CN114598849A - Building construction safety monitoring system based on thing networking - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, in particular to a building construction safety monitoring system based on the Internet of things, which comprises an image acquisition unit: collecting multiple frames of building construction monitoring images, and determining the same moving object according to the characteristic points in the building construction monitoring images; an image processing unit: acquiring a gray level histogram of the building construction monitoring image, analyzing the enhancement effect of the gray level histogram before and after stretching according to the actual gray level range and the maximum gray level range, and obtaining the enhancement coefficient of the building construction monitoring image by combining the moving speed of a moving object in the building construction monitoring image; an image optimization unit: and constructing a linear transformation formula of each moving object by combining the moving speed of the moving object and the enhancement coefficient so as to perform image enhancement in a targeted manner. And performing targeted gray level enhancement according to the moving characteristics of the moving target in the monitoring picture to highlight the details of the moving target, so that the monitoring effect can be improved by realizing the image enhancement of the moving target.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a building construction safety monitoring system based on the Internet of things.
Background
The outdoor monitoring is applied to various scenes, and certain guarantee can be provided for the safety of people. In recent years, the scale of construction engineering projects is getting larger and larger, the construction process is more and more complex, the technical and management requirements are higher and more, and correspondingly, the difficulty of safety supervision on a construction site is higher and more, so that the safety problem of construction is paid much attention by society and people. On the construction site, a construction safety monitoring system is needed to be arranged to monitor the construction safety and ensure that the construction site meets the safety requirements. When building construction is monitored safely, related targets of a building construction site need to be monitored and identified, and the existing building construction safety monitoring system collects building construction monitoring images through related image collecting equipment and identifies the targets of the building construction monitoring images so as to realize safety monitoring of the related targets in the building construction site.
Disclosure of Invention
The invention aims to provide a building construction safety monitoring system based on the Internet of things, which is used for solving the technical problem that the existing building construction safety monitoring system cannot accurately identify related targets in building construction monitoring images, and adopts the following technical scheme:
the system comprises an image acquisition unit, a display unit and a control unit, wherein the image acquisition unit is used for acquiring a plurality of frames of building construction monitoring images and determining the same moving object in the two frames of building construction monitoring images according to the moving speed, the moving direction and the moving distance of characteristic points in the two frames of building construction monitoring images;
the image processing unit is used for obtaining a gray level histogram of the building construction monitoring image, stretching the actual gray level range of the gray level histogram to the maximum gray level range, obtaining the enhancement significance degree of the average gray level of the corresponding gray level histogram before and after the stretching, and obtaining the enhancement degree of the gray level of no pixel in the corresponding gray level histogram before and after the stretching; obtaining the moving speed of all moving objects in the building construction monitoring image to obtain the maximum moving speed and the minimum moving speed, and obtaining the enhancement coefficient of the building construction monitoring image by combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree;
and the image optimization unit is used for combining the enhancement coefficient with the moving speed of each moving object respectively to obtain a stretching parameter corresponding to each moving object, obtaining a linear transformation formula of each moving object according to the gray value range of each stretched moving object and the corresponding stretching parameter, and performing image enhancement on a plurality of moving objects in the building construction monitoring image respectively by using the linear transformation formula to obtain an optimized building construction monitoring image.
Further, the method for determining the same moving object in two frames of building construction monitoring images according to the moving speed, the moving direction and the moving distance of the feature points in the two frames of building construction monitoring images in the image acquisition unit comprises the following steps:
using a FLANN matching algorithm to match characteristic points in two frames of building construction monitoring images to obtain a plurality of characteristic point pairs according to the second step
Calculating the moving distance of each characteristic point pair according to the position information of each characteristic point in the characteristic point pairs; obtaining the second time difference and the moving distance from the sampling time difference and the moving distance of two frames of building construction monitoring images
The moving speed of the individual characteristic point pairs; according to the first
Obtaining the moving direction of the characteristic point pair by the position information of the characteristic point pair;
when the moving distance, the moving speed and the moving direction of at least preset number of feature point pairs are the same, determining the feature points of the at least preset number of feature point pairs belonging to the same moving object, and obtaining the same moving object.
Further, the method for enhancing the significance of the average gray level of the corresponding gray histogram before and after the contrast stretching in the image processing unit includes:
and counting the gray scale sum of gray scales contained in the gray scale histogram before stretching, calculating the average gray scale of the corresponding gray scale histogram before stretching according to the actual gray scale range and the gray scale sum, calculating the average gray scale of the corresponding gray scale histogram after stretching according to the maximum gray scale range and the gray scale sum of the corresponding gray scale histogram after stretching, and taking the ratio of the average gray scale of the corresponding gray scale histogram before stretching to the average gray scale of the corresponding gray scale histogram after stretching as the enhancement significance degree.
Further, the method for enhancing the gray level of no pixel in the corresponding gray histogram before and after the contrast stretching in the image processing unit includes:
counting the gray levels of pixels in the gray level histogram before stretching, and calculating the average value of the gray levels to obtain a first gray level average value; counting the gray levels of pixels in the stretched gray level histogram, and calculating the average value of the gray levels to obtain a second gray level average value; and taking the ratio of the first gray level average value to the second gray level average value as the enhancement degree.
Further, the method for obtaining the enhancement coefficient of the building construction monitoring image in the image processing unit by combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree comprises the following steps:
wherein the content of the first and second substances,
is the enhancement factor;
is the maximum moving speed;
is the minimum moving speed;
to a significant extent of said enhancement;
is the degree of enhancement.
Further, the stretch parameter in the image optimization unit is a product between a moving speed of a moving object and the enhancement coefficient.
Further, the method for obtaining the linear transformation formula of each moving object in the image optimization unit according to the gray scale value range of each stretched moving object and the corresponding stretching parameter includes:
obtaining a gray value range of the corresponding gray histogram after stretching according to the stretching parameters, and obtaining a maximum gray adjusting variable according to the gray value range and the maximum gray level range; and combining the maximum gray level adjustment variable and the stretching parameter to obtain a linear change formula corresponding to the moving object, wherein the linear change formula is as follows:
wherein the content of the first and second substances,
as a result of the original pixel values,
for the purpose of the enhanced pixel values,
is the stretching parameter;
the maximum gray scale adjustment variable.
The embodiment of the invention at least has the following beneficial effects: and performing targeted gray level enhancement according to the moving characteristics of the moving target in the monitoring picture to realize image enhancement of the moving target and highlight the details of the moving target, so that the moving target can be accurately identified and the monitoring effect is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of a building construction safety monitoring system based on the internet of things according to an embodiment of the present invention;
fig. 2 is a graph showing the comparative effect before and after stretching according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, with reference to the accompanying drawings and preferred embodiments, describes specific embodiments, structures, features and effects of a building construction safety monitoring system based on the internet of things according to the present invention. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the building construction safety monitoring system based on the internet of things is specifically described below with reference to the attached drawings.
The embodiment of the invention aims at the following specific scenes: the construction safety monitoring system is used for monitoring and identifying the target in the construction site, and when the construction monitoring image is not clear, the target identification is inaccurate, so that the construction monitoring image needs to be enhanced to ensure that the target in the construction site can be clearly monitored and identified.
Referring to fig. 1, a structural block diagram of a building construction safety monitoring system based on the internet of things according to an embodiment of the present invention is shown, where the system includes:
and the image acquisition unit 10 is used for acquiring multiple frames of building construction monitoring images and determining the same moving object in the two frames of building construction monitoring images according to the moving speed, the moving direction and the moving distance of the characteristic points in the two frames of building construction monitoring images.
Specifically, multiple frames of building construction monitoring images in the monitoring video are obtained, feature point detection is carried out on objects in each frame of building construction monitoring image, all the same moving objects in two frames of building construction monitoring images are detected according to the detected feature points, and the detection method comprises the following steps: using a FLANN matching algorithm to match characteristic points in two frames of building construction monitoring images to obtain N characteristic point pairs, wherein N is a positive integer greater than 0, and according to the second step
The position information of each characteristic point in the characteristic point pair calculates the moving distance of the characteristic point pair
And the sampling time difference of two frames of building construction monitoring images
And distance of movement
To obtain the first
Moving speed of characteristic point pair
While according to
The position information of each characteristic point pair obtains the moving direction of the characteristic point pair; when the moving distance, the moving speed and the moving direction of at least M preset feature point pairs are the same, the feature point pairs belong to feature points on the same object, and the object belongs to a moving object, and then the same moving object in the two frames of building construction monitoring images is obtained, wherein M is a positive integer equal to or smaller than N.
It should be noted that the moving features of all feature points on the same object are the same, and the feature points may be corner points or center points of the object.
The image processing unit 20 is configured to obtain a gray level histogram of the building construction monitoring image, stretch an actual gray level range of the gray level histogram to a maximum gray level range, obtain an enhanced significance degree for an average gray level of a corresponding gray level histogram before and after stretching, and obtain an enhanced degree for a gray level of no pixel in the corresponding gray level histogram before and after stretching; and obtaining the moving speed of all moving objects in the building construction monitoring image to obtain the maximum moving speed and the minimum moving speed, and obtaining the enhancement coefficient of the building construction monitoring image by combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree.
Specifically, to obtain a more sharp enhanced image, the original image is stretched to highlight the interesting part. The gray length of the gray histogram is increased in the stretching process, the gray value is uniformly stretched and distributed between [0,255], and the image with low contrast originally can be enhanced by changing the interval of the gray histogram, and referring to fig. 2, a contrast effect graph before and after stretching is shown.
According to the embodiment of the invention, the gray level histogram of each building construction monitoring image is obtainedObtaining actual gray level range of each gray histogram
Stretching the actual gray scale range to the maximum gray scale range [0,255]](ii) a The gray histogram is formed by counting the gray level distribution in the building construction monitoring image according to the gray level size and the occurrence frequency of the gray level, and counting the gray sum of the gray levels contained in the gray histogram before stretching
Calculating the average gray level of the corresponding gray histogram before stretching from the actual gray level range and the gray sum
Meanwhile, the average gray level of the corresponding stretched gray level histogram is calculated according to the maximum gray level range and the gray sum of the corresponding stretched gray level histogram
(ii) a The significance degree of the average gray level of the corresponding gray level histogram before and after the contrast stretching is enhanced
。
For stretching the gray level histogram, a region with concentrated pixel values is stretched, a region with a small part of pixel values is not stretched, however, in order to accurately describe the change of the gray level histogram before and after stretching, the gray levels which do not exist in the building construction monitoring image, namely the gray levels which do not have pixels in the gray level histogram before stretching are firstly counted, the average value of the gray levels is calculated, and the first gray level average value is obtained
Similarly, the gray levels of no pixel in the stretched gray level histogram are counted, and the average value of the gray levels is also calculated to obtain a second gray level average value
Comparing the first gray average value with the second gray average value to obtain corresponding enhancement degrees before and after stretching
。
Further, in order to accurately analyze the change before and after stretching, the moving object with fast speed change can be enhanced more obviously when the image is enhanced, and the moving object with low speed is enhanced insignificantly, so that the moving speeds of all the moving objects in one building construction monitoring image are obtained to obtain the maximum moving speed
And minimum moving speed
And further analyzing the amplitude change of the stretching by combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree to obtain an enhancement coefficient of the building construction monitoring image, wherein the calculation formula of the enhancement coefficient is as follows:
wherein the content of the first and second substances,
for enhancing the coefficient, the larger the moving speed, the larger the stretching width, and the larger the corresponding enhancement coefficient.
The image optimization unit 30 is configured to combine the enhancement coefficient with the moving speed of each moving object to obtain a stretching parameter corresponding to each moving object, obtain a linear transformation formula of each moving object according to the gray value range of each stretched moving object and the corresponding stretching parameter, and perform image enhancement on the plurality of moving objects in the building construction monitoring image by using the linear transformation formula to obtain an optimized building construction monitoring image.
Specifically, the core of the embodiment of the present invention is to enhance the moving objects according to the moving speed of the moving objects, so that the moving objects are purposefully stretched according to the moving speed of each moving object, and the calculation formula of the stretching parameter of each moving object is as follows:
wherein the content of the first and second substances,
is as follows
A stretch parameter of the individual moving object;
is as follows
The moving speed of the moving object.
The greater the speed, the greater the degree of stretching, and the greater the corresponding stretching parameters.
Furthermore, the stretching parameter only affects the gray width of the building construction monitoring image after equalization, but the stretched gray histogram is not necessarily stretched to the maximum gray level range, so that when the adjustment of the moving parameter of the moving object to the stretching parameter is limited, the adjustment amount needs to be obtained, and the specific method is as follows: assuming that the range of the corresponding gray level histogram of a moving object after stretching according to the corresponding stretching parameters is [0, R ]]The maximum gray scale range is [0,255]]The effect of speed on the draw width can be:
wherein, in the step (A),
adjust the variables for maximum gray scale, andand combining the stretching parameters of the moving object to obtain a linear transformation formula:
wherein, in the step (A),
is the value of the original pixel(s),
is the enhanced pixel value.
By utilizing the method for acquiring the linear transformation formula, the linear transformation formula of each moving object in the building construction monitoring image can be acquired, and the building construction monitoring image is subjected to linear piecewise transformation according to the linear transformation formula of each moving object, namely, the moving objects in the building construction monitoring image are subjected to image enhancement in a targeted manner, so that the optimized building construction monitoring image is finally acquired.
In summary, an embodiment of the present invention provides a building construction safety monitoring system based on the internet of things, including an image acquisition unit: collecting a plurality of frames of building construction monitoring images, and determining the same moving object according to the characteristic points in the building construction monitoring images; an image processing unit: acquiring a gray level histogram of the building construction monitoring image, analyzing an enhancement effect between before and after stretching of the gray level histogram according to an actual gray level range and a maximum gray level range, and obtaining an enhancement coefficient of the building construction monitoring image by combining the maximum moving speed and the minimum moving speed of a moving object in the building construction monitoring image; an image optimization unit: and constructing a linear transformation formula of each moving object by combining the moving speed of the moving object and the enhancement coefficient so as to perform image enhancement in a targeted manner. And performing targeted gray level enhancement according to the moving characteristics of the moving target in the monitoring picture to highlight the details of the moving target, so that the monitoring effect can be improved by realizing the image enhancement of the moving target.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
Claims (7)
1. The utility model provides a construction safety monitored control system based on thing networking which characterized in that, this system includes:
the system comprises an image acquisition unit, a monitoring unit and a control unit, wherein the image acquisition unit is used for acquiring multi-frame building construction monitoring images and determining the same moving object in the two frames of building construction monitoring images according to the moving speed, the moving direction and the moving distance of characteristic points in the two frames of building construction monitoring images;
the image processing unit is used for obtaining a gray level histogram of the building construction monitoring image, stretching the actual gray level range of the gray level histogram to the maximum gray level range, obtaining the enhancement significance degree of the average gray level of the corresponding gray level histogram before and after the stretching, and obtaining the enhancement degree of the gray level of no pixel in the corresponding gray level histogram before and after the stretching; obtaining the moving speed of all moving objects in the building construction monitoring image to obtain the maximum moving speed and the minimum moving speed, and obtaining the enhancement coefficient of the building construction monitoring image by combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree;
and the image optimization unit is used for combining the enhancement coefficient with the moving speed of each moving object respectively to obtain a stretching parameter corresponding to each moving object, obtaining a linear transformation formula of each moving object according to the gray value range of each stretched moving object and the corresponding stretching parameter, and performing image enhancement on a plurality of moving objects in the building construction monitoring image respectively by using the linear transformation formula to obtain an optimized building construction monitoring image.
2. The building construction safety monitoring system based on the internet of things as claimed in claim 1, wherein the method for determining the same moving object in two frames of building construction monitoring images according to the moving speed, moving direction and moving distance of the feature points in the two frames of building construction monitoring images in the image acquisition unit comprises:
using a FLANN matching algorithm to match characteristic points in two frames of building construction monitoring images to obtain a plurality of characteristic point pairs according to the second step
Calculating the moving distance of each characteristic point pair according to the position information of each characteristic point in the characteristic point pairs; obtaining the second by the sampling time difference and the moving distance of two frames of building construction monitoring images
The moving speed of the individual characteristic point pairs; according to the first
Obtaining the moving direction of the characteristic point pair by the position information of the characteristic point pair;
when the moving distance, the moving speed and the moving direction of at least preset number of feature point pairs are the same, determining the feature points of the at least preset number of feature point pairs belonging to the same moving object, and obtaining the same moving object.
3. The building construction safety monitoring system based on the internet of things as claimed in claim 1, wherein the method for enhancing the significance degree of the average gray level of the corresponding gray level histogram before and after the contrast stretching in the image processing unit comprises:
and counting the gray scale sum of gray scales contained in the gray scale histogram before stretching, calculating the average gray scale of the corresponding gray scale histogram before stretching according to the actual gray scale range and the gray scale sum, calculating the average gray scale of the corresponding gray scale histogram after stretching according to the maximum gray scale range and the gray scale sum of the corresponding gray scale histogram after stretching, and taking the ratio of the average gray scale of the corresponding gray scale histogram before stretching to the average gray scale of the corresponding gray scale histogram after stretching as the enhancement significance degree.
4. The building construction safety monitoring system based on the internet of things as claimed in claim 1, wherein the method for enhancing the gray level of no pixel in the corresponding gray histogram before and after the contrast stretching in the image processing unit comprises:
counting the gray levels of pixels in the gray level histogram before stretching, and calculating the average value of the gray levels to obtain a first gray level average value; counting the gray levels of pixels in the stretched gray level histogram, and calculating the average value of the gray levels to obtain a second gray level average value; and taking the ratio of the first gray level average value to the second gray level average value as the enhancement degree.
5. The internet of things-based building construction safety monitoring system according to claim 1, wherein the method for combining the maximum moving speed, the minimum moving speed, the enhancement significance degree and the enhancement degree in the image processing unit to obtain the enhancement coefficient of the building construction monitoring image comprises the following steps:
wherein the content of the first and second substances,
is the enhancement factor;
is the maximum moving speed;
is the minimum moving speed;
to a significant extent of said enhancement;
is the degree of enhancement.
6. The internet of things-based construction safety monitoring system of claim 1, wherein the tensile parameter in the image optimization unit is a product between a moving speed of a moving object and the enhancement coefficient.
7. The internet of things-based building construction safety monitoring system of claim 1, wherein the method for obtaining the linear transformation formula of each moving object in the image optimization unit according to the gray scale value range of each stretched moving object and the corresponding stretching parameter comprises:
obtaining a gray value range of the corresponding gray histogram after stretching according to the stretching parameters, and obtaining a maximum gray adjusting variable according to the gray value range and the maximum gray level range; and combining the maximum gray level adjustment variable and the stretching parameter to obtain a linear change formula corresponding to the moving object, wherein the linear change formula is as follows:
wherein the content of the first and second substances,
as a result of the original pixel values,
for the purpose of the enhanced pixel values,
is the stretching parameter;
adjusting variables for maximum gray scale。
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