CN116259092A - Unmanned aerial vehicle aerial photo face recognition method, system, equipment and readable storage medium - Google Patents
Unmanned aerial vehicle aerial photo face recognition method, system, equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle aerial photo face recognition method, an unmanned aerial vehicle aerial photo face recognition system, unmanned aerial vehicle aerial photo face recognition equipment and a readable storage medium. The unmanned aerial vehicle aerial photo face recognition method, system, equipment and readable storage medium of the invention comprise: acquiring image information through unmanned aerial vehicle aerial photography; inputting the image information into a face recognition model, and intercepting a face image; inputting the image information or the face image into a quality evaluation model, and obtaining the score of the image information or the face image; if the score of the image information or the face image information is higher than a preset score threshold, calculating the similarity between the face image and the face features in a face database; and if the similarity exceeds a preset similarity threshold, sending out alarm information. The unmanned aerial vehicle aerial photo face recognition method, the unmanned aerial vehicle aerial photo face recognition system, the unmanned aerial vehicle aerial photo face recognition equipment and the readable storage medium have the advantages that the recognition accuracy is improved, and the probability of judging errors caused by fuzzy comparison of acquired image information is reduced.
Description
Technical Field
The invention relates to the field of image recognition, in particular to an unmanned aerial vehicle aerial photo face recognition method, an unmanned aerial vehicle aerial photo face recognition system, unmanned aerial vehicle aerial photo face recognition equipment and a readable storage medium.
Background
At present, in an urban security intelligent patrol sensitive area, as time goes on, especially in a densely populated personnel gathering area, face detection and recognition are extremely difficult, an unmanned aerial vehicle carrying a visible light camera is deployed in a sensitive key area for patrol, a system automatically captures a face visible light picture of the area in real time, then a manual screening mode is adopted, a background interface of a remote monitoring center can pop up a picture with corresponding equipment at the moment, and whether the face belongs to a face to be compared or not is judged. And alarm. The platform manager then confirms and takes corresponding measures in time.
Judging whether a human face to be identified exists in the visible light slice of the unmanned aerial vehicle inspection, wherein the method of manual inspection or the method of combining the unmanned aerial vehicle with a visible light camera is adopted, and then judging in a manual screening mode, so that the biggest problem is that the urban environment is complex and the screening is difficult; that is, the urban environment has multiple angles, light rays, shielding and other factors, which brings great challenges to intelligent inspection of the city, and leads to false inspection to a certain extent.
Disclosure of Invention
Based on the above, the invention aims to provide an unmanned aerial vehicle aerial photo face recognition method, an unmanned aerial vehicle aerial photo face recognition system, unmanned aerial vehicle aerial photo face recognition equipment and a readable storage medium, which have the advantages of improving recognition accuracy and reducing probability of judging errors caused by fuzzy comparison of acquired image information.
An unmanned aerial vehicle aerial image face recognition method comprises the following steps:
acquiring image information through unmanned aerial vehicle aerial photography;
inputting the image information into a face recognition model, and intercepting a face image;
inputting the image information or the face image into a quality evaluation model, and obtaining the score of the image information or the face image;
if the score of the image information or the face image information is higher than a preset score threshold, calculating the similarity between the face image and the face features in a face database;
and if the similarity exceeds a preset similarity threshold, sending out alarm information.
An unmanned aerial vehicle aerial image face recognition system, comprising:
the information acquisition module is used for acquiring image information through unmanned aerial vehicle aerial photography;
the face recognition module is used for inputting the image information into a face recognition model and intercepting a face image;
the quality evaluation module is used for inputting the image information or the face image into a quality evaluation model to obtain the score of the image information or the face image;
the similarity judging module is used for calculating the similarity of the face image and the face characteristics in the face database if the score of the image information or the face image information is higher than a preset score threshold value;
and the alarm module is used for sending alarm information if the similarity exceeds a preset similarity threshold value.
A computer device, comprising: the unmanned aerial vehicle aerial image face recognition method comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the steps of the unmanned aerial vehicle aerial image face recognition method are realized when the processor executes the computer program.
A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by the processor realizes the steps of the unmanned aerial vehicle aerial image face recognition method.
According to the unmanned aerial vehicle aerial image face recognition method, the unmanned aerial vehicle aerial image is used for acquiring image information, and whether the image information contains a corresponding face image or not is judged through face recognition, quality evaluation and similarity judgment. According to the scheme, the image information is subjected to quality evaluation, the score of the image information is obtained, and whether the image information is subjected to similarity judgment is judged, so that the quality of the image information for similarity judgment is improved, the recognition accuracy is improved, and the probability of judgment errors caused by comparison blurring of the obtained image information is reduced.
For a better understanding and implementation, the present invention is described in detail below with reference to the drawings.
Drawings
Fig. 1 is a flowchart of steps of an unmanned aerial vehicle aerial image face recognition method in an embodiment of the present application;
fig. 2 is a flowchart of steps for acquiring image information by aerial photography of an unmanned aerial vehicle in an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps for capturing a face image according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating steps for calculating the similarity between the face image and the face features in the face database according to the embodiment of the present application;
fig. 5 is a block diagram of an unmanned aerial vehicle aerial image face recognition system in one embodiment in the present application;
fig. 6 is a schematic diagram of a computer device of an unmanned aerial vehicle aerial image face recognition method in an embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Example 1
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of an unmanned aerial vehicle aerial image face recognition method according to an embodiment of the present application.
An unmanned aerial vehicle aerial image face recognition method comprises the following steps:
s101, acquiring image information through unmanned aerial vehicle aerial photography;
s102, inputting the image information into a face recognition model, and intercepting a face image;
s103, inputting the image information or the face image into a quality evaluation model, and obtaining the score of the image information or the face image;
s104, if the score of the image information or the face image information is higher than a preset score threshold, calculating the similarity between the face image and the face characteristics in a face database;
and S105, if the similarity exceeds a preset similarity threshold, sending out alarm information.
According to the unmanned aerial vehicle aerial image face recognition method, the unmanned aerial vehicle aerial image is used for acquiring image information, and whether the image information contains a corresponding face image or not is judged through face recognition, quality evaluation and similarity judgment. According to the scheme, the image information is subjected to quality evaluation, the score of the image information is obtained, and whether the image information is subjected to similarity judgment is judged, so that the quality of the image information for similarity judgment is improved, the recognition accuracy is improved, and the probability of judgment errors caused by comparison blurring of the obtained image information is reduced.
For step S101, obtaining image information by unmanned aerial vehicle aerial photographing;
the unmanned aerial vehicle aerial photographing comprises a unmanned aerial vehicle, a target road section is inspected through the unmanned aerial vehicle, and photographing is carried out in the process. The image information is a picture shot by the unmanned aerial vehicle in the inspection process or a frame of image shot from a video shot by the unmanned aerial vehicle in the inspection process.
In the application, through using unmanned aerial vehicle to take photo by plane, acquire image information, reduced the degree of difficulty that image information obtained, for face identification, quality evaluation and similarity judge provide corresponding image information.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps for acquiring image information by unmanned aerial vehicle aerial photography in an embodiment of the present application. In one embodiment, the aerial photographing of the unmanned aerial vehicle to obtain the image information includes the following steps:
s201, acquiring a route of the unmanned aerial vehicle through route planning software according to a route inspection target road section;
s202, controlling the unmanned aerial vehicle to patrol according to the route of the unmanned aerial vehicle, and acquiring image information.
For step S201, acquiring a route of the unmanned aerial vehicle according to the patrol target road section through route planning software;
the inspection target road section is a target road section of the unmanned aerial vehicle, for example, in the process of urban intelligent inspection, a road needing inspection is used as the inspection target road section.
The route planning software is used for planning a route and ensuring that the unmanned aerial vehicle can fly normally. In the embodiment of the present application, the route planning software is not limited. And route planning software of the unmanned aerial vehicle route can be obtained according to the patrol target road section, and can be used for route planning in the application.
For step S202, controlling the unmanned aerial vehicle to patrol according to the route of the unmanned aerial vehicle, and obtaining image information;
after acquiring the route of the unmanned aerial vehicle, the unmanned aerial vehicle is used for carrying out inspection on the inspection target road section according to the route, and in the inspection process, aerial photographing is carried out through a camera device arranged on the unmanned aerial vehicle to acquire image information. In one embodiment, the image information includes a plurality of frames of images captured by capturing an image through an imaging device provided on the unmanned aerial vehicle, or by capturing a video of the inspection target road section through the imaging device on the unmanned aerial vehicle.
For step S102, inputting the image information into a face recognition model, and intercepting a face image;
wherein the face recognition model is a model for capturing face images in the image information, and in one embodiment, the face recognition model comprises a YOLOv5 recognition model. And the face image is intercepted through the face recognition model, so that the influence of the background image on the judgment result is reduced when the image similarity is judged, and the judgment accuracy is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating steps for capturing a face image according to an embodiment of the present application. In one embodiment, the inputting the image information into a face recognition model, and capturing a face image, includes the following steps:
s301, adjusting the size of the image information to a preset size, and inputting the size to a basic framework in a face recognition model;
s302, judging whether a face image exists in the image information, and if so, intercepting the face image.
For step S301, the size of the image information is adjusted to a preset size, and the preset size is input to a basic architecture in a face recognition model;
in this embodiment, the face recognition model used is a YOLOv5 recognition model, and in this application, the YOLOv5 recognition model first adjusts the size of the image information to a preset size during the working process, where the preset size is a fixed image size that is set in advance, and in one embodiment, the preset size is 640×640. By adjusting the image information to a preset size, the consistency of the sizes of the images to be identified is ensured, and the probability of error identification is reduced.
And after the image information is adjusted to be of a preset size, inputting the image information into a basic framework of a Yolov5 recognition model, and extracting a human face.
For step S302, judging whether a face image exists in the image information, and if so, intercepting the face image;
wherein the face image is a portion including a face in the image information. By intercepting the face image in the image information, the interference of the background image of the image information on the recognition in the face recognition process is reduced, and the recognition accuracy is improved.
Inputting the image information into a basic framework of a Yolov5 recognition model, judging whether a face image exists in the image information, and intercepting the face image if the face image exists in the image information. In one embodiment, the specific operation is as follows: and performing convolution adjustment extraction on the image information by using the basic framework of the YOLOv5 recognition model, selecting a target area with a face by a prediction frame, removing redundant frames in a non-maximum suppression mode, retaining a final prediction result, giving out target frame coordinates, and intercepting the face image according to the target frame coordinates.
By using the face recognition model, the face image is intercepted from the image information, so that the interference of the background image in the image information when the face recognition is carried out on the image information is reduced.
For step S103, inputting the image information or the face image into a quality evaluation model to obtain the score of the image information or the face image;
the quality evaluation model is a model for scoring the image information or the face image.
In one embodiment, the inputting the image information or the face image into the quality evaluation model, and obtaining the score of the image information or the face image includes the following steps:
the image information or face image is scored according to brightness, color, contrast, noise, and sharpness of the image.
In this embodiment, the quality evaluation model scores image information or face images input into the quality evaluation model according to brightness, color, contrast, noise, and sharpness of the images. In one embodiment, the backbone network of the quality assessment model is based on ResNet-50, changing the final fully connected layer to one output, and removing the softmax activation function.
For step S104, if the score of the image information or the face image information is higher than a preset score threshold, calculating the similarity between the face image and the face features in the face database;
the preset score threshold is a score threshold which is set in advance and used for judging image quality. In this embodiment of the present application, the similarity determination is performed only when the score of the image information or the face image information is higher than a preset score threshold.
Referring to fig. 4, fig. 4 is a flowchart illustrating steps of calculating the similarity between the face image and the face feature in the face database according to the embodiment of the present application. In one embodiment, calculating the similarity of the face image to the face features in the face database comprises the steps of:
s401, inputting the face image into a face feature extraction model, and extracting a face feature vector corresponding to the face image;
s402, calculating the similarity between the face feature vector and the face feature in the face database through a support vector machine.
For step S401, inputting the face image into a face feature extraction model, and extracting a face feature vector corresponding to the face image;
the face feature extraction model is used for extracting feature vectors in the face image, and in one embodiment, the face feature extraction model comprises a facenet face feature extraction model.
And extracting the feature vector of the face image through the face feature extraction model.
For step S402, calculating the similarity between the face feature vector and the face feature in the face database by using a support vector machine;
the support vector machine is a classifier developed by a generalized portrait algorithm in pattern recognition and is mainly applied to situations such as portrait recognition and text classification. The face database comprises a plurality of face features for judging whether a target image exists in the face image. For example, in the case of city intelligent patrol, the face database may be set to include face features of criminals, so that it may be determined whether the criminals exist in the target population during face recognition.
In the embodiment of the application, the face feature vector of the face image is obtained by using the facenet face feature extraction model, and then the similarity between the face feature vector and the face features in the face database is calculated by using the support vector machine.
For step S105, if the similarity exceeds a preset similarity threshold, an alarm message is sent.
The similarity threshold is a threshold which is set in advance and used for judging whether alarm information needs to be sent out or not. When the similarity exceeds the similarity threshold, indicating that the face features in the face database exist in the face image, sending out alarm information so as to timely grab the target person.
In one embodiment, the unmanned aerial vehicle aerial image face recognition method further comprises the following steps:
acquiring image information of different weather in different time periods through aerial photography of the unmanned aerial vehicle;
and labeling the face image in the image information, inputting the image information and the face image into the face recognition model, and training the face recognition model.
The face recognition model is trained by acquiring image information of different weather in different time periods, and the face recognition model is mainly used for improving accuracy of the face recognition model.
In one embodiment, the unmanned aerial vehicle aerial image face recognition method further comprises the following steps:
according to the face image identified by the face recognition model, a data set corresponding to a face feature extraction model is manufactured;
and training the face feature extraction model by combining the data set and the Asian face data set.
The face feature extraction model is trained by using the data set made of the face image identified by the face recognition model and combining with the Asian face data set, so that the face feature vector corresponding to the face image can be rapidly and accurately extracted in the working process of the face feature extraction model.
According to the unmanned aerial vehicle aerial image face recognition method, the unmanned aerial vehicle aerial image is used for acquiring image information, and whether the image information contains a corresponding face image or not is judged through face recognition, quality evaluation and similarity judgment. According to the scheme, the image information is subjected to quality evaluation, the score of the image information is obtained, and whether the image information is subjected to similarity judgment is judged, so that the quality of the image information for similarity judgment is improved, the recognition accuracy is improved, and the probability of judgment errors caused by comparison blurring of the obtained image information is reduced.
Example 2
Compared with embodiment 1, the main difference of this embodiment is that:
inputting the image information into a quality evaluation model to obtain the score of the image information;
and if the score of the image information is higher than a preset score threshold, inputting the image information into a face recognition model, and intercepting a face image.
And grading the acquired image information through the quality evaluation model, judging the image information according to the grading of the image information, and inputting the image information into a face recognition model to intercept a face image when the grading of the image information is higher than a preset score threshold.
Compared with the embodiment 1, the quality evaluation is firstly carried out and then the face image extraction is carried out, so that the operation of intercepting the face image for unnecessary image information is reduced, and the efficiency of the unmanned aerial vehicle aerial image face recognition method is improved.
Referring to fig. 5, fig. 5 is a block diagram of an unmanned aerial vehicle aerial image face recognition system in an embodiment in the present application, and the present application further provides an unmanned aerial vehicle aerial image face recognition system, including:
the information acquisition module 11 is used for acquiring image information through unmanned aerial vehicle aerial photography;
the face recognition module 12 is used for inputting the image information into a face recognition model and intercepting a face image;
a quality evaluation module 13, configured to input the image information or the face image into a quality evaluation model, and obtain a score of the image information or the face image;
the similarity judging module 14 is configured to calculate a similarity between the face image and the face feature in the face database if the score of the image information or the face image information is higher than a preset score threshold;
and the alarm module 15 is used for sending alarm information if the similarity exceeds a preset similarity threshold.
Referring to fig. 6, fig. 6 is a schematic diagram of a computer device of an unmanned aerial vehicle aerial image face recognition method in an embodiment of the present application. As shown in fig. 4, the computer device 21 includes: a processor 211, a memory 212 and a computer program 213 stored in said memory 212 and executable on said processor 211, for example: unmanned aerial vehicle aerial image face recognition program; the processor 211 executes the computer program 213 to implement the unmanned aerial vehicle aerial image face recognition method according to the above embodiment.
Wherein the processor 211 may include one or more processing cores. The processor 211 performs various functions of the computer device 21 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 212, and invoking data in the memory 212, using various interfaces and lines connecting various parts within the computer device 21, alternatively, the processor 211 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 211 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the touch display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 211 and may be implemented by a single chip.
The Memory 212 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 212 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 212 may be used to store instructions, programs, code, sets of codes, or instruction sets. The memory 212 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 212 may also optionally be at least one memory device located remotely from the aforementioned processor 211.
The embodiment of the present application further provides a computer readable storage medium, where a plurality of instructions may be stored, where the instructions are suitable for being loaded by a processor and executed by a processor, and a specific execution process may refer to a specific description of the foregoing embodiment, which is not repeated herein.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (10)
1. The unmanned aerial vehicle aerial image face recognition method is characterized by comprising the following steps of:
acquiring image information through unmanned aerial vehicle aerial photography;
inputting the image information into a face recognition model, and intercepting a face image;
inputting the image information or the face image into a quality evaluation model, and obtaining the score of the image information or the face image;
if the score of the image information or the face image information is higher than a preset score threshold, calculating the similarity between the face image and the face features in a face database;
and if the similarity exceeds a preset similarity threshold, sending out alarm information.
2. The unmanned aerial vehicle aerial image face recognition method according to claim 1, wherein the step of acquiring image information through unmanned aerial vehicle aerial image comprises the following steps:
acquiring a route of the unmanned aerial vehicle through route planning software according to the patrol target road section;
and controlling the unmanned aerial vehicle to patrol according to the route of the unmanned aerial vehicle, and acquiring image information.
3. The unmanned aerial vehicle aerial image face recognition method according to claim 1, wherein the steps of inputting image information into a face recognition model and capturing a face image comprise the following steps:
the size of the image information is adjusted to be a preset size, and the preset size is input into a basic framework in a face recognition model;
judging whether a face image exists in the image information, and if so, intercepting the face image.
4. The unmanned aerial vehicle aerial image face recognition method according to claim 1, wherein the step of inputting the image information or the face image into a quality evaluation model to obtain the score of the image information or the face image comprises the following steps:
the image information or face image is scored according to brightness, color, contrast, noise, and sharpness of the image.
5. The unmanned aerial vehicle aerial image face recognition method according to claim 1, wherein the calculating the similarity of the face image and the face features in the face database comprises the following steps:
inputting the face image into a face feature extraction model, and extracting a face feature vector corresponding to the face image;
and calculating the similarity between the face feature vector and the face features in the face database through a support vector machine.
6. The unmanned aerial vehicle aerial image face recognition method of claim 1, further comprising the steps of:
acquiring image information of different weather in different time periods through aerial photography of the unmanned aerial vehicle;
and labeling the face image in the image information, inputting the image information and the face image into the face recognition model, and training the face recognition model.
7. The unmanned aerial vehicle aerial image face recognition method of claim 5, further comprising the steps of:
according to the face image identified by the face recognition model, a data set corresponding to a face feature extraction model is manufactured;
and training the face feature extraction model by combining the data set and the Asian face data set.
8. Unmanned aerial vehicle aerial image face recognition system, characterized by comprising:
the information acquisition module is used for acquiring image information through unmanned aerial vehicle aerial photography;
the face recognition module is used for inputting the image information into a face recognition model and intercepting a face image;
the quality evaluation module is used for inputting the image information or the face image into a quality evaluation model to obtain the score of the image information or the face image;
the similarity judging module is used for calculating the similarity of the face image and the face characteristics in the face database if the score of the image information or the face image information is higher than a preset score threshold value;
and the alarm module is used for sending alarm information if the similarity exceeds a preset similarity threshold value.
9. A computer device, comprising: processor, memory and computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the unmanned aerial vehicle aerial image recognition method according to any of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor performs the steps of the unmanned aerial vehicle aerial image face recognition method according to any one of claims 1 to 7.
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