CN115294486B - Method for identifying and judging illegal garbage 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 garbage identification and judgment 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) calculating the preprocessed pictures through an artificial intelligence algorithm, and further realizing the identification and judgment of the garbage. According to the invention, the image data is acquired by the unmanned aerial vehicle, and by improving the frame difference algorithm and expanding the target with slow frame difference detection change, useless or repeated pictures can be obtained and discarded by effective high-definition pictures, so that 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 garbage identification and judgment 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 the 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 garbage identification and judgment method based on an unmanned aerial vehicle and artificial intelligence.
The technical scheme adopted by the invention is as follows:
the method for identifying and judging the illegal garbage 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 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) carrying out operation on 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, performing binarization processing on the frame difference image obtained by threshold T processing:
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 colors, and solving the average value of 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.
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; eight gradient directions are set, 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 classification result is 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 into 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 garbage identification and judgment 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 transition 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 wider ground, so that the stability and the safety are good, and the transition is very easy.
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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 the features in the embodiments may be combined with each other, and the technical solution 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the preferred embodiment of the present invention provides a violation garbage identification and determination method based on unmanned aerial vehicles and artificial intelligence, which 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.
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 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, performing binarization processing on the frame difference image obtained by threshold T processing:
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 colors, and solving the average value of the three components of the color image to obtain a gray value;
the color inversion formula is as follows:
wherein,it is the current value of the pixel that is being displayed,the pixel values after color inversion;
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,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.
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 classification result is a result obtained by matrix multiplication and addition, 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 maxporoling 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 used for improving the characterization capability of the artificial intelligence algorithm.
Mapping the original data to a characteristic vector space by the convolution layer, the pooling layer and the activation function structure, wherein the full connection layer is obtained by the calculation formula:
wherein,for the mapped feature samples, the learned distributed features are integrated and summarized and then mapped to a sample label space.
Softmax maps the input feature vectors of the 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, 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,the threshold value selected during the binarization of the differential image,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,、andthe three components respectively represent red, green and blue components, and the three component brightness 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,is the pixel value 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 amplitude and verticalA straight gradient magnitude.
Calculating gradient values and gradient directions through a sobel operator, and filtering out a maximum value; eight gradient directions are set, 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 graph, the result obtained by multiplication and addition of the matrixes is the output classification result, and the calculation method comprises the following steps:
wherein,for convolution kernel channel number, sum is matrix addition operationThe character is that,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 the calculation formula is as follows:
Softmax maps the feature vectors of the input neural network to (0,1) space, and the sum of these values is 1, and 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 (8)
1. The utility model provides a rubbish discernment and decision-making 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 picture to a ground workstation by a 5G technology, and further preprocessing the picture;
s1.4: carrying out operation on the preprocessed pictures through an artificial intelligence algorithm, and further realizing the recognition and judgment of the garbage;
in the S1.2, an improved inter-frame difference method is adopted for inter-frame difference processing, and the improved inter-frame difference method utilizes three frames of image information and performs difference operation on images of a current frame and previous and next frames;
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,for the image to be detected;
And then, performing binarization processing on the frame difference image obtained by threshold T processing:
2. The violation garbage identification and determination method based on unmanned aerial vehicle and artificial intelligence 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 colors, and solving the average value of 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:
3. The violation garbage identification and determination method based on unmanned aerial vehicle and artificial intelligence as recited in claim 2, 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; eight gradient directions are set, 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:
4. The violation garbage identification and determination 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.
5. The violation garbage identification and determination method based on unmanned aerial vehicle and artificial intelligence as recited in claim 4, wherein: the convolution calculation is that a small matrix slides on an image or an input characteristic diagram, the output classification result is a result obtained by matrix multiplication and addition, 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;
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 into 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.
6. The violation garbage identification and determination method based on unmanned aerial vehicle and artificial intelligence as recited in claim 4, 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:
7. The violation rubbish identification and determination method based on the unmanned aerial vehicle and the artificial intelligence as recited in claim 4, wherein: softmax maps the input feature vectors of the neural network to (0,1) space, the sum of these values is 1, and the maximum probability value is selected as the classification result:
8. The violation garbage identification and determination method based on unmanned aerial vehicle and artificial intelligence as recited in claim 4, 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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