CN112466024B - Intelligent epidemic prevention system - Google Patents
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Abstract
The invention provides an intelligent epidemic prevention system which comprises an intelligent inductive switch, an information storage module, an image acquisition module, a face recognition module, an iris recognition module, a body temperature detection module, an information processing module, a voice module and a passing device.
Description
Technical Field
The invention relates to the technical field of epidemic prevention detection, in particular to an intelligent epidemic prevention system.
Background
The traditional epidemic prevention mode only depends on manual operation, so that huge manpower is consumed, pressure is brought to personnel movement of each cell, and zero contact cannot be achieved.
Today, the technology is rapidly developing, and intelligent products are more and more. The micro-epidemic prevention entrance guard system based on the raspberry pi is a product of times development and receives more and more attention so as to facilitate work of people and control places such as entrance and exit of people by an epidemic situation recognition device.
There are many work scenes usually manual operation can waste some artifical costs to some environment manual works also can not observe 24 hours, and the manual work is high again, and consumes time, and is very dangerous, has increased the risk that the epidemic situation is infected. Therefore, an intelligent epidemic prevention system is provided.
The system mainly uses an artificial intelligence technology and integrates an intelligent epidemic prevention system which can be used for entrance guard of districts, office buildings and other places.
Disclosure of Invention
In order to at least partially solve the problems, the invention provides an intelligent epidemic prevention system, which comprises an intelligent inductive switch, an information storage module, an image acquisition module, a face recognition module, a body temperature detection module, an information processing module, a voice module and a passing device, wherein the intelligent inductive switch is connected with the information storage module;
the intelligent inductive switch is used for starting the intelligent epidemic prevention system when people arrive at the entrance;
the information storage module is used for inputting and storing the iris data characteristic diagram of the personnel in the community;
the image acquisition module is used for acquiring a face image and an iris image of a person at an entrance by a camera;
the face recognition module is used for recognizing the face image of the person and judging whether the forehead and the mask are worn correctly or not;
the body temperature detection module is used for carrying out infrared body temperature detection on the forehead of a person at the inlet;
the information processing module is used for receiving the information of the face recognition module and the body temperature detection module, and transmitting an opening command to the passing device when the entrance personnel meet the requirement that the community personnel have normal body temperature and correctly wear the mask;
the voice module is used for carrying out voice explanation or reminding on the personnel according to the reason that the passing device is not started;
and the passing device is used for receiving the opening instruction sent by the signal processing module to open the passing device and closing the passing device after the personnel pass through the passing device.
Further, the information storage module performs the following operations:
iris data characteristic diagrams of personnel in the community are collected, and left and right eye attributes are added;
and storing the collected iris data characteristic map in a database in a K-nearest neighbor mode.
Further, the face recognition module analyzes the real-time image of the face of the person at the entrance by adopting a deep learning-based image recognition method to determine whether the person at the entrance wears the mask correctly.
Further, the face recognition module performs the following operations:
collecting images of a correctly worn mask, an incorrectly worn mask, and a person's face forehead
Carrying out rectangular frame labeling on the acquired image, respectively carrying out rectangular frame labeling on a forehead image, a correct mask wearing image and an incorrect mask wearing image of a person, and dividing labeled image information into a training set and a test set;
establishing a deep neural network target detection model, using the training set and the test set images for model training and testing, and obtaining a trained deep neural network target detection model;
and (3) applying the trained deep neural network target detection model to face image recognition, and judging the forehead area of the face and whether the mask is worn correctly.
Further, the system also comprises an iris recognition module, wherein the iris recognition module performs the following operations:
acquiring an iris image of a person at an entrance acquired by an image acquisition module;
and comparing the obtained iris image of the person at the entrance with the iris data characteristic diagram stored in the database, and judging whether the person is the person in the cell according to the comparison result.
Further, the comparing the obtained iris image of the person at the entrance with the iris data feature map stored in the database includes:
step A1, judging abnormal pixel points in the iris image of the person at the entrance by the following formula, and rejecting the abnormal pixel points:
wherein, P (x)i) RepresentsProbability that the ith pixel point in the iris image of the person at the entrance is an abnormal pixel point, pi and e represent natural constants, a represents a pixel difference value, and x representsiThe i-th pixel value in the iris image representing the person at the entrance, i being 1, 2, … n, if P (x)i)>0.5 is an abnormal pixel point, and the abnormal pixel point is modified into the average value in the adjacent area;
step A2, obtaining the iris image of the person at the entrance with the abnormal pixel points removed, integrating the image data into a data feature vector, integrating the iris data feature map stored in the database into a data feature vector, and determining the similarity between the iris image and the data feature vector according to the following formula:
wherein sim (C, D) represents the similarity between the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed and the feature vector corresponding to the iris data feature map stored in the database, C represents the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed, and C ═ β1、β2……βn) D represents a feature vector corresponding to the iris data feature map stored in the database, and D ═ γ1、γ2……γn) If the similarity sim (C, D) is more than or equal to 97 percent, determining that the person at the entrance is the person in the community, and if the similarity sim (C, D)<And 97%, judging that the personnel at the entrance is not the personnel in the community.
Further, the body temperature detection module performs the following operations:
and detecting the body temperature by adopting infrared detection according to the forehead area of the face, which is judged by the face recognition module, and conventionally sending information to the information processing module.
Furthermore, the information processing module is also used for receiving the information of the iris identification module, and transmitting an opening command to the passing device when the entrance personnel are the personnel in the community, the body temperature is normal and the mask is worn correctly.
Furthermore, the reason that the passing device in the voice module is not opened comprises abnormal body temperature, people in the community and incorrect mask wearing.
Furthermore, when epidemic prevention and control are not needed, all or part of functions of the image acquisition module, the face recognition module, the body temperature detection module, the voice module, the information processing module and the iris recognition module are selected to be turned off.
Compared with the prior art, the invention has the beneficial effects that: the invention provides an intelligent epidemic prevention system which comprises an intelligent inductive switch, an information storage module, an image acquisition module, a face recognition module, an iris recognition module, a body temperature detection module, an information processing module, a voice module and a passing device.
The following description of the preferred embodiments for carrying out the present invention will be made in detail with reference to the accompanying drawings so that the features and advantages of the present invention can be easily understood.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly described below. Wherein the drawings are only for purposes of illustrating some embodiments of the invention and are not to be construed as limiting the invention to all embodiments thereof.
Fig. 1 is a block diagram of an intelligent epidemic prevention system according to the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, the technical problem to be solved by the present invention is to provide an intelligent epidemic prevention system, which comprises an intelligent inductive switch, an information storage module, an image acquisition module, a face recognition module, a body temperature detection module, an information processing module, a voice module and a passing device;
the intelligent inductive switch is used for starting the intelligent epidemic prevention system when people arrive at the entrance;
the information storage module is used for inputting and storing the iris data characteristic diagram of the personnel in the community;
the image acquisition module is used for acquiring a face image and an iris image of a person at an entrance by a camera;
the face recognition module is used for recognizing the face images of the personnel and judging the forehead, whether the mask is worn correctly or not and whether the person is the person in the community or not;
the body temperature detection module is used for carrying out infrared body temperature detection on the forehead of a person at the inlet;
the information processing module is used for receiving the information of the face recognition module and the body temperature detection module, and transmitting an opening command to the passing device when the entrance personnel meet the requirement that the community personnel have normal body temperature and correctly wear the mask;
the voice module is used for carrying out voice explanation or reminding on the personnel according to the reason that the passing device is not started;
and the passing device is used for receiving the opening instruction sent by the signal processing module to open the passing device and closing the passing device after the personnel pass through the passing device.
The working principle of the technical scheme is as follows: the intelligent inductive switch adopts a microwave radar and an infrared sensor, and the intelligent epidemic prevention system is started when a person reaches an entrance; an information storage module in the intelligent epidemic prevention system inputs and stores iris data characteristic diagrams of personnel in the community, the iris data characteristic diagrams of the personnel in the community are collected, left and right eye attributes are added, and the collected iris data characteristic diagrams are stored in a database in a K neighbor mode; the image acquisition module is a camera arranged at the entrance and is used for acquiring a face image and an iris image of a person at the entrance; identifying the face image of the person through a face identification module, and judging whether the forehead area of the person correctly wears the mask or not; the body temperature detection module measures the body temperature of the forehead area of a person at an inlet based on infrared detection, and information is traditionally sent to the information processing module; the intelligent epidemic prevention system also comprises an iris recognition module used for judging whether the personnel is the personnel in the community, the iris image of the personnel at the entrance acquired by the image acquisition module is acquired, the acquired iris image of the personnel at the entrance is compared with the iris data characteristic diagram stored in the database in terms of characteristics, and whether the personnel is the personnel in the community is judged according to the comparison result; the information processing module receives information of the face recognition module, the iris recognition module and the body temperature detection module, and when the personnel in the community meet that the body temperature is normal and the mask is worn correctly, the entrance personnel transmit an opening command to the passing device, and when one of the personnel in the community does not meet the requirement, the opening command cannot be transmitted; the passing device is started after receiving the starting instruction sent by the signal processing module, and is closed after the personnel pass through the passing device; the face recognition module analyzes a real-time image of the face of a person at an entrance by adopting a deep learning-based image recognition method, and determines whether the person at the entrance correctly wears the mask; the reason why the communication device in the voice module is not started comprises abnormal body temperature, people in the community and incorrect mask wearing; when the intelligent epidemic prevention system does not need to control epidemic situation, all or part of functions of the image acquisition module, the face identification module, the body temperature detection module, the voice module, the information processing module and the iris identification module can be selected to be closed.
The beneficial effects of the above technical scheme are that: the technical proposal integrates a set of intelligent epidemic prevention system which can be used for entrance guard of districts, office buildings and other places, the comparison speed is accelerated by adopting a machine learning algorithm K nearest neighbor mode in storing the iris data characteristic diagram, meanwhile, the system adopts a deep neural network target detection model, not only carries out classification and identification on image categories, but also can position image category areas, so that the positions of the forehead areas of the personnel can be accurately determined, thereby carrying out body temperature detection, the system can intelligently identify and only allow people wearing the mask correctly, having normal body temperature and entering the community, so that the passing device can only pass one person at a time, thereby limiting the number of the personnel, avoiding the over-dense personnel, eliminating the potential safety hazard of mutual infection among the personnel, through the system, the workload of epidemic situation prevention and control personnel is reduced, and the epidemic situation prevention and control efficiency is improved.
In an embodiment provided by the present invention, the face recognition module performs the following operations:
collecting images of a correct mask, an incorrect mask and the forehead of the face of a person;
carrying out rectangular frame labeling on the acquired image, respectively carrying out rectangular frame labeling on a forehead image, a correct mask wearing image and an incorrect mask wearing image of a person, and dividing labeled image information into a training set and a test set;
establishing a deep neural network target detection model, using the training set and the test set images for model training and testing, and obtaining a trained deep neural network target detection model;
and (3) applying the trained deep neural network target detection model to face image recognition, and judging the forehead area of the face and whether the mask is worn correctly.
The working principle of the technical scheme is as follows: firstly, collecting images of a correctly worn mask, an incorrectly worn mask and the forehead of the face of a person; then, carrying out rectangular frame labeling on the acquired image, and respectively carrying out rectangular frame labeling on the forehead image, the correct mask wearing image and the incorrect mask wearing image of the person, wherein the mask is not worn correctly (such as exposed nose, exposed nose and mouth), and dividing the labeled image information into a training set and a test set; secondly, establishing a deep neural network target detection model, and using the training set and the test set images for model training and testing to obtain a trained deep neural network target detection model; and finally, the trained deep neural network target detection model is used for face image recognition, the forehead area of the face and whether the mask is worn correctly are judged, and the prediction accuracy of the trained deep neural network target detection model representation model on the test set is higher than 99%.
The beneficial effects of the above technical scheme are that: according to the technical scheme, the deep neural network target detection model is used for face image recognition, CNN convolution extraction is adopted for the face image, so that image characteristics in a rectangular frame can be better learned, BP reverse iteration training is adopted for the neural network, parameters in the network can be better fitted, and the prediction accuracy of the deep neural network target detection model is greatly improved.
In an embodiment of the present invention, the comparing, by the iris recognition module, the acquired iris image of the person at the entrance with the iris data feature map stored in the database includes:
step A1, judging abnormal pixel points in the iris image of the person at the entrance by the following formula, and rejecting the abnormal pixel points:
wherein, P (x)i) Representing the probability that the ith pixel point in the iris image of the person at the entrance is an abnormal pixel point, pi, e represent natural constants, a represents a pixel difference value, and x represents the probability that the ith pixel point is an abnormal pixel pointiThe i-th pixel value in the iris image representing the person at the entrance, i being 1, 2, … n, if P (x)i)>0.5 is an abnormal pixel point, and the abnormal pixel point is modified into the average value in the adjacent area;
step A2, obtaining the iris image of the person at the entrance with the abnormal pixel points removed, integrating the image data into a data feature vector, integrating the iris data feature map stored in the database into a data feature vector, and determining the similarity between the iris image and the data feature vector according to the following formula:
wherein sim (C, D) represents the similarity between the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed and the feature vector corresponding to the iris data feature map stored in the database, C represents the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed, and C ═ β1、β2……βn) D represents a feature vector corresponding to the iris data feature map stored in the database, and D ═ γ1、γ2……γn) If the similarity sim (C, D) is more than or equal to 97 percent, judging that the personnel at the entrance is the personnel in the community, and if the similarity sim (C, D)<And 97%, judging that the personnel at the entrance is not the personnel in the community.
The working principle of the technical scheme is as follows: firstly, judging abnormal pixel points in an iris image of a person at an entrance, and removing the abnormal pixel points, wherein the removing mode is replaced by using a pixel mean value in an adjacent area; then, the iris images of the personnel at the entrance with the abnormal pixel points removed are integrated into a data characteristic vector, the iris data characteristic maps stored in the database are also integrated into a data characteristic vector, the iris data characteristic maps and the data characteristic vector are subjected to similarity comparison, so that whether the personnel are the personnel in the cell is judged, the personnel in the cell are released, the personnel not in the cell and the personnel entering the cell for the first time are input into the system, the system inputs identity information, residence information and health condition, the time to be registered and returned from the province is judged, whether the personnel pass the system is judged, and the personnel returned from the province and the high-risk area are not released.
The beneficial effects of the above technical scheme are that: abnormal pixel points in the iris image of the personnel at the entrance acquired by the technology are modified into a mean value in the adjacent region, so that the situation that the identification rate of the iris identification module is influenced by the abnormal pixel points is avoided, the iris identification module is further checked, the safety performance is further improved, the personnel in the community are prevented from entering, the contrast similarity acquired by the similarity calculation formula through the algorithm can effectively improve the accuracy of similarity judgment, and the identification accuracy of the iris identification module is further improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle scope of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent epidemic prevention system is characterized in that: the system comprises an intelligent inductive switch, an information storage module, an image acquisition module, a face recognition module, a body temperature detection module, an information processing module, a voice module and a passing device;
the intelligent inductive switch is used for starting the intelligent epidemic prevention system when people arrive at the entrance;
the information storage module is used for inputting and storing the iris data characteristic diagram of the personnel in the community;
the image acquisition module is used for acquiring a face image and an iris image of a person at an entrance by a camera;
the face recognition module is used for recognizing the face image of the person and judging whether the forehead and the mask of the person are worn correctly;
the body temperature detection module is used for carrying out infrared body temperature detection on the forehead of a person at the inlet;
the information processing module is used for receiving the information of the face recognition module and the body temperature detection module, and transmitting an opening command to the passing device when the entrance personnel meet the requirement that the community personnel have normal body temperature and correctly wear the mask;
the voice module is used for carrying out voice explanation or reminding on the personnel according to the reason that the passing device is not started;
the passing device is used for receiving the opening instruction sent by the signal processing module to open the passing device and closing the passing device after the personnel pass through the passing device;
the system also includes an iris recognition module that performs the operations of:
acquiring an iris image of a person at an entrance acquired by an image acquisition module;
comparing the obtained iris image of the person at the entrance with the iris data characteristic diagram stored in the database, and judging whether the person is the person in the cell according to the comparison result;
the characteristic comparison is carried out to the iris image of personnel of entrance that will acquire and the iris data characteristic map of storing in the database, includes:
step A1, judging abnormal pixel points in the iris image of the personnel at the entrance by the following formula, and removing:
wherein, P (x)i) Representing the probability that the ith pixel point in the iris image of the person at the entrance is an abnormal pixel point, pi, e represent natural constants, a represents a pixel difference value, and x represents the probability that the ith pixel point is an abnormal pixel pointiThe i-th pixel point pixel value in the iris image representing the person at the entrance, i ═ 1, 2,. n, if P (x)i)>0.5 is an abnormal pixel point, and the abnormal pixel point is modified into the average value in the adjacent area;
step A2, obtaining the iris image of the person at the entrance with the abnormal pixel points removed, integrating the image data into a data feature vector, integrating the iris data feature map stored in the database into a data feature vector, and determining the similarity between the iris image and the data feature vector according to the following formula:
wherein sim (C, D) represents the similarity between the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed and the feature vector corresponding to the iris data feature map stored in the database, C represents the data feature vector of the iris image of the person at the entrance where the abnormal pixel is removed, and C ═ β1、β2......βn) And D represents the number of irises stored in the databaseAccording to the feature vector corresponding to the feature map, and D ═ gamma1、γ2......γn) If the similarity sim (C, D) is more than or equal to 97 percent, determining that the personnel at the inlet is the personnel in the community, and if the similarity sim (C, D)<And 97%, judging that the personnel at the entrance is not the personnel in the community.
2. The intelligent epidemic prevention system of claim 1, wherein the information storage module performs the following operations:
iris data characteristic diagrams of personnel in the community are collected, and left and right eye attributes are added;
and storing the collected iris data characteristic map in a database in a K-nearest neighbor mode.
3. The intelligent epidemic prevention system according to claim 1, wherein the face recognition module analyzes the real-time image of the face of the person at the entrance by using a deep learning-based image recognition method to determine whether the person at the entrance wears the mask correctly.
4. The intelligent epidemic prevention system of claim 3, wherein the face recognition module performs the following operations:
collecting images of a correct mask, an incorrect mask and the forehead of the face of a person;
carrying out rectangular frame labeling on the acquired image, respectively carrying out rectangular frame labeling on a forehead image, a correct mask wearing image and an incorrect mask wearing image of a person, and dividing labeled image information into a training set and a test set;
establishing a deep neural network target detection model, and using the training set and the test set images for model training and testing to obtain the trained deep neural network target detection model;
and (3) applying the trained deep neural network target detection model to face image recognition, and judging the forehead area of the face and whether the mask is worn correctly.
5. The intelligent epidemic prevention system of claim 1, wherein the body temperature detection module performs the following operations:
and detecting the body temperature by adopting infrared detection according to the forehead area of the face, which is judged by the face recognition module, and conventionally sending information to the information processing module.
6. The intelligent epidemic prevention system of claim 1, wherein the information processing module is further configured to receive information from the iris recognition module, and to transmit an opening command to the access device when the entrance staff are the community staff and the body temperature is normal and the mask is worn correctly.
7. The intelligent epidemic prevention system according to claim 1, wherein the reasons for the traffic device not being turned on in the voice module include abnormal body temperature, not the person in the community, and incorrect wearing of the mask.
8. The intelligent epidemic prevention system of claim 1, wherein when epidemic prevention and control are not required, all or part of the functions of the image acquisition module, the face recognition module, the body temperature detection module, the voice module, the information processing module and the iris recognition module are selectively turned off.
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