CN115294486A - Method for identifying violation building data based on unmanned aerial vehicle and artificial intelligence - Google Patents
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Abstract
The invention relates to the technical field of image processing, in particular to a violation building data identification method based on an unmanned aerial vehicle and artificial intelligence, which comprises the following steps: collecting a high-definition video by an unmanned aerial vehicle; performing interframe difference processing on a high-definition video acquired by an unmanned aerial vehicle to acquire an effective high-definition picture; transmitting the obtained high-definition picture to a ground workstation by a 5G technology, and further preprocessing the picture; and (4) carrying out operation on the preprocessed pictures through an artificial intelligence algorithm, and further realizing the identification and judgment of the garbage. According to the invention, the unmanned aerial vehicle is used for acquiring image data, and by improving the frame difference algorithm and expanding the target with slow frame difference detection change, useless or repeated pictures discarded by effective high-definition pictures can be obtained, and the 5G transmission speed and the detection efficiency of the artificial intelligence algorithm are further improved; the garbage data image is combined with an artificial intelligence algorithm to realize automatic recognition and judgment of garbage.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a violation building data identification method based on an unmanned aerial vehicle and artificial intelligence.
Background
In the past, informatization technology is not common, and the illegal building management department usually adopts a manual inspection means to manually find rubbish. In recent years, a method for performing remote monitoring by using a camera is also presented for monitoring nearby illegal buildings, but the method has some defects, such as dead monitoring corners, large capital investment and the like. Although the methods can be used for collecting the garbage images, the garbage identification and judgment are mainly carried out in a manual identification mode, when the number of the obtained images is large or the image range is large, huge workload can be generated in the manual identification mode, and meanwhile, the identification efficiency is relatively low. And the current technologies such as intelligent identification and automatic feature extraction are still in the research stage and cannot be widely applied, so that the unmanned aerial vehicle inspection effect is greatly reduced.
Disclosure of Invention
The invention aims to solve the defects in the background technology by providing a violation building data identification method based on an unmanned aerial vehicle and artificial intelligence.
The technical scheme adopted by the invention is as follows:
the method for identifying the violation building data based on the unmanned aerial vehicle and the artificial intelligence comprises the following steps:
s1.1: collecting a high-definition video by an unmanned aerial vehicle;
s1.2: performing interframe difference processing on a high-definition video acquired by an unmanned aerial vehicle to acquire an effective high-definition picture;
s1.3: transmitting the obtained high-definition picture to a ground workstation by a 5G technology, and further preprocessing the picture;
s1.4: and (4) calculating the preprocessed pictures through an artificial intelligence algorithm, and further realizing the identification and judgment of the garbage.
As a preferred technical scheme of the invention: in the step S1.2, an improved inter-frame difference method is adopted for inter-frame difference processing, and the improved inter-frame difference method performs difference operation on images of a current frame and previous and next frames by using three frames of image information.
As a preferred technical scheme of the invention: the improved interframe difference method formula is as follows:
andthe images are respectively the difference between the front frame and the back frame and the current frame,and、the gray values of the image at time t, time t-1 and time t +1 respectively,is a coefficient of gray scale to be used,is the total number of pixels of the region to be detected,an image to be detected is obtained;
and then the frame difference image is obtained through threshold value T processing, and binarization processing is carried out:
As a preferred technical scheme of the invention: in the S1.3, the preprocessing method comprises image gray processing, color inversion and a canny edge detection algorithm;
the picture gray level processing formula is as follows:
wherein,、andrespectively representing components of red, green and blue, and averaging the three components of the color image to obtain a gray value;
the color inversion formula is as follows:
the canny edge detection algorithm is as follows:
is a Gaussian distributionThe standard deviation of the measured data was found to be,is a pixel point;
multiplying each pixel point and the neighborhood thereof by a Gaussian matrix, and taking the weighted average value as the final gray value:
wherein,is the amplitude of the received signal and is,in the form of a direction of rotation,andrespectively the image at a pixel pointHorizontal gradient magnitude and vertical gradient magnitude.
As a preferred technical scheme of the invention: after the canny edge detection algorithm is detected, calculating a gradient value and a gradient direction through a sobel operator, and filtering out a maximum value; setting eight gradient directions, including:;
and setting a threshold value:
and filtering the noise according to the image definition evaluation value:
As a preferred technical scheme of the invention: in S1.4, the artificial intelligence algorithm includes: image input, basic feature extraction, multi-layer complex feature extraction, feature learning and classification detection results; the method comprises the steps of carrying out convolution calculation on an image, outputting a classification result by a Softmax classifier through a pooling layer, an activation function and a full connection layer, and realizing the identification and judgment of garbage.
As a preferred technical scheme of the invention: the convolution calculation is that a small matrix slides on an image or an input characteristic diagram, the output characteristic diagram is a result obtained by multiplying and adding the matrix, and the calculation method comprises the following steps:
wherein,is the number of convolution kernel channels, sum is the matrix addition operator,bin order to be a characteristic parameter of the device,
performing aggregation statistics through the features of different positions, and selecting a representative value to represent the original feature; by adopting the maxpololing method, the calculation formula is as follows:
Introducing an activation function relu function in the neural network, wherein the calculation formula is as follows:
wherein x is a characteristic value used for improving the characterization capability of the artificial intelligence algorithm.
As a preferred technical scheme of the invention: the convolution layer, the pooling layer and the activation function structure map original data to a feature vector space, and the full connection layer is obtained by a calculation formula:
wherein,for the mapped feature samples, the learned distributed features are integrated and summarized and then mapped to a sample label space.
As a preferred technical scheme of the invention: softmax maps the feature vectors of the input neural network to (0, 1) space, the sum of these values is 1, and the maximum probability value is selected as the classification result:
As a preferred technical scheme of the invention: and after the artificial intelligence algorithm is processed, performing garbage detection, and summarizing detection results to terminal equipment for visual display and information storage.
Compared with the prior art, the violation building data identification method based on the unmanned aerial vehicle and the artificial intelligence has the beneficial effects that:
according to the invention, the unmanned aerial vehicle is used for acquiring image data, and by improving the frame difference algorithm and expanding the target with slow frame difference detection change, useless or repeated pictures discarded by effective high-definition pictures can be obtained, and the 5G transmission speed and the detection efficiency of the artificial intelligence algorithm are further improved; the garbage data image is combined with an artificial intelligence algorithm to realize automatic recognition and judgment of garbage. The unmanned aerial photography technology is used for photography, and a remote sensing platform which is convenient to operate and easy to transfer is provided for aerial photography. The take-off and landing are less limited by the field, and the landing can be carried out on playgrounds, highways or other open grounds, so that the take-off and landing are good in stability and safety, and the take-off and landing are very easy to transfer.
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FIG. 1 is a flow chart of a method of a preferred embodiment of the present invention;
fig. 2 is a technical structural view of a preferred embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other, and the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, a preferred embodiment of the present invention provides a violation building data identification method based on unmanned aerial vehicles and artificial intelligence, including the following steps:
s1.1: collecting a high-definition video by an unmanned aerial vehicle;
s1.2: performing interframe difference processing on the high-definition video acquired by the unmanned aerial vehicle to acquire an effective high-definition picture;
s1.3: transmitting the obtained high-definition picture to a ground workstation by a 5G technology, and further preprocessing the picture;
s1.4: and (4) carrying out operation on the preprocessed pictures through an artificial intelligence algorithm, and further realizing the identification and judgment of the garbage.
In the step S1.2, an improved inter-frame difference method is adopted for inter-frame difference processing, and the improved inter-frame difference method performs difference operation on images of a current frame and previous and next frames by using three frames of image information.
The improved interframe difference method formula is as follows:
andthe images are respectively the difference between the front frame and the back frame and the current frame,and、the gray values of the image at the time t, the time t-1 and the time t +1 respectively,is a function of the gamma of the color,is the total number of pixels of the region to be detected,for the image to be detected;
And then the frame difference image is obtained through threshold value T processing, and binarization processing is carried out:
In the S1.3, the preprocessing method comprises image gray processing, color inversion and a canny edge detection algorithm;
the picture gray level processing formula is as follows:
wherein,、andrespectively representing components of red, green and blue, and averaging the three components of the color image to obtain a gray value;
the color inversion formula is as follows:
the canny edge detection algorithm is as follows:
multiplying each pixel point and the neighborhood thereof by a Gaussian matrix, and taking the weighted average value as the final gray value:
wherein,in order to be the amplitude value,in the form of a direction of rotation,andrespectively the image at a pixel pointHorizontal gradient magnitude and vertical gradient magnitude.
After the canny edge detection algorithm is detected, calculating a gradient value and a gradient direction through a sobel operator, and filtering out a maximum value; setting eight gradient directions, including:;
and setting a threshold value:
and filtering the noise according to the image definition evaluation value:
In S1.4, the artificial intelligence algorithm includes: image input, basic feature extraction, multi-layer complex feature extraction, feature learning and classification detection results; the method comprises the steps of carrying out convolution calculation on an image, outputting a classification result by a Softmax classifier through a pooling layer, an activation function and a full connection layer, and realizing the identification and judgment of the garbage.
The convolution calculation is that a small matrix slides on an image or an input characteristic diagram, the output characteristic diagram is a result obtained by multiplication and addition of the matrix, and the calculation method is as follows:
wherein,is the number of convolution kernel channels, sum is the matrix addition operator,bin order to be a characteristic parameter of the device,
performing aggregation statistics through the features of different positions, and selecting a representative value to represent the original feature; by adopting the maxporoling method, the calculation formula is as follows:
Introducing an activation function relu function in the neural network, wherein the calculation formula is as follows:
wherein x is a characteristic value used for improving the characterization capability of the artificial intelligence algorithm.
The convolution layer, the pooling layer and the activation function structure map original data to a feature vector space, and the full connection layer is obtained by a calculation formula:
wherein,for the mapped feature samples, the learned distributed features are summarized in an integration and then mapped to a sample label space.
Softmax maps the feature vectors of the input neural network to (0, 1) space, and the sum of these values is 1, selecting the maximum probability value as the classification result:
And after the artificial intelligence algorithm is processed, performing garbage detection, and summarizing detection results to terminal equipment for visual display and information storage.
In this embodiment, referring to fig. 2, the garbage data identification algorithm based on the unmanned aerial vehicle and the artificial intelligence mainly comprises three parts: unmanned aerial vehicle, 5G technology and artificial intelligence algorithm. The artificial intelligence algorithm comprises image input, basic feature extraction, multi-layer complex feature extraction, feature learning and classification detection results.
The unmanned aerial vehicle plays a role in collecting high-definition videos, interframe difference processing is carried out on the videos, effective high-definition pictures are obtained, useless or repeated pictures are abandoned, and the 5G transmission speed and the detection efficiency of an artificial intelligence algorithm are further improved. The implementation principle mathematical formula of the interframe difference method is expressed as follows:
wherein,is a difference image between two successive frame images,andare respectively asAndthe image of the moment in time,is a threshold value selected when the difference image is binarized,the representation of the foreground is performed,representing the background.
The method is characterized in that an inter-frame difference method is improved, and a target with slow change is detected by expanding a frame difference, wherein the improved inter-frame difference method utilizes three frames of image information and carries out difference operation on images of a current frame and a front frame and a rear frame.
The improved interframe difference method formula is as follows:
andthe images are respectively the difference between the front frame and the back frame and the current frame,and、the gray values of the image at the time t, the time t-1 and the time t +1 respectively,is a coefficient of gray scale to be used,is the total number of pixels of the region to be detected,an image to be detected is obtained;
and then, performing binarization processing on the frame difference image obtained by threshold T processing:
And transmitting the image processed by the interframe difference method to a ground workstation by using a 5G technology, and further preprocessing the image, wherein the preprocessing method comprises image gray processing, color inversion and canny edge detection.
The mathematical expression formula of the picture gray processing is as follows:
wherein,、andrespectively represent components of red, green and blueThe three component luminance in the color image is averaged to obtain a gray value.
The color inversion mathematical expression is as follows:
wherein,for the value of the current pixel, it is,the pixel values after color inversion; the inverted pixel value is equal to 255 minus the current pixel value.
The mathematical expression for canny edge detection is as follows:
wherein,is the standard deviation of the Gaussian distribution,for pixel points, multiplying each pixel point and the neighborhood thereof by a Gaussian matrix, and taking the weighted average value as the final gray value:
wherein,in order to be the amplitude value,in the form of a direction of rotation,andrespectively the image at a pixel pointHorizontal gradient magnitude and vertical gradient magnitude.
Calculating a gradient value and a gradient direction through a sobel operator, and filtering out a maximum value; setting eight gradient directions, including:;
and setting a threshold value:
and filtering the noise according to the image definition evaluation value:
And (3) sending the preprocessed image to an artificial intelligence algorithm, performing convolution calculation on the image, and outputting a classification result by a Softmax classifier after passing through a pooling layer, an activation function and a full connection layer. And then realize discernment and judgement to rubbish.
Convolution calculation is that some small matrixes slide on an image or an input characteristic diagram, the result obtained by multiplication and addition of the matrixes is an output characteristic diagram, and the calculation method comprises the following steps:
wherein,is the number of convolution kernel channels, sum is the matrix addition operator,bis a characteristic parameter.
The feature size extracted after convolutional layer is still too large, and it is very inconvenient to directly use for training and easy to overfit. And performing aggregate statistics on the features at different positions, and selecting a representative value to represent the original feature. The model selection is a maxporoling method, and the calculation formula is as follows:
In order for an artificial intelligence algorithm to have good characterization capabilities, nonlinear elements must be introduced. Therefore, an activation function is introduced in the neural network. The activation function introduced by the model is a relu function, and the calculation formula is as follows:
wherein x is a characteristic value.
The convolution layer, the pooling layer, the activation function and other structures map original data to a feature vector space, the full-connection layer is used for integrating and summarizing learned distributed features and then mapping the integrated and summarized distributed features to a sample mark space, and a calculation formula is as follows:
Softmax maps the feature vectors of the input neural network to the (0, 1) space, and the sum of these values is 1, the output value can be understood as a probability value. So when outputting the result, the classification result with the highest probability value is selected.
After the processing of the artificial intelligence algorithm, the garbage can be accurately detected, and the detection result is gathered to the terminal equipment for visual display and information storage.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. The utility model provides a building data recognition method violating regulations based on unmanned aerial vehicle and artificial intelligence which characterized in that: the method comprises the following steps:
s1.1: collecting a high-definition video by an unmanned aerial vehicle;
s1.2: performing interframe difference processing on the high-definition video acquired by the unmanned aerial vehicle to acquire an effective high-definition picture;
s1.3: transmitting the obtained high-definition pictures to a ground workstation by a 5G technology, and further preprocessing the pictures;
s1.4: and (4) calculating the preprocessed pictures through an artificial intelligence algorithm, and further realizing the identification and judgment of the garbage.
2. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 1, wherein: in the step S1.2, an improved inter-frame difference method is adopted for inter-frame difference processing, and the improved inter-frame difference method performs difference operation on images of a current frame and previous and next frames by using three frames of image information.
3. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 2, wherein: the improved interframe difference method formula is as follows:
andthe images are respectively the difference between the front frame and the back frame and the current frame,and、the gray values of the image at time t, time t-1 and time t +1 respectively,is a coefficient of gray scale to be used,Nis the total number of pixels of the region to be detected,an image to be detected is obtained;
and then, performing binarization processing on the frame difference image obtained by threshold T processing:
4. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 1, wherein: in the S1.3, the preprocessing method comprises image gray processing, color inversion and a canny edge detection algorithm;
the picture gray level processing formula is as follows:
wherein,、andrespectively representing components of red, green and blue, and averaging the three components of the color image to obtain a gray value;
the color inversion formula is as follows:
the canny edge detection algorithm is as follows:
multiplying each pixel point and the neighborhood thereof by a Gaussian matrix, and taking the weighted average value as the final gray value:
5. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 4, wherein: after the canny edge detection algorithm is detected, calculating a gradient value and a gradient direction through a sobel operator, and filtering out a maximum value; setting eight gradient directions, including: 0 °,45 °,90 °,135 °,180 °,225 °,270 °,315 °;
and setting a threshold value:
and filtering the noise according to the image definition evaluation value:
6. The violation building data identification method based on unmanned aerial vehicle and artificial intelligence as recited in claim 1, wherein: in S1.4, the artificial intelligence algorithm includes: image input, basic feature extraction, multi-layer complex feature extraction, feature learning and classification detection results; the method comprises the steps of carrying out convolution calculation on an image, outputting a classification result by a Softmax classifier through a pooling layer, an activation function and a full connection layer, and realizing the identification and judgment of the garbage.
7. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 6, wherein: the convolution calculation is that a small matrix slides on an image or an input characteristic diagram, the output characteristic diagram is a result obtained by multiplication and addition of the matrix, and the calculation method is as follows:
wherein,is the number of the convolution kernel channels, sum is the matrix addition operator,bis a characteristic parameter;
performing aggregation statistics through the features of different positions, and selecting a representative value to represent the original feature; by adopting the maxpololing method, the calculation formula is as follows:
introducing an activation function relu function into the neural network, wherein the calculation formula is as follows:
wherein x is a characteristic value and is used for improving the characterization capability of the artificial intelligence algorithm.
8. The unmanned aerial vehicle and artificial intelligence based violation building data identification method of claim 6, wherein: the convolution layer, the pooling layer and the activation function structure map original data to a feature vector space, and the full connection layer is obtained by a calculation formula:
9. The unmanned aerial vehicle and artificial intelligence based violation building data identification method as recited in claim 6, wherein: softmax maps the feature vectors of the input neural network to (0, 1) space, the sum of these values is 1, and the maximum probability value is selected as the classification result:
10. The unmanned aerial vehicle and artificial intelligence based violation building data identification method of claim 6, wherein: and after the artificial intelligence algorithm is processed, performing garbage detection, and summarizing detection results to terminal equipment for visual display and information storage.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140118716A1 (en) * | 2012-10-31 | 2014-05-01 | Raytheon Company | Video and lidar target detection and tracking system and method for segmenting moving targets |
CN108230368A (en) * | 2016-12-14 | 2018-06-29 | 贵港市瑞成科技有限公司 | A kind of fast-moving target detection method |
CN109460764A (en) * | 2018-11-08 | 2019-03-12 | 中南大学 | A kind of satellite video ship monitoring method of combination brightness and improvement frame differential method |
CN109472200A (en) * | 2018-09-29 | 2019-03-15 | 深圳市锦润防务科技有限公司 | A kind of intelligent sea rubbish detection method, system and storage medium |
CN110298323A (en) * | 2019-07-02 | 2019-10-01 | 中国科学院自动化研究所 | Detection method of fighting based on video analysis, system, device |
CN110599523A (en) * | 2019-09-10 | 2019-12-20 | 江南大学 | ViBe ghost suppression method fused with interframe difference method |
CN110910318A (en) * | 2019-10-21 | 2020-03-24 | 中国科学院西安光学精密机械研究所 | Weak contrast schlieren small ball center calculation method for comprehensive diagnosis light path quick automatic collimation system |
CN111539296A (en) * | 2020-04-17 | 2020-08-14 | 河海大学常州校区 | Method and system for identifying illegal building based on remote sensing image change detection |
CN112581492A (en) * | 2019-09-27 | 2021-03-30 | 北京京东尚科信息技术有限公司 | Moving target detection method and device |
CN114120120A (en) * | 2021-11-25 | 2022-03-01 | 广东电网有限责任公司 | Method, device, equipment and medium for detecting illegal building based on remote sensing image |
CN114241364A (en) * | 2021-11-30 | 2022-03-25 | 南京理工大学 | Method for quickly calibrating foreign object target of overhead transmission line |
-
2022
- 2022-10-08 CN CN202211219087.2A patent/CN115294486B/en active Active
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140118716A1 (en) * | 2012-10-31 | 2014-05-01 | Raytheon Company | Video and lidar target detection and tracking system and method for segmenting moving targets |
CN108230368A (en) * | 2016-12-14 | 2018-06-29 | 贵港市瑞成科技有限公司 | A kind of fast-moving target detection method |
CN109472200A (en) * | 2018-09-29 | 2019-03-15 | 深圳市锦润防务科技有限公司 | A kind of intelligent sea rubbish detection method, system and storage medium |
CN109460764A (en) * | 2018-11-08 | 2019-03-12 | 中南大学 | A kind of satellite video ship monitoring method of combination brightness and improvement frame differential method |
CN110298323A (en) * | 2019-07-02 | 2019-10-01 | 中国科学院自动化研究所 | Detection method of fighting based on video analysis, system, device |
CN110599523A (en) * | 2019-09-10 | 2019-12-20 | 江南大学 | ViBe ghost suppression method fused with interframe difference method |
CN112581492A (en) * | 2019-09-27 | 2021-03-30 | 北京京东尚科信息技术有限公司 | Moving target detection method and device |
CN110910318A (en) * | 2019-10-21 | 2020-03-24 | 中国科学院西安光学精密机械研究所 | Weak contrast schlieren small ball center calculation method for comprehensive diagnosis light path quick automatic collimation system |
CN111539296A (en) * | 2020-04-17 | 2020-08-14 | 河海大学常州校区 | Method and system for identifying illegal building based on remote sensing image change detection |
CN114120120A (en) * | 2021-11-25 | 2022-03-01 | 广东电网有限责任公司 | Method, device, equipment and medium for detecting illegal building based on remote sensing image |
CN114241364A (en) * | 2021-11-30 | 2022-03-25 | 南京理工大学 | Method for quickly calibrating foreign object target of overhead transmission line |
Non-Patent Citations (2)
Title |
---|
XIAOHE LUO等: "Improved Three-Frame-Difference Algorithm for Infrared Moving Target", 《IEEE》 * |
赵柏山 等: "基于帧差与背景差分的改进目标识别算法", 《通信技术》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115588145A (en) * | 2022-12-12 | 2023-01-10 | 深圳联和智慧科技有限公司 | Unmanned aerial vehicle-based river channel garbage floating identification method and system |
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