm.
Background
After or during snowfall, when the wind speed is high, the air flow moves with the snow particles, so that a two-phase flow of air and snow particles is formed, which is called wind-snow flow, and the process of carrying and depositing snow by wind is called wind blowing snow. The wind blowing snow disaster can seriously affect traffic infrastructures such as roads, for example, strong wind is usually accompanied when the wind blowing snow disaster happens, snow particles carried by airflow move in the air, the visibility of the road surface is reduced or the road surface is buried, and the driving safety is seriously affected. Snow melting in later stage can permeate into the road bed, induces phenomena such as thick liquid turning, frost heaving and the like, reduces the service life of roads, and brings great inconvenience to local transportation industry.
The method is particularly critical for effectively monitoring the wind blowing snow phenomenon, and in the wind blowing snow monitoring, the snow depth is taken as an important monitoring parameter and needs to be accurately measured. At present, in the methods for monitoring snow depth in the prior art, sensors based on ultrasonic ranging or laser ranging are generally adopted for detection, the methods can only monitor the snow depth of a single point, and the monitoring process is interfered by various factors, so that the monitoring result is inaccurate. For example, the influence of dust and sand, the influence of sundries, etc. are misjudged as snowfall and the snow depth is calculated.
Disclosure of Invention
The embodiment of the invention provides a snow depth monitoring system based on an artificial intelligence algorithm, which aims to overcome the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A snow depth monitoring system based on artificial intelligence algorithm, comprising: the snow depth sensor comprises a controller, a snow depth sensor, a camera and a processor, wherein the controller is respectively in circuit connection with the snow depth sensor, the camera and the processor;
the snow depth sensor is used for acquiring snow depth data values in a monitoring scene according to a set time interval and transmitting the acquired snow depth data values to the controller;
the controller is used for receiving the snow depth data value transmitted by the snow depth sensor, judging whether the snow depth data value is larger than a set snowing threshold value or not, if so, sending an acquisition signal to the camera by the controller, and forwarding a field outdoor scene image returned by the camera to the processor; otherwise, the controller does not send a collecting signal to the camera;
the camera is used for collecting the scene external scene image of the monitored scene after receiving the collecting signal sent by the controller and transmitting the collected scene external scene image to the controller;
and the processor is used for calculating the field outdoor scene image by using an artificial intelligent snow depth monitoring model after receiving the field outdoor scene image transmitted by the controller to obtain a snow depth value of the monitored scene, and judging whether the monitored scene snows according to the snow depth value.
Preferably, the processor is specifically configured to record a current snow depth value if it is determined that the monitored scene is snowfall according to the snow depth value; and if the monitored scene is judged not to be snowfall, updating the initial value of the snow depth without recording.
Preferably, the processor is specifically configured to train an artificial intelligence snow depth monitoring model by using a deep learning VGG16 network, and the training process includes the following processing steps:
(1) collecting field outdoor scene images in a plurality of monitoring scenes, judging and labeling the field outdoor scene images, and dividing the field outdoor scene images into snow scene images and non-snow scene images;
(2) performing edge cutting and horizontal turning pretreatment on the snow scene picture;
(3) designing a structure of a deep learning VGG16 network, finely adjusting full-connection layer output according to the categories of snow-scene pictures and non-snow-scene pictures which need to be distinguished, and defining a cross entropy loss function, an Adam optimizer and the like;
(4) randomly distributing the collected scene external scene images, and according to a training set: the test set was 8: 2, dividing pictures according to the proportion, training the deep learning VGG16 network by using a training set to adjust proper iteration times, and primarily checking classification results by using a test set picture;
(5) adjusting the learning rate, and retraining and testing the artificial intelligent snow depth monitoring model according to the accuracy of the obtained picture classification of whether snow falls or not;
(6) drawing the indexes of accuracy, loss function, ROC curve and PR curve in the training process of the snowfall-free image classification, and further evaluating the performance of the artificial intelligent snow depth monitoring model.
According to the technical scheme provided by the embodiment of the invention, the camera is used for shooting the scene in real time, and the judgment of whether the snow falls is carried out by adopting the depth learning artificial intelligence algorithm in combination with the measurement value of the snow depth sensor, so that the influence of interference factors on snow depth monitoring is reduced, and the snow depth monitoring can be carried out more accurately.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a structural diagram of a snow depth monitoring system based on an artificial intelligence algorithm according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an operating principle of a snow depth monitoring system based on an artificial intelligence algorithm according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a training process of an artificial intelligence snow depth monitoring model of a processor in the snow depth monitoring system according to the embodiment of the present invention.
Fig. 4 is a photograph of snowfall collected on site according to an embodiment of the present invention.
Fig. 5 is a photograph of a scene taken without snowfall according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Fig. 1 is a structural diagram of a snow depth monitoring system based on an artificial intelligence algorithm according to an embodiment of the present invention, as shown in fig. 1, and a schematic diagram of a working principle is shown in fig. 2, where the snow depth monitoring system includes: the snow depth monitoring system comprises a controller (1), a snow depth sensor (2), a camera (3) and a processor (4). The controller (1) is respectively connected with the snow depth sensor (2), the camera (3) and the processor (4) through circuits.
The controller (1) sends out snow depth and gathers the instruction, and snow depth sensor (2) give controller (1) with snow depth numerical value transmission, and controller (1) sends image acquisition instruction, and camera (3) are with image data transmission to controller (1), and several data are synthesized in controller (1), and the transmission is for treater (4) in unison.
The snow depth sensor (2) is used for collecting snow depth data values in a monitoring scene according to a set time interval and transmitting the collected snow depth data values to the controller (1). The principle adopted by the snow depth sensor (2) can be an ultrasonic method or a laser ranging method.
The controller (1) is used for receiving the snow depth data value transmitted by the snow depth sensor (2), judging whether the snow depth data value is larger than a set snowing threshold value, wherein the snowing threshold value is the numerical value of the snow depth sensor (2) during installation, if so, the controller (1) sends an acquisition signal to the camera (3), and transmits a scene external image returned by the camera (3) to the processor (4); otherwise, the controller (1) does not send a collection signal to the camera (3). The controller (1) can be realized by embedded processors such as a single chip microcomputer and an ARM processor.
The camera (3) is used for collecting the scene external scene image of the monitoring scene after receiving the collecting signal sent by the controller (1) and transmitting the collected scene external scene image to the controller (1);
and the processor (4) is used for calculating the field outdoor scene image by using an artificial intelligent snow depth monitoring model after receiving the field outdoor scene image transmitted by the controller (1) to obtain a snow depth value of the monitored scene, automatically judging whether the monitored scene is snowy or not according to information such as the snow depth value and the like, recording the current snow depth value if the judgment is snowing, and proving that the snow depth change is caused by other interference without recording if the judgment is that the monitored scene is not snowing, wherein only the initial value of the snow depth needs to be updated. The processor (4) can be realized by high-performance embedded processors such as a raspberry pi and a digital signal processor. The artificial intelligent snow depth monitoring model processes the scene outdoor scene images, which belongs to a black box processing mode, namely, the network operation of deep learning is carried out, and the specific processing mode is determined by the deep learning network without manual intervention.
Fig. 3 is a schematic diagram of a training process of an artificial intelligent snow depth monitoring model of a processor (4) in the snow depth monitoring system according to the embodiment of the present invention, the model is trained by using a VGG16 network in the algorithm, and the VGG16 network is a deep learning network formed by adding 3 full connection layers to 13 convolution layers, so that the accuracy is high in the application of image classification, and the algorithm is simple and convenient, so that whether snow falls is determined by using the deep learning network. The method comprises the following processing steps:
(1) firstly, collecting field outdoor scene images in a plurality of monitoring scenes, manually judging and marking, and dividing the field outdoor scene images into snow scene images and non-snow scene images according to whether snow exists in the field outdoor scene images;
(2) because the training of deep learning needs the support of a large number of data sets, the snow scene picture is subjected to edge cutting and horizontal turning preprocessing operations, so that the effect of adding the data sets is achieved, and overfitting of the model is avoided;
(3) designing a structure of a deep learning VGG16 network, finely adjusting full-connection layer output according to the categories of snow-scene pictures and non-snow-scene pictures which need to be distinguished, and defining a cross entropy loss function, an Adam optimizer and the like;
(4) randomly distributing the collected scene external scene images, and according to a training set: the test set was 8: 2, dividing the pictures according to the proportion, training the neural network by using a training set to adjust proper iteration times, and primarily checking a classification result by using a test set picture;
(5) in order to enable the performance of the artificial intelligent snow depth monitoring model to reach the highest, the learning rate is adjusted, in order to avoid gradient explosion, the range is reduced in one direction, parameters such as batch size and the like are adjusted, and the model is retrained and tested according to the accuracy of the obtained image classification with or without snowfall;
(6) indexes such as accuracy, loss functions, ROC curves, PR curves and the like in the training process of the snowfall-free image classification are drawn, and therefore the model performance is further evaluated.
Through the steps, the artificial intelligence snow depth monitoring model is trained, and can be used for judging whether snow falls in actual field pictures, for example, whether snow falls in the pictures shown in fig. 4 and 5 can be accurately judged. The judgment is automatically carried out by the processor (4) without human intervention. Fig. 4 is a photograph of snowfall collected on site, and fig. 5 is a photograph of snowfall without snow collected on site, according to an embodiment of the present invention.
In summary, the snow depth monitoring system of the embodiment of the invention adopts the deep learning artificial intelligence algorithm to accurately judge whether snow falls in the monitoring scene, so that the influence of interference factors on the snow depth monitoring can be reduced, the snow depth monitoring can be more accurately performed, and the wind blowing disaster can be more accurately monitored.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. A snow depth monitoring system based on artificial intelligence algorithm, comprising: the snow depth sensor comprises a controller, a snow depth sensor, a camera and a processor, wherein the controller is respectively in circuit connection with the snow depth sensor, the camera and the processor;
the snow depth sensor is used for acquiring snow depth data values in a monitoring scene according to a set time interval and transmitting the acquired snow depth data values to the controller;
the controller is used for receiving the snow depth data value transmitted by the snow depth sensor, judging whether the snow depth data value is larger than a set snowing threshold value or not, if so, sending an acquisition signal to the camera by the controller, and forwarding a field outdoor scene image returned by the camera to the processor; otherwise, the controller does not send a collecting signal to the camera;
the camera is used for collecting the scene external scene image of the monitored scene after receiving the collecting signal sent by the controller and transmitting the collected scene external scene image to the controller;
and the processor is used for calculating the field outdoor scene image by using an artificial intelligent snow depth monitoring model after receiving the field outdoor scene image transmitted by the controller to obtain a snow depth value of the monitored scene, and judging whether the monitored scene snows according to the snow depth value.
2. The artificial intelligence algorithm-based snow depth monitoring system of claim 1, wherein the processor is specifically configured to record a current snow depth value if it is determined from the snow depth value that the monitored scene is snowfall; and if the monitored scene is judged not to be snowfall, updating the initial value of the snow depth without recording.
3. The artificial intelligence algorithm-based snow depth monitoring system according to claim 1, wherein the processor is specifically configured to train an artificial intelligence snow depth monitoring model using a deep learning VGG16 network, and the training process includes the following processing steps:
(1) collecting field outdoor scene images in a plurality of monitoring scenes, judging and labeling the field outdoor scene images, and dividing the field outdoor scene images into snow scene images and non-snow scene images;
(2) performing edge cutting and horizontal turning pretreatment on the snow scene picture;
(3) designing a structure of a deep learning VGG16 network, finely adjusting full-connection layer output according to the categories of snow-scene pictures and non-snow-scene pictures which need to be distinguished, and defining a cross entropy loss function, an Adam optimizer and the like;
(4) randomly distributing the collected scene external scene images, and according to a training set: the test set was 8: 2, dividing pictures according to the proportion, training the deep learning VGG16 network by using a training set to adjust proper iteration times, and primarily checking classification results by using a test set picture;
(5) adjusting the learning rate, and retraining and testing the artificial intelligent snow depth monitoring model according to the accuracy of the obtained picture classification of whether snow falls or not;
(6) drawing the indexes of accuracy, loss function, ROC curve and PR curve in the training process of the snowfall-free image classification, and further evaluating the performance of the artificial intelligent snow depth monitoring model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114202736A (en) * | 2021-11-01 | 2022-03-18 | 中山大学 | Accumulated snow parameter acquisition method and device based on delayed photography and terminal equipment |
| CN114202736B (en) * | 2021-11-01 | 2022-12-02 | 中山大学 | Accumulated snow parameter acquisition method and device based on delayed photography and terminal equipment |
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