CN117809244A - Dynamic rainy day river water level monitoring method and device based on video monitoring network - Google Patents
Dynamic rainy day river water level monitoring method and device based on video monitoring network Download PDFInfo
- Publication number
- CN117809244A CN117809244A CN202311826041.1A CN202311826041A CN117809244A CN 117809244 A CN117809244 A CN 117809244A CN 202311826041 A CN202311826041 A CN 202311826041A CN 117809244 A CN117809244 A CN 117809244A
- Authority
- CN
- China
- Prior art keywords
- image
- water level
- river channel
- coordinates
- key frame
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 201
- 238000012544 monitoring process Methods 0.000 title claims abstract description 144
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000013507 mapping Methods 0.000 claims abstract description 30
- 238000005070 sampling Methods 0.000 claims abstract description 26
- 238000006243 chemical reaction Methods 0.000 claims abstract description 6
- 238000004590 computer program Methods 0.000 claims description 14
- 238000012806 monitoring device Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 abstract description 20
- 230000008859 change Effects 0.000 abstract description 6
- 238000005215 recombination Methods 0.000 abstract description 6
- 230000006798 recombination Effects 0.000 abstract description 6
- 238000010183 spectrum analysis Methods 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract 1
- 238000003062 neural network model Methods 0.000 description 7
- 238000005259 measurement Methods 0.000 description 6
- 238000000605 extraction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 235000004522 Pentaglottis sempervirens Nutrition 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Landscapes
- Closed-Circuit Television Systems (AREA)
Abstract
The application relates to a rainy day river channel water level dynamic monitoring method and device based on a video monitoring network, wherein the method comprises the steps of arranging the video monitoring network covering two sides of a river channel; based on the panoramic video image, establishing a mapping model of the mutual conversion of the pixel point coordinates of the river channel two-dimensional panoramic image and the three-dimensional geographic space coordinates; extracting a plurality of single-frame images from panoramic continuous video images on two sides of a river channel during rainfall by using a video monitoring network as key frames, and removing rain drops on the basis of spectral analysis and pixel recombination; binarization is carried out based on the gray level mean value of the sampling point, and then the binarization is input into a preset water line identification model, so that water line image coordinates corresponding to the key frames are obtained; inputting the image coordinates of the water level lines into a mapping model to obtain geographic space coordinates corresponding to the water level lines; and combining the geospatial coordinates of the water lines at different moments to obtain the water level change of the two sides of the river channel. The method and the device can realize the effect of continuously and completely monitoring the river channel water level in the rainfall process with low cost and improving the monitoring precision.
Description
Technical Field
The application relates to the technical field of hydrologic monitoring, in particular to a rainy day river channel water level dynamic monitoring method and device based on a video monitoring network.
Background
With frequent extreme weather, urban flood control has higher and higher requirements on hydrologic monitoring technology, and particularly, a large number of community buildings are distributed on two sides of a river channel, so that the urban river channel is required to receive not only surface confluence but also drainage rainwater of complex drainage systems of all levels, and the water level fluctuation has uncertainty. Meanwhile, when the river water level overflows during the occurrence of storm, economic property loss is extremely easy to cause to residents on two sides, and even serious life and health are threatened. Therefore, the river water level is monitored in the rainfall process, particularly in the heavy rain, the dynamic change of the water level is mastered, and proper preventive measures are timely taken in combination with the rainfall forecast result, so that the method has important emergency management significance. At present, the technology of hydrologic monitoring after rainfall is mature, and in the rainfall process, the monitoring is quite difficult due to the burstiness of rainfall, especially short-duration heavy rainfall, and the complexity and the danger of the environment.
The existing main mode for monitoring the river channel water level in the rainfall process comprises the following steps:
1. Through manual actual measurement, the actual measurement is performed near a river channel in rainy days, so that the river channel has certain danger and poor data continuity;
2. the data can be continuously acquired through the measurement of the sensor, but the data can be acquired only by arranging the sensor at fixed point positions such as under-bridge, gate and the like, so that the coverage range of the data is limited, and the complete river water level information can not be acquired;
3. the technology can acquire complete river channel information by remote sensing, but has high technical cost, is affected by rain fall, has low image definition and insufficient monitoring precision.
Disclosure of Invention
In order to continuously and completely monitor the river channel water level in the rainfall process at low cost and improve the monitoring precision, the application provides a rainy day river channel water level dynamic monitoring method and device based on a video monitoring network.
In a first aspect, the application provides a rainy day river water level dynamic monitoring method based on a video monitoring network.
The application is realized by the following technical scheme:
a rainy day river water level dynamic monitoring method based on a video monitoring network, which comprises the following steps,
laying video monitoring networks covering two sides of a river channel;
acquiring panoramic video images of the two sides of the river channel at a low tide level in sunny days by using the video monitoring network;
Based on the panoramic video image and in combination with a geospatial coordinate system, a mapping model for interconversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geospatial coordinates is established;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset first time interval when rainfall is less than or equal to a preset rainfall threshold value by using the video monitoring network to obtain a first key frame sequence;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is greater than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
inputting the first key frame sequence or the second key frame sequence into a preset water line identification model to obtain water line image coordinates corresponding to key frames;
inputting the water line image coordinates into the mapping model to obtain geographical space coordinates of the water line;
and combining the geospatial coordinates of the water lines at different moments to obtain the water lines of the continuous time sequences of the two sides of the river channel.
The present application may be further configured in a preferred example to: the step of obtaining a second key frame sequence by utilizing the video monitoring network to obtain a plurality of single frame images extracted from panoramic continuous video images of the two sides of the river channel at a preset second time interval when the rainfall is larger than a preset rainfall threshold value,
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at the second time interval when rainfall is greater than the rainfall threshold value during rainfall, wherein the second time interval is smaller than the first time interval, the plurality of single-frame images are divided into a plurality of image groups according to time sequence, each image group comprises n Shan Zhen images, and the condition that int (n/2) T < T is satisfied, wherein T is the first time interval and T is the second time interval;
calculating the brightness and saturation of each pixel point on each single frame image;
ordering the pixel points on the same image coordinate of each single frame image in the image group according to the order of the brightness from high to low, and taking the first m pixel points as a target key frame pixel sequence set, wherein m is less than n;
selecting a pixel point corresponding to the minimum saturation as a pixel point of an image group key frame based on the target key frame pixel sequence set, and associating RGB values corresponding to the pixel point of the image group key frame;
And combining RGB values corresponding to pixel points on all image coordinates of the image group key frames to obtain the image group key frames, and combining the image group key frames of all image groups to obtain the second key frame sequence.
The present application may be further configured in a preferred example to: the step of calculating the brightness and saturation of each pixel point on each of the single frame images includes,
obtaining RGB values of each pixel point on the single frame image to obtain RGB values (R) of pixel points with the image coordinates (x, y) of the ith Shan Zhen image ixy ，G ixy ，B ixy )；
Calculating the brightness B of the pixel point based on the RGB value of the pixel point i Comprising the steps of, in combination,
B i ＝((R ixy *299)+(G ixy *587)+(B ixy *114))/1000；
calculating the saturation S of the pixel point based on the RGB value of the pixel point i Comprising the steps of, in combination,
S i ＝(V max -V min )/V max ；
V max ＝max(R ixy ，G ixy ，B ixy )；
V min ＝min(R ixy ，G ixy ，B ixy )。
the present application may be further configured in a preferred example to: the step of arranging the video monitoring network covering the two sides of the river channel comprises the steps of,
according to the width and length of the river bank, arranging a high-definition camera which rotates up and down and left and right;
presetting a resolution threshold of the high-definition camera, taking a monitoring picture with resolution exceeding the resolution threshold in the high-definition camera as an effective shooting area, and connecting the effective shooting areas to form a complete shooting area until the complete shooting area covers two sides of the river channel.
The present application may be further configured in a preferred example to: the method also comprises the following steps of,
if the complete shot region cannot cover the two sides of the river channel, judging whether the width of the river channel exceeds a preset width threshold value;
and when the width of the river exceeds the width threshold, adjusting the high-definition cameras until the high-definition cameras are distributed in opposite directions on two sides.
The present application may be further configured in a preferred example to: the method also comprises the following steps of,
and when the width of the river channel is smaller than or equal to the width threshold value, adjusting the high-definition cameras until the high-definition cameras are distributed at intervals on two sides.
The present application may be further configured in a preferred example to: after the step of obtaining the first key frame sequence or the step of obtaining the second key frame sequence, the method further comprises,
marking a water area range in the panoramic video image;
recording image coordinates of a plurality of sampling points positioned at the edge of the water area according to the water area range to obtain a sampling point coordinate set;
performing binarization processing on the first key frame sequence or the second key frame sequence by adopting a binarization rule, wherein the binarization rule comprises,
where g (x, y) is the gray value of the single frame image at coordinates (x, y), g b And (x, y) is the gray value of the binarized single-frame image at the coordinates (x, y), L is a gray threshold value, and the gray threshold value is obtained by solving the average number of the gray values of each image pixel in the coordinate set of the corresponding sampling point of the first key frame sequence or the second key frame sequence.
In a second aspect, the application provides a dynamic rainy day river water level monitoring device based on a video monitoring network.
The application is realized by the following technical scheme:
a rainy day river water level dynamic monitoring device based on a video monitoring network, which comprises,
the video monitoring network module is used for arranging video monitoring networks covering two sides of the river channel;
the basic data module is used for acquiring panoramic video images of the two sides of the river channel at low tide level in sunny days by utilizing the video monitoring network;
the mapping model module is used for establishing a mapping model for the mutual conversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geographic space coordinates based on the panoramic video image and in combination with a geographic space coordinate system;
the small-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two sides of a river channel at a preset first time interval when rainfall is smaller than or equal to a preset rainfall threshold value by utilizing the video monitoring network to obtain a first key frame sequence;
The large-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is larger than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
the water level image identification module is used for inputting the first key frame sequence or the second key frame sequence into a preset water level line identification model to obtain water level image coordinates corresponding to the key frames;
the water level line space coordinate module is used for inputting the water level line image coordinates into the mapping model to obtain geographical space coordinates of the water level line;
and the river channel full-time-sequence water level information module is used for combining the geospatial coordinates of the water level lines at different moments to obtain the water level lines of the continuous time sequence at the two sides of the river channel.
In a third aspect, the present application provides a computer device.
The application is realized by the following technical scheme:
the computer equipment comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of any one of the rainy day river water level dynamic monitoring method based on the video monitoring network when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium.
The application is realized by the following technical scheme:
a computer readable storage medium storing a computer program which when executed by a processor implements any one of the steps of the rainy day river level dynamic monitoring method based on a video monitoring network.
To sum up, compared with the prior art, the beneficial effects brought by the technical scheme provided by the application at least include:
the video monitoring network covering the two sides of the river channel is arranged to replace the traditional sensor and manual actual measurement mode, so that more comprehensive and sustainable river channel water level data information can be obtained, and the acquired data are more abundant; establishing a mapping model for interconversion of pixel point coordinates of a two-dimensional image and three-dimensional geospatial coordinates, further acquiring geospatial position coordinates corresponding to the two-dimensional coordinates in a photographed water level image, acquiring three-dimensional position information of a water level without setting a remote sensing mode, and reducing implementation cost; a video monitoring network is utilized to obtain a plurality of single-frame images extracted from panoramic continuous video images of two sides of a river channel in small rainfall and in large rainfall, so that more local features are extracted aiming at a scene with large rainfall to reduce the influence of unclear water level images; inputting the extracted single-frame image into a preset water level line identification model to obtain water level line image coordinates corresponding to the key frames, and inputting the water level line image coordinates into a mapping model to obtain geographical space coordinates of the water level lines; the geospatial coordinates of the water level lines at different moments are combined to obtain the water level lines of the continuous time sequences of the two sides of the river course, so that the continuous and complete monitoring of the water level of the river course in the rainfall process is realized at low cost, and the water level monitoring precision in the rainfall process is improved.
Drawings
Fig. 1A is a schematic diagram of two-bank opposite camera layout of a rainy day river level dynamic monitoring method based on a video monitoring network according to another exemplary embodiment of the present application.
Fig. 1B is a schematic diagram of two-bank spaced cameras layout of a rainy day river level dynamic monitoring method based on a video monitoring network according to another exemplary embodiment of the present application.
Fig. 2 is a schematic diagram of setting sampling points at the edge of a water area of a rainy day river channel water level dynamic monitoring method based on a video monitoring network according to an exemplary embodiment of the present application.
Fig. 3A is a keyframe sequence gray scale chart of a rainy day river level dynamic monitoring method based on a video monitoring network according to an exemplary embodiment of the present application.
Fig. 3B is a keyframe sequence binarization chart of a rainy day river level dynamic monitoring method based on a video monitoring network according to an exemplary embodiment of the present application.
Fig. 4 is a main flow chart of a rainy day river level dynamic monitoring method based on a video monitoring network according to an exemplary embodiment of the present application.
Detailed Description
The present embodiment is merely illustrative of the present application and is not intended to be limiting, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as required, but is protected by patent laws within the scope of the claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
The application provides a rainy day river channel water level dynamic monitoring method based on a video monitoring network, which realizes one-to-one correspondence between monitoring images and space positions according to a geographic space mutual mapping relation by arranging a high-definition video monitoring network; then collecting continuous images at fixed time intervals in a rainfall day, constructing a key frame through pixel recombination, binarizing the key frame, and extracting a water line by adopting a neural network model; and acquiring the space information of the water level line, and combining all monitoring moments to obtain the full-time water level information of the whole river channel. The full-time-sequence water level information refers to continuous-time-sequence river water level information. The water level line refers to the boundary line between the water body and the river bank. Compared with the traditional method, the method not only reduces the danger of manual rainy day operation, but also can obtain the water level information with high precision, full river course and full time sequence in the rainfall process at low cost, thereby providing technical support for preventing and controlling urban flood disasters.
The embodiment of the application provides a rainy day river channel water level dynamic monitoring method based on a video monitoring network, and the main steps of the method are described as follows.
Laying video monitoring networks covering two sides of a river channel;
acquiring panoramic video images of the two sides of the river channel at a low tide level in sunny days by using the video monitoring network;
based on the panoramic video image and in combination with a geospatial coordinate system, a mapping model for interconversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geospatial coordinates is established;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset first time interval when rainfall is less than or equal to a preset rainfall threshold value by using the video monitoring network to obtain a first key frame sequence;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is greater than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
inputting the first key frame sequence or the second key frame sequence into a preset water line identification model to obtain water line image coordinates corresponding to key frames;
Inputting the water line image coordinates into the mapping model to obtain geographical space coordinates of the water line;
and combining the geospatial coordinates of the water lines at different moments to obtain the water lines of the continuous time sequences of the two sides of the river channel.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
Specifically, high-definition cameras are uniformly distributed along two sides of a river channel, and unique numbers are allocated to each camera so as to comprehensively monitor real-time water level information of the two sides of the river channel, and a video monitoring network covering the two sides of the river channel is built. According to the method, under a clear environment, the camera is adjusted and rotated, the optimal focal length is recorded and used for initializing the camera, and the initialized camera is used for collecting high-definition images of the river bank at low tide level under different angles. The high-definition images of the river banks with different angles at low tide level can be obtained by shooting after the shooting angles of the cameras are fixed in advance by a manager, and the frames of the cameras with the fixed angles are spliced to obtain panoramic video images.
Based on the panoramic video image, through a unified geospatial coordinate system, one-to-one mapping of two-dimensional image pixel coordinates (x, y) of the panoramic video image to geographical space coordinates (x ', y ', z ') of actual sampling points of the river channel is determined, and a mapping model of the two-dimensional image pixel coordinates of the panoramic video image and geographical space coordinate information is established for interconversion of the two-dimensional image pixel coordinates of the panoramic two-dimensional image on two sides of the river channel and the three-dimensional geographical space coordinates.
Specifically, a coordinate system is established by taking the center of a panoramic video image as an origin and the size of a pixel as a unit length, oblique photogrammetry is carried out through an unmanned aerial vehicle, so that three-dimensional coordinates of the panoramic video image on the ground surface are obtained, a real-scene three-dimensional model of a target area is established, ground measurement of a control point is carried out through the unmanned aerial vehicle, the result of the oblique photogrammetry is corrected, and the photogrammetry precision is improved; and obtaining a bird's eye view map with coordinate data, which is consistent with the view angle of the panoramic video image, through rotation of the live-action three-dimensional model, and establishing a coordinate conversion equation according to the bird's eye view map coordinate system and the panoramic video image coordinate system to realize the mapping of the two-dimensional image coordinates and the three-dimensional geographic space coordinates.
After the model is established, the mutual conversion of the three-dimensional geospatial coordinates of the two-dimensional image pixel points and the river channel actual sampling points can be realized by inputting any image coordinate of the panoramic two-dimensional image into the mapping model.
And then, acquiring a plurality of single-frame images extracted from panoramic continuous video images of the two sides of the river channel at a preset first time interval when rainfall is less than or equal to a preset rainfall threshold by utilizing a video monitoring network, so as to obtain a first key frame sequence. The rainfall threshold is the water quantity maximum value obtained by statistics according to historical rainfall data on two sides of a river channel. The panoramic continuous video image is a continuous video image of two sides of a river channel at a preset shooting angle in the same period in the rainfall process. The periodic setting may be the time required for the camera to traverse all preset shooting angles. And acquiring a plurality of single-frame images extracted from a preset shooting angle of each camera in the same period in the rainfall process, and obtaining a first key frame sequence.
When the rainfall on two sides of the river channel is smaller than or equal to the rainfall threshold, namely the current rainfall is smaller, directly extracting single-frame images with a fixed time interval T from continuous video images to serve as key frames, and collecting n Shan Zhen extracted images according to time sequence to obtain a first key frame sequence.
And when the rainfall is large, acquiring a plurality of single-frame images extracted from panoramic continuous video images of the two sides of the river channel at a second time interval when the rainfall is larger than a preset rainfall threshold value by utilizing a video monitoring network, so as to obtain a second key frame sequence. The rainfall threshold may be a water minimum value when the strong rainfall is counted according to historical rainfall data on both sides of the river channel. The minimum amount of water is allowed to fluctuate within a preset value range. The panoramic continuous video image is a continuous video image of two sides of a river channel at a preset shooting angle in the same period in the rainfall process.
When the rainfall on two sides of the river channel is larger than the rainfall threshold, namely the current rainfall is larger, single-frame images with a fixed time interval t are extracted from continuous video images to serve as key frames, and the extracted n Shan Zhen images are collected in time sequence to obtain a second key frame sequence.
In order to identify and extract a water line from an image, the method uses an extracted single-frame image as a water line identification sample, adopts a pre-trained neural network model to conduct water line identification, acquires water level images of two sides of a massive river channel and marks the water line as a training set, inputs the water level images into the yolov5 model to conduct iterative training, outputs continuous two-dimensional water line coordinates of the image, and takes the yolov5 model which is currently completed to train as a trained neural network model until the output precision meets a preset precision threshold value to obtain a water line identification model.
And inputting the first key frame sequence or the second key frame sequence into the water level line identification model, and outputting water level line image coordinates corresponding to the key frames.
And inputting the image coordinates of the water level line into a mapping model, and converting to obtain the geospatial coordinates of the water level line.
And combining the geospatial coordinates of the water lines at different moments, and establishing complete full-time-sequence water level information in continuous monitoring time to obtain the water lines at the continuous time sequences of the two sides of the river channel.
Finally, continuous water level information exists on both sides of the river, and the change of the target river water level along with time can be reflected through monitoring data of different periods, so that the purpose of comprehensively, completely and highly accurately monitoring the river water level in the rainfall process is realized.
In one embodiment, in order to enable the video monitoring network to capture high-definition monitoring video of the water level of the two sides of the river, the step of arranging the video monitoring network covering the two sides of the river includes,
according to the width and length of the river bank, arranging a high-definition camera which rotates up and down and left and right;
presetting a resolution threshold of the high-definition camera, taking a monitoring picture with resolution exceeding the resolution threshold in the high-definition camera as an effective shooting area, and connecting the effective shooting areas to form a complete shooting area until the complete shooting area covers two sides of the river channel.
Specifically, the number of the high-definition cameras can be uniformly distributed according to the width and the length of the river bank, after the cameras are fixedly installed, monitoring pictures with the resolution of the shooting pictures exceeding a preset resolution threshold value in the high-definition cameras are taken as effective shooting areas, namely, areas with the resolution exceeding a certain threshold value in the monitoring pictures under a certain angle of the cameras are called effective shooting areas, and the effective shooting areas are combined according to shooting time sequence to obtain complete shooting areas covering the two sides of the river channel, namely, the maximum area formed by connecting the effective shooting areas when each shooting is rotated, so that the water level monitoring video of the two sides of the river channel with high definition is obtained.
If the intercepted effective shooting area of the high-definition camera cannot cover two banks of the river channel after combination, the resolution threshold of the high-definition camera is manually adjusted according to the missing condition of the image until the complete shooting area obtained by combination can cover the two banks of the river channel. In this embodiment, the resolution threshold may be 0.01m.
Referring to fig. 1A, in an embodiment, in order to solve the problem that the effective camera area of the camera on one side of the river bank cannot be related to the opposite river bank when the river bank is wider, the step of laying the video monitoring network covering both sides of the river bank further includes the following steps,
if the complete shot region cannot cover the two sides of the river channel, judging whether the width of the river channel exceeds a preset width threshold value;
And when the width of the river exceeds the width threshold, adjusting the high-definition cameras until the high-definition cameras are distributed in opposite directions on two sides.
Whether the width of the river exceeds a preset width threshold value is judged, so that the problem that whether the effective shooting area of a camera on one side of the river bank cannot be related to the opposite-bank river and the water level monitoring video of the two sides of the river is unclear is solved. The width threshold is designed by related professionals according to the shooting range of the high-definition camera. When the width of the river exceeds the width threshold, the high-definition cameras are oppositely distributed along the two banks at preset interval distance intervals, so that an effective shooting area of the cameras on one side of the river bank can be related to the river channel on the opposite bank.
In one embodiment, in order to reduce the hardware expenditure cost on the premise of acquiring the high-definition river channel two-bank water level monitoring video, the step of arranging the video monitoring network covering the two sides of the river channel further comprises the following steps,
and when the width of the river channel is smaller than or equal to the width threshold value, adjusting the high-definition cameras until the high-definition cameras are distributed at intervals on two sides.
As shown in fig. 1B, when the width of the river is smaller than or equal to the width threshold, that is, the river at this time belongs to a narrower situation, the image of the opposite bank can be clearly shot only by the camera at one side of the river bank, so that the high-definition cameras are distributed along the two banks at the intersection interval of the preset interval distance.
In one embodiment, when the rainfall is large, the view of the lens of the camera is blurred by rainwater, and in order to obtain a clearer monitoring image in a scene with large rainfall, the scheme provides a method for extracting a key frame through spectral analysis and pixel recombination, so as to remove the rainwater from the monitoring image, which comprises the following steps of,
when a plurality of single-frame images which are extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is greater than a preset rainfall threshold are acquired, the second time interval is smaller than a first time interval which is adopted when single-frame images are extracted when rainfall is smaller, namely T < T, so that more local water level image information is acquired, the plurality of single-frame images are divided into a plurality of image groups according to time sequence, each image group comprises n Shan Zhen images, the time interval between the shooting time of each image group (n/2) +1 single-frame image and the time interval between the shooting time of the previous image (Shan Zhen) is equal to the first time interval T, and continuity of the water level information of the extracted single-frame images is ensured, namely, the time interval of each image is equal to the time interval T, wherein T is the first time interval, and T is the second time interval.
Calculating the brightness and saturation of each pixel on each single frame image, in particular, by reading R, G, B three-channel values of each pixel on a single frame image because raindrops or rain lines have the characteristics of high brightness and low saturation in the image, assuming that the (i) th image, the RGB values at the image coordinates (x, y) of the pixels are respectively (R ixy ，G ixy ，B ixy ) Brightness B i The calculation formula is as follows:
B i ＝((R ixy *299)+(G ixy *587)+(B ixy *114))/1000；
saturation S at the corresponding position i The calculation formula is as follows:
S i ＝(V max -V min )/V max ；
V max ＝max(R ixy ，G ixy ，B ixy )；
V min ＝min(R ixy ，G ixy ，B ixy )；
repeating the above steps until the brightness and saturation of each pixel point on each Shan Zhen image are obtained (B i ，S i )。
Ordering the pixel points on the same image coordinate of each Shan Zhen image in the image group according to the order of the brightness from high to low, and taking the first m pixel points as a target key frame pixel sequence set, wherein m is less than n;
selecting a pixel point corresponding to the minimum saturation as a pixel point of an image group key frame based on the target key frame pixel sequence set, and associating RGB values corresponding to the pixel point of the image group key frame;
and so on, obtaining RGB values corresponding to pixel points of the image group key frame;
and combining RGB values corresponding to pixel points on all image coordinates of the image group key frames to obtain the image group key frames, and combining the image group key frames of all image groups to obtain the second key frame sequence, wherein the shooting time of the second key frame sequence is the average shooting time of the corresponding image group.
Through carrying out rain removal processing on the collected water level image by means of spectral analysis and pixel recombination when the rainfall is large, the obtained water level image is clearer, and the accuracy of the subsequent water level line extraction is improved.
And because the geographic scene shot by the camera is basically fixed, the variable in the monitoring process is mainly dynamic water level information, the water level change of two sides of a river channel is difficult to monitor in rainy days, the water level mark is a reference for monitoring the water level change, in order to avoid the inconvenience and the safety hazard of manual water level mark in the raining process, the automatic pixel optimization is realized, sampling points can be acquired in advance, and referring to FIG. 2, the sampling points are k sampling points which are artificially selected and are uniformly distributed near the edge of the water area, and k is more than or equal to 6. In this embodiment, the number of sampling points may be 6.
In particular, in one embodiment, after obtaining the first key frame sequence or the second key frame sequence, further comprising,
marking a water area range in the panoramic video image;
recording image coordinates of a plurality of sampling points positioned at the edge of the water area according to the water area range to obtain a sampling point coordinate set;
performing binarization processing on the first key frame sequence or the second key frame sequence by adopting a binarization rule, wherein the binarization rule comprises,
where g (x, y) is the gray value of the single frame image at coordinates (x, y), g b (x, y) is the gray value of the binarized single frame image at the coordinates (x, y), L is a gray threshold value, and the gray threshold value is obtained by solving the average number of the gray values of the pixels of each image in the coordinate set of the corresponding sampling points of the first key frame sequence or the second key frame sequence To (d).
When the water level monitoring is carried out in the raining process, the same sampling point coordinate set is adopted to carry out binarization processing on the pixel points on the first key frame sequence or the second key frame sequence, so that the problem that the neural network model is insufficient in recognition precision when the water level line is extracted due to the fact that the acquired water level image is easily influenced by water color, light brightness and the like to cause image color difference in the raining process is solved, and the recognition precision of the model is further improved.
According to the scheme, prior information of sample images is adopted for analysis, sample images with the same camera numbers and the same rotation angles corresponding to key frames are selected, k fixed sampling points preset on the sample images are combined, image coordinates corresponding to the k sampling points are matched on a key frame sequence, pixel gray values corresponding to the k image coordinates are recorded, finally the average number of the k gray values is solved to serve as a gray threshold of the key frame sequence, a binarization rule is utilized to convert the key frame sequence images into a key frame sequence gray map (shown in fig. 3A), and binarization is carried out by combining the gray threshold to obtain a binarized key frame sequence (shown in fig. 3B).
In this embodiment, the scheme does not directly adopt the original keyframe as the input data of the neural network model, but adopts the image binarized by the keyframe as the input data of the neural network model, and outputs the coordinates of the water level line image in the keyframe sequence, so as to separate the water body of the river channel from the river bank or other fixed scenes, and further, the detail characteristics of the water level image can be highlighted, thereby eliminating the influence of dynamic environmental factors on the water level image recognition, and further, the water level line can be better recognized.
In one embodiment, the extraction water line can be identified by using pixel clustering and smoothing algorithm, and the method further comprises the following steps,
carrying out pixel clustering on the binarized key frame image, and aggregating pixels with gray scale of 0 into a pixel cluster;
judging the river bank azimuth in the key frame image, and taking pixels at the boundary of a pixel cluster of the river bank azimuth as initial water line pixels;
and smoothing by adopting a moving window least square polynomial smoothing algorithm based on the image coordinates of the initial water line pixels to obtain water line image coordinates.
Referring to fig. 4, in one embodiment, a method for dynamically monitoring a rainy day river water level based on a video monitoring network includes,
constructing a high-definition video monitoring network covering two banks of a river channel;
performing mutual mapping of multi-view fixed scene water level images and geospatial coordinate information;
performing key frame extraction through spectrum analysis and pixel recombination;
based on a gray average value of a preset sampling point as a gray threshold value, carrying out binarization on a key frame sequence;
detecting a binarized key frame sequence by adopting a preset convolutional neural network model, and extracting a water line;
and combining the multi-time water level lines converted into space coordinates to construct the full-time change of the river water level.
In summary, according to the rainy day river water level dynamic monitoring method based on the video monitoring network, the video monitoring network covering the two sides of the river is arranged to replace the traditional sensor and manual actual measurement mode, so that more comprehensive and sustainable river water level data information can be obtained, and the acquired data are more abundant; establishing a mapping model for interconversion of pixel point coordinates of a two-dimensional image and three-dimensional geospatial coordinates, further acquiring geospatial position coordinates corresponding to the two-dimensional coordinates in a photographed water level image, acquiring three-dimensional position information of a water level without setting a remote sensing mode, and reducing implementation cost; a video monitoring network is utilized to obtain a plurality of single-frame images extracted from panoramic continuous video images of two sides of a river channel in small rainfall and in large rainfall, so that more local features are extracted aiming at a scene with large rainfall to reduce the influence of unclear water level images; inputting the extracted single-frame image into a preset water level line identification model to obtain water level line image coordinates corresponding to the key frames, and inputting the water level line image coordinates into a mapping model to obtain geographical space coordinates of the water level lines; the geospatial coordinates of the water level lines at different moments are combined to obtain the water level lines of the continuous time sequences of the two sides of the river course, so that the continuous and complete monitoring of the water level of the river course in the rainfall process is realized at low cost, and the water level monitoring precision in the rainfall process is improved.
Furthermore, by providing a method for extracting key frames through spectral analysis and pixel recombination, the water level image in large rainfall is subjected to rain removal treatment, so that the influence of raindrops and rain lines on the definition of the water level image in a storm environment is effectively improved, and a clearer and real water level image of two sides of a storm heaven river is obtained.
Further, after a plurality of single-frame images extracted from panoramic continuous video images on two sides of a river channel in small rainfall and in large rainfall are obtained, key frame images are subjected to binarization processing according to a coordinate set of pre-sampling points on the panoramic video images in sunny days and then are input into a neural network model for water level line extraction, so that water body features in the key frames are more outstanding, the model can better recognize and extract water level lines, and model generalization capability is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
The embodiment of the application also provides a rainy day river water level dynamic monitoring device based on the video monitoring network, which corresponds to the rainy day river water level dynamic monitoring method based on the video monitoring network in the embodiment. The rainy day river water level dynamic monitoring device based on the video monitoring network comprises,
The video monitoring network module is used for arranging video monitoring networks covering two sides of the river channel;
the basic data module is used for acquiring panoramic video images of the two sides of the river channel at low tide level in sunny days by utilizing the video monitoring network;
the mapping model module is used for establishing a mapping model for the mutual conversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geographic space coordinates based on the panoramic video image and in combination with a geographic space coordinate system;
the small-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two sides of a river channel at a preset first time interval when rainfall is smaller than or equal to a preset rainfall threshold value by utilizing the video monitoring network to obtain a first key frame sequence;
the large-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is larger than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
the water level image identification module is used for inputting the first key frame sequence or the second key frame sequence into a preset water level line identification model to obtain water level image coordinates corresponding to the key frames;
The water level line space coordinate module is used for inputting the water level line image coordinates into the mapping model to obtain geographical space coordinates of the water level line;
and the river channel full-time-sequence water level information module is used for combining the geospatial coordinates of the water level lines at different moments to obtain the water level lines of the continuous time sequence at the two sides of the river channel.
The rainy day river water level dynamic monitoring device based on the video monitoring network also comprises,
the sampling point module is used for marking a water area range in the panoramic video image, and recording image coordinates of a plurality of sampling points positioned at the edge of the water area according to the water area range to obtain a sampling point coordinate set;
a binarization module for performing binarization processing on the first key frame sequence or the second key frame sequence by adopting a binarization rule, wherein the binarization rule comprises,
where g (x, y) is the gray value of the single frame image at coordinates (x, y), g b And (x, y) is the gray value of the binarized single-frame image at the coordinates (x, y), L is a gray threshold value, and the gray threshold value is obtained by solving the average number of the gray values of the pixels of each image in the coordinate set of the corresponding sampling points of the first key frame sequence or the second key frame sequence.
The specific limitation of the dynamic monitoring device for the rainy day river water level based on the video monitoring network can be referred to above for the limitation of the dynamic monitoring method for the rainy day river water level based on the video monitoring network, and will not be described herein.
The modules in the dynamic monitoring device for the river channel water level in the rainy day based on the video monitoring network can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, a memory, a network interface, and a database 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. 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 the processor to realize any one of the dynamic rainy day river water level monitoring methods based on the video monitoring network.
In one embodiment, a computer readable storage medium is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
laying video monitoring networks covering two sides of a river channel;
acquiring panoramic video images of the two sides of the river channel at a low tide level in sunny days by using the video monitoring network;
based on the panoramic video image and in combination with a geospatial coordinate system, a mapping model for interconversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geospatial coordinates is established;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset first time interval when rainfall is less than or equal to a preset rainfall threshold value by using the video monitoring network to obtain a first key frame sequence;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is greater than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
Inputting the first key frame sequence or the second key frame sequence into a preset water line identification model to obtain water line image coordinates corresponding to key frames;
inputting the water line image coordinates into the mapping model to obtain geographical space coordinates of the water line;
and combining the geospatial coordinates of the water lines at different moments to obtain the water lines of the continuous time sequences of the two sides of the river channel.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in the embodiments may be implemented by a computer program stored in a non-volatile computer readable storage medium, where the computer program includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments. The computer program, when executed, may comprise the flow of embodiments of the methods as described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system is divided into different functional units or modules to perform all or part of the above-described functions.
Claims (10)
1. A rainy day river channel water level dynamic monitoring method based on a video monitoring network is characterized by comprising the following steps,
laying video monitoring networks covering two sides of a river channel;
acquiring panoramic video images of the two sides of the river channel at a low tide level in sunny days by using the video monitoring network;
based on the panoramic video image and combining a geospatial coordinate system, establishing a mapping model for interconversion of pixel point coordinates of a panoramic two-dimensional image on two sides of a river channel and three-dimensional geospatial coordinates;
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset first time interval when rainfall is less than or equal to a preset rainfall threshold value by using the video monitoring network to obtain a first key frame sequence;
Acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is greater than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
inputting the first key frame sequence or the second key frame sequence into a preset water line identification model to obtain water line image coordinates corresponding to key frames;
inputting the water line image coordinates into the mapping model to obtain geographical space coordinates of the water line;
and combining the geospatial coordinates of the water lines at different moments to obtain the water lines of the continuous time sequences of the two sides of the river channel.
2. The method for dynamically monitoring the river channel water level in a rainy day based on a video surveillance network according to claim 1, wherein the step of obtaining a second key frame sequence by using the video surveillance network to obtain a plurality of single frame images extracted from panoramic continuous video images of both sides of the river channel at a preset second time interval when the rainfall is greater than a preset rainfall threshold value comprises,
acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at the second time interval when rainfall is greater than the rainfall threshold value during rainfall, wherein the second time interval is smaller than the first time interval, the plurality of single-frame images are divided into a plurality of image groups according to time sequence, each image group comprises n Shan Zhen images, and the condition that int (n/2) T < T is satisfied, wherein T is the first time interval and T is the second time interval;
Calculating the brightness and saturation of each pixel point on each single frame image;
ordering the pixel points on the same image coordinate of each single frame image in the image group according to the order of the brightness from high to low, and taking the first m pixel points as a target key frame pixel sequence set, wherein m is less than n;
selecting a pixel point corresponding to the minimum saturation as a pixel point of an image group key frame based on the target key frame pixel sequence set, and associating RGB values corresponding to the pixel point of the image group key frame;
and combining RGB values corresponding to pixel points on all image coordinates of the image group key frames to obtain the image group key frames, and combining the image group key frames of all image groups to obtain the second key frame sequence.
3. The method for dynamically monitoring the river channel water level in rainy days based on a video surveillance network according to claim 2, wherein the step of calculating the brightness and saturation of each pixel point on each single frame image comprises,
obtaining RGB values of each pixel point on the single frame image to obtain RGB values (R) of pixel points with the image coordinates (x, y) of the ith Shan Zhen image ixy ，G ixy ，B ixy )；
Calculating the brightness B of the pixel point based on the RGB value of the pixel point i Comprising the steps of, in combination,
B i ＝((R ixy *299)+(G ixy *587)+(B ixy *114))/1000；
calculating the saturation S of the pixel point based on the RGB value of the pixel point i Comprising the steps of, in combination,
S i ＝(V max -V min )/V max ；
V max ＝max(R ixy ，G ixy ，B ixy )；
V min ＝min(R ixy ，G ixy ，B ixy )。
4. the method for dynamically monitoring the water level of a river course in a rainy day based on a video monitoring network according to claim 1, wherein the step of arranging the video monitoring network covering both sides of the river course comprises,
according to the width and length of the river bank, arranging a high-definition camera which rotates up and down and left and right;
presetting a resolution threshold of the high-definition camera, taking a monitoring picture with resolution exceeding the resolution threshold in the high-definition camera as an effective shooting area, and connecting the effective shooting areas to form a complete shooting area until the complete shooting area covers two sides of the river channel.
5. The rainy day river water level dynamic monitoring method based on the video monitoring network of claim 4, further comprising the following steps,
if the complete shot region cannot cover the two sides of the river channel, judging whether the width of the river channel exceeds a preset width threshold value;
and when the width of the river exceeds the width threshold, adjusting the high-definition cameras until the high-definition cameras are distributed in opposite directions on two sides.
6. The method for dynamically monitoring the water level of a rainy day river based on a video monitoring network according to claim 5, further comprising the steps of,
And when the width of the river channel is smaller than or equal to the width threshold value, adjusting the high-definition cameras until the high-definition cameras are distributed at intervals on two sides.
7. The method for dynamically monitoring the river channel water level in rainy days based on a video surveillance network according to claim 1, wherein after the step of obtaining the first key frame sequence or the step of obtaining the second key frame sequence, further comprises,
marking a water area range in the panoramic video image;
recording image coordinates of a plurality of sampling points positioned at the edge of the water area according to the water area range to obtain a sampling point coordinate set;
performing binarization processing on the first key frame sequence or the second key frame sequence by adopting a binarization rule, wherein the binarization rule comprises,
where g (x, y) is the gray value of the single frame image at coordinates (x, y), g b And (x, y) is the gray value of the binarized single-frame image at the coordinates (x, y), L is a gray threshold value, and the gray threshold value is obtained by solving the average number of the gray values of the pixels of each image in the coordinate set of the corresponding sampling points of the first key frame sequence or the second key frame sequence.
8. A rainy day river water level dynamic monitoring device based on a video monitoring network is characterized by comprising,
The video monitoring network module is used for arranging video monitoring networks covering two sides of the river channel;
the basic data module is used for acquiring panoramic video images of the two sides of the river channel at low tide level in sunny days by utilizing the video monitoring network;
the mapping model module is used for establishing a mapping model for the mutual conversion of pixel point coordinates of the panoramic two-dimensional image on two sides of the river channel and three-dimensional geographic space coordinates based on the panoramic video image and in combination with a geographic space coordinate system;
the small-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two sides of a river channel at a preset first time interval when rainfall is smaller than or equal to a preset rainfall threshold value by utilizing the video monitoring network to obtain a first key frame sequence;
the large-rainfall key frame module is used for acquiring a plurality of single-frame images extracted from panoramic continuous video images of two banks of a river channel at a preset second time interval when rainfall is larger than a preset rainfall threshold value by utilizing the video monitoring network to obtain a second key frame sequence, wherein the second time interval is smaller than the first time interval;
the water level image identification module is used for inputting the first key frame sequence or the second key frame sequence into a preset water level line identification model to obtain water level image coordinates corresponding to the key frames;
The water level line space coordinate module is used for inputting the water level line image coordinates into the mapping model to obtain geographical space coordinates of the water level line;
and the river channel full-time-sequence water level information module is used for combining the geospatial coordinates of the water level lines at different moments to obtain the water level lines of the continuous time sequence at the two sides of the river channel.
9. A computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to perform the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311826041.1A CN117809244A (en) | 2023-12-28 | 2023-12-28 | Dynamic rainy day river water level monitoring method and device based on video monitoring network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311826041.1A CN117809244A (en) | 2023-12-28 | 2023-12-28 | Dynamic rainy day river water level monitoring method and device based on video monitoring network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117809244A true CN117809244A (en) | 2024-04-02 |
Family
ID=90426860
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311826041.1A Pending CN117809244A (en) | 2023-12-28 | 2023-12-28 | Dynamic rainy day river water level monitoring method and device based on video monitoring network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117809244A (en) |
-
2023
- 2023-12-28 CN CN202311826041.1A patent/CN117809244A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210073573A1 (en) | Ship identity recognition method based on fusion of ais data and video data | |
| CN108109385B (en) | System and method for identifying and judging dangerous behaviors of power transmission line anti-external damage vehicle | |
| CN110533923B (en) | Parking management method and device, computer equipment and storage medium | |
| CN112766274A (en) | Water gauge image water level automatic reading method and system based on Mask RCNN algorithm | |
| CN113159466B (en) | Short-time photovoltaic power generation prediction system and method | |
| CN105611244A (en) | Method for detecting airport foreign object debris based on monitoring video of dome camera | |
| CN112053391B (en) | Monitoring and early warning method and system based on dynamic three-dimensional model and storage medium | |
| US11776104B2 (en) | Roof condition assessment using machine learning | |
| CN111879292B (en) | Coastline dynamic monitoring method, coastline dynamic monitoring equipment and storage medium | |
| CN110569797A (en) | earth stationary orbit satellite image forest fire detection method, system and storage medium thereof | |
| CN109270853A (en) | A kind of working method of intelligent ecological Water Management System | |
| CN114267155A (en) | Geological disaster monitoring and early warning system based on video recognition technology | |
| CN112037543A (en) | Urban traffic light control method, device, equipment and medium based on three-dimensional modeling | |
| Zhang et al. | Aerial image analysis based on improved adaptive clustering for photovoltaic module inspection | |
| US12001938B2 (en) | Convolutional neural network for estimating a solar energy production indicator | |
| CN113936031A (en) | Cloud shadow track prediction method based on machine vision | |
| WO2022104798A1 (en) | 5g-based unmanned electronic traffic police duty system | |
| Aakroum et al. | Deep learning for inferring the surface solar irradiance from sky imagery | |
| CN117351436A (en) | River reach passing ship draft detection method | |
| CN117809244A (en) | Dynamic rainy day river water level monitoring method and device based on video monitoring network | |
| Kročka et al. | Extending parking occupancy detection model for night lighting and snowy weather conditions | |
| CN111583298B (en) | Short-time cloud picture tracking method based on optical flow method | |
| CN115410154A (en) | Method for identifying thermal fault of electrical equipment of wind power engine room | |
| Herrera-Carrillo et al. | Solar irradiance estimation based on image analysis | |
| CN113705442A (en) | Outdoor large-board advertising picture monitoring and identifying system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |