CN113989357A - Shield slag-tapping gradation rapid estimation method based on monitoring video - Google Patents
Shield slag-tapping gradation rapid estimation method based on monitoring video Download PDFInfo
- Publication number
- CN113989357A CN113989357A CN202111326781.XA CN202111326781A CN113989357A CN 113989357 A CN113989357 A CN 113989357A CN 202111326781 A CN202111326781 A CN 202111326781A CN 113989357 A CN113989357 A CN 113989357A
- Authority
- CN
- China
- Prior art keywords
- slag
- video
- tapping
- images
- grading
- 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
- 238000010079 rubber tapping Methods 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000012544 monitoring process Methods 0.000 title claims abstract description 17
- 239000002893 slag Substances 0.000 claims abstract description 63
- 239000004575 stone Substances 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 239000002245 particle Substances 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 3
- 239000011435 rock Substances 0.000 claims description 2
- 238000007599 discharging Methods 0.000 claims 1
- 238000009412 basement excavation Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000009434 installation Methods 0.000 description 4
- 238000012216 screening Methods 0.000 description 3
- 239000002002 slurry Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000004566 building material Substances 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000011410 subtraction method Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a shield slag-tapping gradation rapid estimation method based on a monitoring video, which is characterized by comprising the following steps of: the method comprises the steps of installing a video sensor at a shield slag hole to achieve real-time collection of slag tapping video, obtaining a white pixel area, namely slag tapping block stones, in each video by adopting a frame difference method after the slag tapping video is equally divided, solving the problem that a large amount of slag tapping block stones cannot be captured by the traditional frame difference method, obtaining a calibration coefficient based on the ratio of the image size of a chessboard calibration plate to the size of the chessboard calibration plate, calculating the size of the white pixel area, namely the block stones, in a video frame image based on the calibration coefficient, enabling the size of the calculated block stones to be closer to the actual size of the block stones, and finally obtaining a final slag tapping grading result by calculating the slag tapping grading of each grading zone and then based on the average calculation of an image sequence, so that the estimation of the final slag tapping grading is more accurate.
Description
Technical Field
The invention relates to a shield slag-tapping gradation rapid estimation method based on a monitoring video.
Background
With the acceleration of urbanization process, the demand of tunnel construction by a shield method in China is increasing, wherein a slurry shield is an important construction method. During the construction process of the slurry shield engineering, a large amount of muck and slag stone waste is generated along with the excavation of an excavation surface, and after screening treatment, slag is discharged, so that the slag can be recovered and become building material resources again. Due to the characteristics of the construction method of the shield tunnel, the shield tunnel has few opportunities of directly contacting a geological excavation surface, and unscreened slag grading parameters depend on mud ingredients and surrounding rock characteristics and can indirectly reflect geological parameters to a certain degree.
At present, the shield slag tapping parameter is calculated and controlled mainly by the experience of a shield main driver, and the subjective factor is large. Aiming at the grading of shield slag tapping, a real-time calculation mode at the slag tapping stage is not available at present, and estimation is mainly carried out through screening after slag tapping at present. Because the method has hysteresis, the grain size distribution of the lump stones can be determined only by secondary screening of the slag, and the effect of dynamically reflecting geological parameters in real time is lost during slag tapping. And the amount of the muck of each ring of shield excavation is larger, so the granularity judgment level of the muck is also thicker, and the geological condition of the excavation process can not be known conveniently through muck. Therefore, a method for rapidly calculating shield slag tapping parameters is urgently needed in shield tunnel engineering.
The existing video moving object detection method mainly comprises a frame difference method, an optical flow method, a background subtraction method and the like. The traditional frame difference method is only suitable for capturing a small number of moving objects, while in the shield slag tapping capturing process, a large number of moving targets exist, attention is paid not only to the capturing of the moving targets but to the grading distribution of block stone particles in a video range, and the traditional frame difference method is difficult to deal with. Therefore, how to fully utilize the motion capture result of the frame difference method and construct a proper shield slag-tapping gradation calculation method is worth researching.
Disclosure of Invention
In order to solve the problems, the invention provides a method for estimating the slag grading finely and in real time, which adopts the following technical scheme:
the invention provides a shield slag-tapping gradation rapid estimation method based on a monitoring video, which is characterized by comprising the following steps of: step S1, installing a video sensor at a shield slag outlet to obtain a monitoring video, and marking the slag outlet position based on the monitoring video; step S2, selecting a period of time when the shield does not slag, placing chessboard calibration plates with different sizes at the slag discharge position, acquiring chessboard calibration plate images with different sizes corresponding to the chessboard calibration plates with different sizes based on a video sensor, and acquiring calibration coefficients according to the ratio of the size of the chessboard calibration plate images to the actual size of the chessboard calibration plates; step S3, setting a video recording time interval t of each slag tapping for a video sensor, acquiring a slag tapping video in real time, and dividing the slag tapping video into n parts, wherein each part of video is respectively recorded as fi (x), i is 1,2 … n, and x represents the x-th frame of an image in the i-th video; step S4, acquiring continuous m-frame color images in each video fi (x); step S5, processing the m frames of color images to obtain a preset number of black-and-white images, calculating the actual size of the white pixel areas in the preset number of black-and-white images based on the calibration coefficient and counting the distribution condition of the white pixel areas, and acquiring the number S of the white pixel areas in each gradation section; and step S6, counting the final slag tapping gradation value of n videos based on the number S of white pixel areas in each gradation section, wherein the white pixel areas are slag tapping block stones, and each gradation section is obtained by averagely dividing a preset interval of the block stone grain size.
The shield slag-tapping gradation quick estimation method based on the monitoring video provided by the invention can also have the technical characteristics that the step S5 comprises the following substeps: step S5-1, carrying out graying processing on the m-frame color image to obtain an m-frame gray image; step S5-2, eliminating partial noise of the m frames of gray images; step S5-3, processing the m frames of gray images after denoising based on a frame difference method to obtain m-1 black and white images; and step S5-4, recording white pixel areas in m-1 black-and-white images as slag blocks, acquiring the maximum size h (x) of each white pixel area according to a calibration coefficient, and counting the h (x) distribution in the m-1 black-and-white images to acquire the number S of the white pixel areas in each grading section.
The shield slag-out grading fast estimation method based on the surveillance video provided by the invention can also have the technical characteristics that in the step S5-2, a Gaussian filter is adopted for smoothing to eliminate partial noise.
The shield slag-tapping gradation quick estimation method based on the monitoring video provided by the invention can also have the technical characteristics that the step S5-3 comprises the following steps:
and (3) sequentially subtracting the m frames of gray level images, namely subtracting the m-1 frame of gray level images from the m frame of gray level images to obtain m-1 gray level images, wherein the m-1 gray level images meet the expression:
Ki(j)＝fi(m)-fi(m-1)
in the formula, Ki(j) A sequence of grayscale images for the ith video, where j is 1, 2.. m-1; for the gray image sequence K of the ith videoi(j) Carrying out binarization processing to obtain a corresponding black-and-white image sequence Gi(j) The sequence Gi(j) The image binarization method comprises m-1 black and white images, wherein a binarization processing formula is as follows:
in the formula, L is a preset threshold value set as: l ═ I (I)max-(Imax-Imin) B) 2, wherein, ImaxAnd IminRespectively representing the maximum of the gray value of the imageA value and a minimum value.
The shield slag-tapping gradation quick estimation method based on the monitoring video provided by the invention can also have the technical characteristics that the final slag-tapping gradation value has the calculation formula as follows:
in the formula, WzFinal slag grading, s, for n videosij(z) is the number of white pixel areas in the z-th gradation section of the j-th black-and-white image of the ith video.
Action and Effect of the invention
According to the shield slag-tapping gradation rapid estimation method based on the monitoring video, real-time collection of slag-tapping video is realized by installing the video sensor at the shield slag-tapping hole, and the problem that a large amount of slag-tapping block stones cannot be captured by the traditional frame difference method is solved because the white pixel area in each video, namely the slag-tapping block stones, is obtained by adopting the frame difference method after the slag-tapping video is equally divided. Meanwhile, the size of the block stone in the video frame image is calculated by adopting the calibration coefficient in the chessboard calibration method, so that the calculated size of the block stone is closer to the actual size of the block stone. Finally, the final slag grading result is obtained by calculating the slag grading of each grading section and then performing average calculation based on the image sequence, so that the final slag grading estimation is more accurate.
The shield slag-out grading fast estimation method based on the surveillance video realizes dynamic capture of the pixel region of the slag-out lump stone by combining video segmentation and a frame difference method, obtains the slag-out grading result of the whole video based on statistics of the pixel region of the lump stone in the segmented video, and establishes the shield slag-out grading fast estimation method based on the surveillance video (or images).
Drawings
FIG. 1 is a flow chart of a shield slag grading fast estimation method based on surveillance video in an embodiment of the invention;
FIG. 2 is a view showing an example of installation of a video sensor in the embodiment of the present invention;
FIG. 3 is a schematic diagram of a chessboard calibration board image in an embodiment of the invention;
fig. 4 is a flowchart for acquiring the number of white pixel areas in each gradation section according to the embodiment of the present invention.
Detailed Description
In order to make the technical means, creation features, achievement purposes and effects of the method easy to understand, the method for rapidly estimating shield slag grading based on surveillance video is specifically described below with reference to the embodiments and the accompanying drawings.
< example >
Fig. 1 is a flowchart of a shield slag-tapping gradation rapid estimation method based on a surveillance video in an embodiment of the present invention.
As shown in fig. 1, the shield slag-tapping gradation rapid estimation method based on the surveillance video comprises the following steps:
and step S1, installing a video sensor at the shield slag outlet to obtain a monitoring video, and marking the slag outlet position based on the monitoring video.
Fig. 2 is a diagram showing an example of installation of a video sensor in the embodiment of the present invention.
In the embodiment, the video sensor is arranged on the slurry shield slag tapping scaffold, and the installation position and the installation distance are based on the principle of optimal picture shooting and no interference to construction.
The video sensor is a solar camera (as shown in fig. 2) with a wireless network card transmission function, and the battery capacity of the solar camera meets the requirement of continuous endurance in rainy days for more than 25 days. To ensure video quality, the selected solar camera configuration should be at least over 200 million pixels.
Step S2, selecting the period of time when the shield does not slag, placing the chessboard calibration plates with different sizes at the slag discharge position, acquiring chessboard calibration plate images (shown in figure 3) with different sizes corresponding to the chessboard calibration plates with different sizes based on the video sensor, and acquiring calibration coefficients according to the ratio of the size of the chessboard calibration plate images to the actual size of the chessboard calibration plates.
In this embodiment, the size displayed by the chessboard calibration board image is set to be m, and the actual size of the chessboard calibration board is n, so that the calibration coefficient k is n/m.
And step S3, setting a video recording time interval t for each slag tapping on the video sensor, acquiring a slag tapping video in real time, and dividing the slag tapping video into n parts, wherein each part of video is respectively recorded as fi (x), and i is 1,2 … n, and x represents the x-th frame of the image in the i-th video.
In step S4, consecutive m-frame color images are obtained in each video fi (x).
Step S5, processing the m frames of color images to obtain a predetermined number of black-and-white images, calculating the actual size of the white pixel regions in the predetermined number of black-and-white images based on the calibration coefficients, and counting the distribution thereof to obtain the number S of the white pixel regions in each gradation section.
In this embodiment, the white pixel region is marked as slag-out lump stone, and the grading is an interval range of the particle size of the lump stone, which may be preset in advance according to actual requirements, in this embodiment, the minimum particle size of the lump stone is set to be Q, the maximum particle size of the lump stone is set to be Q1, the particle size interval of the lump stone is [ Q, Q1], the particle size interval is divided averagely to obtain each grading section, and the proportion of each grading section is counted respectively.
Fig. 4 is a flowchart for acquiring the number of white pixel areas in each gradation section according to the embodiment of the present invention.
As shown in fig. 4, step S5 includes the following sub-steps:
and step S5-1, carrying out graying processing on the m-frame color image to obtain an m-frame grayscale image.
In step S5-2, a gaussian filter is used to perform smoothing to remove part of the noise in the m-frame grayscale image.
And step S5-3, processing the m frames of gray images after noise removal by adopting a frame difference method to obtain m-1 black and white images.
The step S5-3 is specifically:
firstly, making difference on m frames of gray level images in sequence, namely subtracting the m-1 frame of gray level image from the m-1 frame of gray level image to obtain m-1 gray level images, and satisfying the expression:
Ki(j)＝fi(m)-fi(m-1)
in the formula, Ki(j) Is a sequence of grayscale images of the ith video, where j is 1, 2.
Then, binarization processing is carried out on the gray image sequence Ki (j) of the ith video to obtain a corresponding black-and-white image sequence Gi(j) The black-and-white image sequence Gi(j) Containing m-1 black and white images.
The binarization processing formula is as follows:
in the formula, L is a preset threshold value set as: l ═ I (I)max-(Imax-Imin) B) 2, wherein, ImaxAnd IminRespectively representing the maximum and minimum values of the image grey scale value.
And step S5-4, recording white pixel areas in m-1 black-and-white images as slag blocks, acquiring the maximum size h (x) of each white pixel area according to a calibration coefficient, and counting the h (x) distribution in the m-1 black-and-white images to acquire the number S of the white pixel areas in each grading section.
And step S6, counting the final slag tapping gradation value of n videos based on the number S of white pixel areas in each gradation section.
In this embodiment, the calculation formula of the slag tapping gradation is as follows:
in the formula, WzFinal slag grading, s, for n videosij(z) is the number of white pixel areas in the z-th gradation section of the j-th black-and-white image of the ith video.
Examples effects and effects
According to the shield slag-tapping gradation quick estimation method based on the monitoring video, real-time collection of slag-tapping video is achieved by installing the video sensor at a shield slag-tapping hole, the slag-tapping video is evenly divided and then smoothed by the Gaussian filter, partial noise in the video is eliminated, then a white pixel area in each video, namely slag-tapping block stone, is obtained by a frame difference method, and the size of the block stone is obtained by a calibration coefficient in a chessboard calibration method, so that the size of the calculated block stone is closer to the actual size of the block stone. And finally, counting the number of the stones in each grading section, and then obtaining a final slag tapping grading result based on average calculation of the image sequence, so that the final slag tapping grading estimation is not only quick but also more accurate.
In the embodiment, the deslagging video is divided into n parts equally at first, and then a frame difference method is adopted to capture a white pixel region, namely block stones, in each part of video, so that the problem that the traditional frame difference method cannot be used for capturing the block stones in shield deslagging is solved, and meanwhile, as the final deslagging gradation result is obtained by calculating the deslagging gradation of each gradation section and then based on the average calculation of an image sequence, the deslagging gradation is estimated more accurately.
The above-described embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-described embodiments.
Claims (5)
1. A shield slag-tapping gradation rapid estimation method based on a monitoring video is characterized by comprising the following steps:
step S1, installing a video sensor at a shield slag hole to obtain a monitoring video, and marking a slag discharging position based on the monitoring video;
step S2, selecting a period of time when the shield does not slag, placing chessboard calibration plates with different sizes at the slag discharge position, acquiring chessboard calibration plate images with different sizes corresponding to the chessboard calibration plates with different sizes based on the video sensor, and acquiring calibration coefficients according to the ratio of the size of the chessboard calibration plate images to the actual size of the chessboard calibration plates;
step S3, setting a video time interval t of each slag tapping for the video sensor, acquiring a slag tapping video in real time, and dividing the slag tapping video into n parts, wherein each part of video is respectively recorded as fi (x), i is 1,2 … n, and x represents the x-th frame of an image in the i-th video;
step S4, acquiring continuous m-frame color images in each video fi (x);
step S5, obtaining black and white images with preset number by processing the m frames of color images by a frame difference method, calculating the actual size of the white pixel areas in the black and white images with preset number based on the calibration coefficient and counting the distribution condition thereof, and obtaining the number S of the white pixel areas in each grading section;
step S6, counting the final slag tapping gradation value of n videos based on the number S of the white pixel areas in each gradation section,
wherein the white pixel area is the slag block stone,
each grading section is obtained by averagely dividing a preset interval of the particle size of the rock.
2. The shield slag-tapping grading rapid estimation method based on the surveillance video according to claim 1, characterized in that:
wherein the step S5 includes the following substeps:
step S5-1, carrying out graying processing on the m-frame color image to obtain an m-frame gray image;
step S5-2, eliminating partial noise of the m frames of gray images;
step S5-3, processing the m frames of gray images after denoising based on a frame difference method to obtain m-1 black and white images;
step S5-4, recording the white pixel areas in the m-1 black-and-white images as slag blocks, acquiring the maximum size h (x) of each white pixel area according to the calibration coefficient, and counting the h (x) distribution in the m-1 black-and-white images to acquire the number S of the white pixel areas in each grading section.
3. The shield slag-tapping grading rapid estimation method based on the surveillance video according to claim 2, characterized in that:
in step S5-2, a gaussian filter is used to perform a smoothing process to remove a part of the noise.
4. The shield slag-tapping grading rapid estimation method based on the surveillance video according to claim 2, characterized in that:
wherein the step S5-3 includes the steps of:
and (3) sequentially subtracting the m frames of gray level images, namely subtracting the m-1 frame of gray level images from the m frame of gray level images to obtain m-1 gray level images, wherein the m-1 gray level images meet the expression:
Ki(j)＝fi(m)-fi(m-1)
in the formula, Ki(j) A sequence of grayscale images for the ith video, where j is 1, 2.. m-1;
for the gray image sequence K of the ith videoi(j) Carrying out binarization processing to obtain a corresponding black-and-white image sequence Gi(j) The sequence Gi(j) Contains m-1 black-and-white images,
the binarization processing formula is as follows:
in the formula, L is a preset threshold value set as: l ═ I (I)max-(Imax-Imin) B) 2, wherein, ImaxAnd IminRespectively representing the maximum and minimum values of the image grey scale value.
5. The shield slag-tapping grading rapid estimation method based on the surveillance video according to claim 4, characterized in that:
wherein, the calculation formula of the final slag grading value is as follows:
in the formula, WzFor the final slag grading value, s, of the n videosij(z) is the number of white pixel areas in the z-th gradation section of the j-th black-and-white image of the ith video.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111326781.XA CN113989357A (en) | 2021-11-10 | 2021-11-10 | Shield slag-tapping gradation rapid estimation method based on monitoring video |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111326781.XA CN113989357A (en) | 2021-11-10 | 2021-11-10 | Shield slag-tapping gradation rapid estimation method based on monitoring video |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113989357A true CN113989357A (en) | 2022-01-28 |
Family
ID=79747694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111326781.XA Pending CN113989357A (en) | 2021-11-10 | 2021-11-10 | Shield slag-tapping gradation rapid estimation method based on monitoring video |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113989357A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867332A (en) * | 2015-05-26 | 2015-08-26 | 南京通用电器有限公司 | Interval-frame difference method based detection method of driving vehicle in front lane line |
| CN205352389U (en) * | 2016-02-24 | 2016-06-29 | 广西大学 | System for realize subway shield tunnel earth's surface displacement real -time supervision through high definition video |
| WO2018095082A1 (en) * | 2016-11-28 | 2018-05-31 | 江苏东大金智信息***有限公司 | Rapid detection method for moving target in video monitoring |
| CN109184709A (en) * | 2018-10-29 | 2019-01-11 | 幸智军 | A kind of construction of Suporting structure is slagged tap monitoring management system and method |
| CN110513114A (en) * | 2019-08-08 | 2019-11-29 | 中国建筑第四工程局有限公司 | A kind of shield-tunneling construction passes through the construction of high-strength boulder group |
-
2021
- 2021-11-10 CN CN202111326781.XA patent/CN113989357A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867332A (en) * | 2015-05-26 | 2015-08-26 | 南京通用电器有限公司 | Interval-frame difference method based detection method of driving vehicle in front lane line |
| CN205352389U (en) * | 2016-02-24 | 2016-06-29 | 广西大学 | System for realize subway shield tunnel earth's surface displacement real -time supervision through high definition video |
| WO2018095082A1 (en) * | 2016-11-28 | 2018-05-31 | 江苏东大金智信息***有限公司 | Rapid detection method for moving target in video monitoring |
| CN109184709A (en) * | 2018-10-29 | 2019-01-11 | 幸智军 | A kind of construction of Suporting structure is slagged tap monitoring management system and method |
| CN110513114A (en) * | 2019-08-08 | 2019-11-29 | 中国建筑第四工程局有限公司 | A kind of shield-tunneling construction passes through the construction of high-strength boulder group |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8279345B2 (en) | System and method for random noise estimation in a sequence of images | |
| CN101127835B (en) | A pre-processing method and device for improving video image quality of digital video camera | |
| CN109167928B (en) | Rapid automatic exposure method and system based on display panel defect detection | |
| JP4767240B2 (en) | Method and apparatus for detecting video boundary and computer-readable recording medium embodying the same | |
| WO2020073686A1 (en) | Foundation pit automatic monitoring data screening method and system based on image identification | |
| CN104867159A (en) | Stain detection and classification method and device for sensor of digital camera | |
| CN106521066A (en) | Blast furnace burden particle size monitoring system and distributed data on-line analysis method | |
| CN112149543B (en) | Building dust recognition system and method based on computer vision | |
| CN114004834B (en) | Method, equipment and device for analyzing foggy weather condition in image processing | |
| CN109359593B (en) | Rain and snow environment picture fuzzy monitoring and early warning method based on image local grid | |
| CN115100207B (en) | Machine vision-based detection system and detection method | |
| CN116542969B (en) | Road asphalt adhesion detection method based on vision technology | |
| CN115797473B (en) | Concrete forming evaluation method for civil engineering | |
| CN116630321B (en) | Intelligent bridge health monitoring system based on artificial intelligence | |
| CN104700405A (en) | Foreground detection method and system | |
| CN107610147A (en) | A kind of waste film Reinforced Aeolian Sand method for processing foundation | |
| JP5307695B2 (en) | Granular material particle size identification method and particle size identification system | |
| JP2016126750A (en) | Image processing system, image processing device, imaging device, image processing method, program, and recording medium | |
| CN116030054A (en) | Feed processing screening quality evaluation method | |
| CN110533626B (en) | All-weather water quality identification method | |
| CN107784258A (en) | Subway density of stream of people method of real-time | |
| CN109711313B (en) | Real-time video monitoring method for identifying sewage poured into river channel | |
| CN113989357A (en) | Shield slag-tapping gradation rapid estimation method based on monitoring video | |
| JP2928714B2 (en) | Cell activity determination method and device | |
| TW201913040A (en) | Method for detecting flood in mountain area |
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 |