CN111681770B - Intelligent detection method and device for abnormal target, computer equipment and storage medium - Google Patents
Intelligent detection method and device for abnormal target, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111681770B CN111681770B CN202010296948.1A CN202010296948A CN111681770B CN 111681770 B CN111681770 B CN 111681770B CN 202010296948 A CN202010296948 A CN 202010296948A CN 111681770 B CN111681770 B CN 111681770B
- Authority
- CN
- China
- Prior art keywords
- target person
- target
- body temperature
- abnormal
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 84
- 238000001514 detection method Methods 0.000 title claims abstract description 67
- 230000036760 body temperature Effects 0.000 claims abstract description 181
- 230000001815 facial effect Effects 0.000 claims abstract description 44
- 230000007613 environmental effect Effects 0.000 claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 19
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000002372 labelling Methods 0.000 claims description 11
- 230000000241 respiratory effect Effects 0.000 claims description 11
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 208000015181 infectious disease Diseases 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 8
- 241000711573 Coronaviridae Species 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000009529 body temperature measurement Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 206010020843 Hyperthermia Diseases 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 208000021760 high fever Diseases 0.000 description 1
- 230000036031 hyperthermia Effects 0.000 description 1
- 230000002631 hypothermal effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000037000 normothermia Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0022—Radiation pyrometry, e.g. infrared or optical thermometry for sensing the radiation of moving bodies
- G01J5/0025—Living bodies
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The application discloses an intelligent detection method, device, computer equipment and storage medium for abnormal targets, which are applied to the technical field of target detection and are used for solving the technical problems of low detection efficiency and high infection risk of people in the prior art. The method provided by the application comprises the following steps: measuring the target body temperature of a target person through an infrared camera, and acquiring a facial image of the target person; acquiring the distance between the infrared camera and the target person and the value of the current environmental factor; calculating a temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor; calculating to obtain the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature; analyzing the facial image and judging whether the target person wears a mask or not; when the real body temperature is abnormal and/or the target person does not wear the mask, judging that the target person is an abnormal target person, and sending out alarm reminding.
Description
Technical Field
The present application relates to the field of object detection technologies, and in particular, to an intelligent detection method and apparatus for an abnormal object, a computer device, and a storage medium.
Background
Under the influence of novel coronaviruses, people are required to be detected in public places and communities to reduce epidemic risks in places where people are concentrated. The existing detection method mainly comprises manual detection, and an electronic body temperature gun is held by a worker to detect the body temperature of people in the past one by one.
The detection efficiency of the existing detection method is low, the labor cost is too high, the travel efficiency of the masses is not improved, and the mass experience is low. And the health condition detection is carried out on the human body by the human body, so that the infection risk of the novel coronavirus is also improved.
There is a need to propose a method for intelligently detecting the physical condition of people in the past and the next.
Disclosure of Invention
The embodiment of the application provides an intelligent detection method, an intelligent detection device, computer equipment and a storage medium for abnormal targets, which are used for solving the technical problems of low detection efficiency and high infection risk of people in the prior art.
According to one aspect of the application, an intelligent detection method for an abnormal target comprises the following steps:
measuring the target body temperature of a target person through an infrared camera, and acquiring a facial image of the target person;
acquiring the distance between the infrared camera and the target person and the value of the current environmental factor;
calculating a temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor;
calculating to obtain the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature;
analyzing the facial image and judging whether the target person wears a mask or not;
when the real body temperature is abnormal and/or the target person does not wear the mask, judging that the target person is an abnormal target person, and sending out alarm reminding.
According to another aspect of the present application, there is provided an intelligent detection apparatus for an abnormal target, comprising:
the target body temperature measuring module is used for measuring the target body temperature of a target person through the infrared camera and acquiring a facial image of the target person;
the influence factor value module is used for acquiring the distance between the infrared camera and the target person and the value of the current environmental factor;
the compensation coefficient calculation module is used for calculating a temperature compensation coefficient according to the distance and the value of the current environmental factor;
the real body temperature calculation module is used for calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature;
the judging module is used for analyzing the facial image and judging whether the target person wears a mask or not;
and the alarm module is used for judging the target person to be an abnormal target person and sending out alarm reminding when the real body temperature is abnormal and/or the target person does not wear the mask.
According to a further aspect of the present application there is provided a computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the intelligent detection method of abnormal objects described above when said computer program is executed.
According to a further aspect of the present application there is provided a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the intelligent detection method of abnormal objects as described above.
According to the intelligent detection method, the intelligent detection device, the computer equipment and the storage medium for the abnormal target, the infrared camera is used for measuring the target body temperature of the target person, the facial image of the target person is obtained, the distance between the infrared camera and the target person and the value of the current environmental factor are obtained, the temperature compensation coefficient is calculated according to the target body temperature, the distance and the value of the current environmental factor, the real body temperature of the target person is obtained according to the temperature compensation coefficient and the target body temperature calculation, the facial image is analyzed, whether the target person wears the mask is judged, when the real body temperature is abnormal and/or the target person does not wear the mask, the target person is judged to be the abnormal target person, alarm reminding is sent out, the whole process is automatically carried out, participation of staff is not needed, the propagation path of novel coronavirus is reduced, and the detection efficiency of the health condition of the masses is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic illustration of an environment of use for an embodiment of the present application;
FIG. 2 is a flow chart of a method for intelligent detection of an abnormal target according to an embodiment of the application;
FIG. 3 is a schematic diagram of an intelligent detection device for abnormal objects according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a computer device in accordance with an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. Based on the embodiments of the present application, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of the present application.
The intelligent detection method of the abnormal target provided by the application can be applied to an application environment as shown in fig. 1, wherein computer equipment comprises, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable equipment. The computer equipment is externally connected with an infrared camera, and the infrared camera is used for measuring the target body temperature of a target person and acquiring the facial image of the target person.
In one embodiment, as shown in fig. 2, an intelligent detection method of an abnormal target is provided, which includes the following steps 101 to 106:
s101, measuring the target body temperature of a target person through an infrared camera, and acquiring a facial image of the target person.
In this embodiment, the infrared camera may be used to remotely measure the body temperature, and the remote measurement of the body temperature may be a distance within 1 meter of the target person to measure the body temperature of the target person.
S102, acquiring the distance between the infrared camera and the target person and the value of the current environmental factor.
In this embodiment, the infrared camera is provided with a distance sensor for measuring the distance between the infrared camera and the target person, so as to calculate the distance compensation value, and when the temperature is measured, the temperature will be reduced with the distance.
In this embodiment, the value of the current environmental factor includes an ambient temperature and an ambient relative humidity, and the infrared camera is equipped with a temperature and humidity sensor, and because the measured distance is far, the ambient temperature and the ambient relative humidity of the current environment will be affected, and the temperature and humidity sensor is used for measuring the ambient temperature and the ambient relative humidity of the current environment, so as to calculate a first temperature compensation value and a humidity compensation value.
S103, calculating a temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor.
In one embodiment, the current environmental factor comprises an ambient temperature and an ambient relative humidity, wherein:
the step of calculating the temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor comprises the following steps:
calculating the calculated temperature compensation coefficient according to the following formula;
Csum＝(TO-25/2)*0.01+(25-TA)*0.01+1+(distance*0.006)+RH* 0.0001；
wherein Csum represents the calculated temperature compensation coefficient, TO represents the target body temperature of a target person, TA represents the ambient temperature, distance represents the distance between the infrared camera of the thermometer and the target person, and RH represents the ambient relative humidity;
the step of calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature comprises the following steps:
calculating the real body temperature of the target person according to the following formula;
TB＝TO*Csum；
wherein TB represents the calculated true body temperature of the target person.
In this embodiment, the distance compensation value cd=1+ (distance 0.006) can be calculated according to the following formula, where CD represents the distance compensation value and distance represents the distance obtained by the distance sensing measurement.
In this embodiment, a first temperature compensation value CV 1= (25-TA) may be calculated according to the following formula, where CV1 represents the first temperature compensation value, and TA represents the ambient temperature measured by the temperature and humidity sensor in the current environment.
In this embodiment, the humidity compensation value crh=rh=0.0001 may be calculated according to the following formula, where CRh represents the humidity compensation value and RH represents the ambient relative humidity measured by the temperature and humidity sensor in the current environment.
In this embodiment, the second temperature value CV2 = (TO-25/2) ×0.01 may be calculated according TO the following formula, where CV2 represents the second temperature compensation value and TO represents the target body temperature measured by the target person.
In this embodiment, the temperature compensation coefficient is calculated according to the target body temperature, the distance and the value of the current environmental factor, and the temperature compensation coefficient csum=cd + cv1+cv2+crh may be calculated according to the following formula, where Csum represents the calculated temperature compensation coefficient, CD represents the distance compensation value, CV1 represents the first temperature compensation value, CV2 represents the second temperature compensation value, distance represents the distance between the infrared camera and the target person, and CRh represents the humidity compensation value.
In this embodiment, the temperature compensation coefficient may also be expressed as csum= (TO-25/2) ×0.01+ (25-TA) ×0.01+1+ (distance) ×0.006) +rh×0.0001, where Csum represents the calculated temperature compensation coefficient, TO represents the target body temperature of the target person, TA represents the ambient temperature, distance represents the distance between the infrared camera and the target person, and RH represents the ambient relative humidity.
And S104, calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature.
In this embodiment, the actual body temperature of the target person is calculated according TO the temperature compensation coefficient and the target body temperature, where TB represents the calculated actual body temperature of the target person, TO represents the target body temperature measured by the target person, and Csum represents the calculated temperature compensation coefficient.
S105, analyzing the facial image and judging whether the target person wears a mask.
In one embodiment, the step further comprises:
and identifying the facial image through a pre-trained target detection model, and judging whether the target person wears the mask.
Wherein training the object detection model comprises:
acquiring a first facial sample image including a mask and a second facial sample image not including the mask;
receiving first labeling information for the first facial sample image and second labeling information for the second facial sample image;
and inputting the first facial sample image, the first annotation information, the second facial sample image and the second annotation information into the target detection model for learning by the target detection model to obtain the trained target detection model.
S106, judging that the target person is an abnormal target person when the real body temperature is abnormal and/or the target person does not wear the mask, and sending out alarm reminding.
Optionally, the intelligent detection method of the abnormal target further includes:
collecting heart rate and respiratory indexes of the target person through a non-contact respiratory and heart rate signal collecting system;
comparing the collected heart rate with a preset heart rate threshold value, and judging whether the heart rate of the target person is abnormal or not;
comparing the acquired respiratory index with a preset respiratory index, and judging whether the respiratory index of the target person is abnormal or not.
The step of judging the target person to be an abnormal target person further comprises the steps of:
and judging that the target person is an abnormal target person when the real body temperature is abnormal, the target person does not wear a mask, the heart rate of the target person is abnormal and/or the breathing index of the target person is abnormal.
Specifically, when the real body temperature is abnormal and/or the target person does not wear a mask and/or the heart rate of the target person is abnormal and/or the respiratory index of the target person is abnormal, judging that the target person is an abnormal target person.
In this embodiment, the non-contact respiration and heart rate signal acquisition system may be connected to the computer device for implementing the functions of this embodiment.
Specifically, as shown in fig. 2, measuring the target body temperature of the target person by the infrared camera may include the following steps 201 to 203:
s201, simultaneously acquiring a plurality of sample body temperatures of the face of a target person through an infrared sensor;
s202, judging whether the number of the sample body temperatures reaches a preset value, and if the number of the sample body temperatures does not reach the preset value, re-collecting a plurality of sample body temperatures of a target person until the number of the collected sample body temperatures reaches the preset value;
s203, if the sample body temperature reaches a preset value, determining a target body temperature according to a preset rule and a plurality of sample body temperatures.
For the above step 201, in this embodiment, the simultaneous acquisition is that the user turns on the infrared camera once to simultaneously acquire a plurality of body temperatures in a short time. The collected multiple body temperatures are analyzed to obtain multiple sample body temperatures, and then the target body temperatures are analyzed from the multiple sample body temperatures, and it can be understood that the infrared camera in the embodiment is internally provided with the chip for writing the program, so that multiple body temperatures can be collected in a very short time.
For ease of understanding, further, the step of simultaneously acquiring a plurality of sample body temperatures of the face of the subject person by the infrared sensor may include one or two of the following:
as shown in fig. 3, the first mode includes the following steps 301 to 304:
s301, simultaneously acquiring a plurality of body temperatures of the same area of the face of a target person through an infrared sensor;
s302, judging whether a plurality of body temperatures are in a preset interval or not;
s303, if the body temperature is within the preset interval, determining the body temperature within the preset interval as a sample body temperature;
s304, if the body temperature is not within the preset interval, processing according to a preset flow.
For the steps 301 and 302, an MLX90614 infrared sensor may be used to obtain a body temperature of an area of a face, determine whether the body temperature is within a preset interval, if yes, determine the body temperature as a sample body temperature, analyze the collected body temperatures one by one, and continuously determine whether the body temperatures are within the preset interval.
For example, 5 body temperatures of a certain area of the face of the target person are collected at the same time, and whether the 5 body temperatures are all within a preset interval is judged, alternatively, the preset interval can be set between 35 degrees and 40 degrees, the target person is low-fever, high-fever or normal body temperature is generally within the range, and whether the collected 5 body temperatures are within the preset interval is judged one by one.
For the above step 303, regarding the body temperatures within the preset interval, the body temperatures within the preset interval are determined as the sample body temperatures, and if the collected 5 body temperatures are within the preset interval, all the 5 body temperatures may be determined as the sample body temperatures.
For the above step 304, the body temperature not within the preset interval is processed according to the preset procedure, for example, the body temperature of 34 ° is measured, and the abnormal body temperature can be determined, and the processing can be directly abandoned.
The number of samples and the number of body temperatures are only used to more clearly describe the present embodiment, and the present embodiment is not limited thereto.
As shown in fig. 4, the second mode includes the following steps 305 to 307:
s305, simultaneously acquiring a plurality of body temperatures of different areas of the face of the target person;
s306, acquiring body temperatures in a preset interval from the body temperatures in the different areas;
s307, determining the highest acquired body temperature in a preset interval as the sample body temperature.
For the above step 307, the acquired highest body temperature in the preset interval is determined as the sample body temperature for a plurality of body temperatures in the preset interval. If 60 body temperatures in the 64 body temperatures meet the preset interval, then judging the highest temperature in the 60 body temperatures, and determining the highest temperature as the sample body temperature.
The number of samples and the number of body temperatures are only used to more clearly describe the present embodiment, and the present embodiment is not limited thereto.
For the above step 202, it is determined whether the number of sample body temperatures reaches a preset value, when the number of sample body temperatures is smaller than the preset value, that is, the number of sample body temperatures has not reached the preset value, a plurality of sample body temperatures of the target person are collected again until the number of collected sample body temperatures reaches the preset value, for example, 5 sample body temperatures are needed, when 5 body temperatures are simultaneously read, it is determined whether 5 sample body temperatures are reached, if less than 5 sample body temperatures are obtained, the current read body temperature is abandoned, and the reading of 5 sample body temperatures is restarted until 5 sample body temperatures are continuously read and reached.
It can be understood that in the process of determining the sample body temperature, the condition that the body temperature does not accord with the preset interval can occur, meanwhile, the collected 5 times of body temperature can not be used as the sample body temperature, and if the number of the sample body temperatures is smaller than the collection times, the calculation of the body temperature is abandoned, and the collection is restarted.
It should be noted that the above number of samples is only for more clearly describing the present embodiment, and the present embodiment is not limited thereto.
For the above step 203, when the number of sample body temperatures reaches the preset value, the target body temperature is determined according to the preset rule and the plurality of sample body temperatures, and specifically, the step of determining the target body temperature according to the preset rule and the plurality of sample body temperatures in step 203 may include the following steps 401 to 403:
s401, calculating the difference value between every two of the plurality of sample body temperatures;
s402, determining the closest preset number of sample body temperatures according to the difference value;
s403, calculating the average value of the preset number of sample body temperatures to obtain the target body temperature.
For the above step 401, it can be understood that, if there are 5 sample body temperatures, the difference is made between the plurality of sample body temperatures, and the difference is made between the 5 sample body temperatures, respectively, so that the closest plurality of sample body temperatures can be calculated according to the plurality of difference values.
For the above steps 402 and 403, for example, after obtaining the difference values between the sample body temperatures, if 3 final sample body temperatures are preset, 3 nearest sample body temperatures are selected from the 5 sample body temperatures according to the difference values, and the final target body temperatures of the target person are obtained by averaging the 3 sample body temperatures.
It should be noted that the above-mentioned number of samples and the preset number are only for describing the present embodiment more clearly, and the present embodiment is not limited thereto.
In a further preferred embodiment of the application, the computer device is externally connected with a loudspeaker with a sound producing function, and can directly broadcast the measurement result through voice. For example, by starting an infrared camera, the body temperature value is directly displayed, and the body temperature detection result is broadcasted through voice, and the results of normothermia, hypothermia or hyperthermia and the like are informed to target personnel, so that people with vision impairment can use the device conveniently.
In one embodiment, the method for intelligently detecting an abnormal target further includes:
when a target person is detected, recording a video file through the infrared camera;
and acquiring the face image of the target person from the recorded video file.
In one embodiment, the method for intelligently detecting an abnormal target further includes:
when the target person is judged to be an abnormal target person, acquiring a recorded video file of the abnormal target person;
and sending out an alarm prompt and simultaneously playing the video file of the abnormal target personnel.
In one embodiment, the step of analyzing the facial image to determine whether the target person wears the mask includes:
and identifying the facial image through a pre-trained target detection model, and judging whether the target person wears the mask.
In one embodiment, the step of training the object detection model comprises:
acquiring a first facial sample image including a mask and a second facial sample image not including the mask;
receiving first labeling information for the first facial sample image and second labeling information for the second facial sample image;
and inputting the first facial sample image, the first annotation information, the second facial sample image and the second annotation information into the target detection model for learning by the target detection model to obtain the trained target detection model.
According to the intelligent detection method for the abnormal target, the infrared camera is used for measuring the target body temperature of the target person, acquiring the facial image of the target person, acquiring the distance between the infrared camera and the target person and the value of the current environmental factor, calculating the temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor, calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature, analyzing the facial image, judging whether the target person wears a mask, judging that the target person is the abnormal target person when the real body temperature is abnormal and/or the target person does not wear the mask, and sending out alarm reminding.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In an embodiment, an intelligent detection device for an abnormal target is provided, where the intelligent detection device for an abnormal target corresponds to the intelligent detection method for an abnormal target in the foregoing embodiment one by one. As shown in fig. 3, the intelligent detection device for abnormal targets includes a target body temperature measurement module 11, an influence factor value module 12, a compensation coefficient calculation module 13, a real body temperature calculation module 14, a judgment module 15 and an alarm module 16. The functional modules are described in detail as follows:
the target body temperature measuring module 11 is used for measuring the target body temperature of a target person through the infrared camera and acquiring a facial image of the target person;
the influence factor value module 12 is used for acquiring the distance between the infrared camera and the target person and the value of the current environmental factor;
a compensation coefficient calculation module 13, configured to calculate a temperature compensation coefficient according to the distance and the value of the current environmental factor;
a real body temperature calculation module 14, configured to calculate a real body temperature of a target person according to the temperature compensation coefficient and the target body temperature;
a judging module 15, configured to analyze the facial image and judge whether the target person wears a mask;
and the alarm module 16 is used for judging that the target person is an abnormal target person and sending out an alarm prompt when the real body temperature is abnormal and/or the target person does not wear the mask.
In one embodiment, the values of the current environmental factors include an environmental temperature and an environmental relative humidity, and the target body temperature measurement module 11 is specifically configured to calculate the calculated temperature compensation coefficient according to the following formula:
Csum＝(TO-25/2)*0.01+(25-TA)*0.01+1+(distance*0.006)+RH* 0.0001；
wherein Csum represents the calculated temperature compensation coefficient, TO represents the target body temperature of a target person, TA represents the ambient temperature, distance represents the distance between the infrared camera and the target person, and RH represents the ambient relative humidity;
the real body temperature calculation module 14 is specifically configured to calculate the real body temperature of the target person according to the following formula;
TB＝TO*Csum；
wherein TB represents the calculated true body temperature of the target person.
Further, the intelligent detection device for abnormal targets further comprises:
the video recording module is used for recording video files through the infrared camera when the target person is detected;
and the image acquisition module is used for acquiring the face image of the target person from the recorded video file.
Further, the intelligent detection device for abnormal targets further comprises:
the judging unit is used for acquiring a recorded video file of the abnormal target person when judging that the target person is the abnormal target person;
and the playing unit is used for sending out an alarm prompt and playing the video file of the abnormal target person.
In one embodiment, the judging module 15 is specifically configured to identify the facial image through a pre-trained target detection model, and judge whether the target person wears the mask.
In one embodiment, the smart detection device 100 for abnormal objects further includes:
a sample image acquisition unit for acquiring a first face sample image including the mask and a second face sample image not including the mask;
the labeling information acquisition unit is used for receiving first labeling information of the first facial sample image and second labeling information of the second facial sample image;
the learning unit is used for inputting the first face sample image, the first labeling information, the second face sample image and the second labeling information into the target detection model for learning by the target detection model to obtain the trained target detection model.
For specific limitations of the intelligent detection device for abnormal targets, reference may be made to the above limitation of the intelligent detection method for abnormal targets, and detailed description thereof will be omitted. The above-mentioned various modules in the intelligent detection device for abnormal targets may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules can be embedded in the processor in the infrared camera or independent of the processor in the infrared camera in a hardware form, and can also be stored in the memory in the infrared camera in a software form, so that the processor can call and execute the operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, the internal structure of which is shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. The input device is, for example, an infrared camera, which is also used for data acquisition, and the processor of the infrared camera is used for providing computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external device through a network connection. The computer program, when executed by a processor, implements a method for intelligent detection of an anomaly target.
In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the steps of the intelligent detection method for abnormal objects in the above embodiment, such as steps 101 to 106 shown in fig. 1. Alternatively, the processor may execute the computer program to implement the functions of the respective modules/units of the intelligent detection device for abnormal objects in the above embodiment, such as the functions of the modules 11 to 16 shown in fig. 3. In order to avoid repetition, a description thereof is omitted.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the steps of the method for intelligently detecting an abnormal target in the above embodiment, such as steps 101 to 106 shown in fig. 1. Alternatively, the computer program when executed by the processor realizes the functions of the respective modules/units of the intelligent detection apparatus for abnormal target in the above-described embodiment, such as the functions of the modules 11 to 16 shown in fig. 3. In order to avoid repetition, a description thereof is omitted.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit and module is illustrated, and in practical application, the above-mentioned functional allocation may be performed by different functional units and modules according to needs, i.e. the internal structure of the device is divided into different functional units or modules, so as to perform all or part of the above-mentioned functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. An intelligent detection method for an abnormal target is characterized by comprising the following steps:
measuring the target body temperature of a target person through an infrared camera, and acquiring a facial image of the target person;
acquiring the distance between the infrared camera and the target person and the value of the current environmental factor;
calculating a temperature compensation coefficient according to the target body temperature, the distance and the value of the current environmental factor;
calculating to obtain the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature;
analyzing the facial image and judging whether the target person wears a mask or not;
when the real body temperature is abnormal and/or the target person does not wear the mask, judging that the target person is an abnormal target person, and sending out an alarm prompt;
wherein the current environmental factor comprises an environmental temperature and an environmental relative humidity, and the step of calculating a temperature compensation coefficient according to the target body temperature, the distance and the current environmental factor comprises calculating the temperature compensation coefficient according to the following formula:
Csum＝(TO-25/2)*0.01+(25-TA)*0.01+1+(distance*0.006)+RH*0.0001；
wherein Csum represents the calculated temperature compensation coefficient, TO represents the target body temperature of a target person, TA represents the ambient temperature, distance represents the distance between the infrared camera and the target person, and RH represents the ambient relative humidity;
the step of calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature comprises the following steps of calculating the real body temperature of the target person according to the following formula:
TB＝TO*Csum；
wherein TB represents the calculated true body temperature of the target person.
2. The method for intelligently detecting an abnormal target according to claim 1, further comprising:
collecting heart rate and respiratory indexes of the target person through a non-contact respiratory and heart rate signal collecting system;
comparing the collected heart rate with a preset heart rate threshold value, and judging whether the heart rate of the target person is abnormal or not;
comparing the acquired respiratory index with a preset respiratory index, and judging whether the respiratory index of the target person is abnormal or not;
the step of judging the target person to be an abnormal target person further comprises the steps of:
and judging that the target person is an abnormal target person when the real body temperature is abnormal, the target person does not wear a mask, the heart rate of the target person is abnormal and/or the breathing index of the target person is abnormal.
3. The method for intelligently detecting an abnormal target according to claim 1, further comprising:
when a target person is detected, recording a video file through the infrared camera;
acquiring a face image of the target person from the recorded video file;
when the target person is judged to be an abnormal target person, acquiring a recorded video file of the abnormal target person;
and sending out an alarm prompt and simultaneously playing the video file of the abnormal target personnel.
4. The method for intelligently detecting an abnormal target according to claim 1, wherein the step of analyzing the face image to determine whether the target person wears a mask comprises:
and identifying the facial image through a pre-trained target detection model, and judging whether the target person wears the mask.
5. The method of claim 4, wherein training the object detection model comprises:
acquiring a first facial sample image including a mask and a second facial sample image not including the mask;
receiving first labeling information for the first facial sample image and second labeling information for the second facial sample image;
and inputting the first facial sample image, the first annotation information, the second facial sample image and the second annotation information into the target detection model for learning by the target detection model to obtain the trained target detection model.
6. An intelligent detection device for an abnormal target, comprising:
the target body temperature measuring module is used for measuring the target body temperature of a target person through the infrared camera and acquiring a facial image of the target person;
the influence factor value module is used for acquiring the distance between the infrared camera and the target person and the value of the current environmental factor;
the compensation coefficient calculation module is used for calculating a temperature compensation coefficient according to the distance and the value of the current environmental factor;
the real body temperature calculation module is used for calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature;
the judging module is used for analyzing the facial image and judging whether the target person wears a mask or not;
the alarm module is used for judging the target person to be an abnormal target person and sending out alarm reminding when the real body temperature is abnormal and/or the target person does not wear the mask;
wherein the current environmental factor comprises an environmental temperature and an environmental relative humidity, and the step of calculating a temperature compensation coefficient according to the target body temperature, the distance and the current environmental factor comprises calculating the temperature compensation coefficient according to the following formula:
Csum＝(TO-25/2)*0.01+(25-TA)*0.01+1+(distance*0.006)+RH*0.0001；
wherein Csum represents the calculated temperature compensation coefficient, TO represents the target body temperature of a target person, TA represents the ambient temperature, distance represents the distance between the infrared camera and the target person, and RH represents the ambient relative humidity;
the step of calculating the real body temperature of the target person according to the temperature compensation coefficient and the target body temperature comprises the following steps of calculating the real body temperature of the target person according to the following formula:
TB＝TO*Csum；
wherein TB represents the calculated true body temperature of the target person.
7. The apparatus for intelligent detection of an anomaly target of claim 6, further comprising:
the video recording module is used for recording video files through the infrared camera when the target person is detected;
and the image acquisition module is used for acquiring the face image of the target person from the recorded video file.
8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method for intelligent detection of an anomaly target according to any one of claims 1 to 5 when the computer program is executed by the processor.
9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the intelligent detection method of an abnormal target according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010296948.1A CN111681770B (en) | 2020-04-15 | 2020-04-15 | Intelligent detection method and device for abnormal target, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010296948.1A CN111681770B (en) | 2020-04-15 | 2020-04-15 | Intelligent detection method and device for abnormal target, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111681770A CN111681770A (en) | 2020-09-18 |
| CN111681770B true CN111681770B (en) | 2023-09-01 |
Family
ID=72451476
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010296948.1A Active CN111681770B (en) | 2020-04-15 | 2020-04-15 | Intelligent detection method and device for abnormal target, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111681770B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112927816B (en) * | 2021-01-27 | 2021-11-23 | 广东顺德鲁棒智能技术有限公司 | Body temperature detection system, body temperature trend prediction method and system |
| CN113314230A (en) * | 2021-05-27 | 2021-08-27 | 创新奇智(上海)科技有限公司 | Intelligent epidemic prevention method, device, equipment and storage medium based on big data |
| CN113420629B (en) * | 2021-06-17 | 2023-04-28 | 浙江大华技术股份有限公司 | Image processing method, device, equipment and medium |
| CN114061791A (en) * | 2021-11-12 | 2022-02-18 | 歌尔科技有限公司 | Body temperature detection method, system and medium |
| CN114469026A (en) * | 2022-01-21 | 2022-05-13 | 北京理工大学前沿技术研究院 | Driver vital sign monitoring method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103565422A (en) * | 2013-11-06 | 2014-02-12 | 江苏大学 | Medical infrared thermometer and measurement compensating method of medical infrared thermometer |
| CN106546338A (en) * | 2016-11-04 | 2017-03-29 | 中云智慧（北京）科技有限公司 | Self-service passing method for health quarantine |
| CN106570970A (en) * | 2016-11-03 | 2017-04-19 | 中云智慧（北京）科技有限公司 | Self-service passing method for health quarantine |
| CN110196103A (en) * | 2019-06-27 | 2019-09-03 | Oppo广东移动通信有限公司 | Thermometry and relevant device |
| CN112444318A (en) * | 2019-08-30 | 2021-03-05 | 梅州市青塘实业有限公司 | Method and device for measuring body temperature in non-contact mode, thermometer and storage medium |
-
2020
- 2020-04-15 CN CN202010296948.1A patent/CN111681770B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103565422A (en) * | 2013-11-06 | 2014-02-12 | 江苏大学 | Medical infrared thermometer and measurement compensating method of medical infrared thermometer |
| CN106570970A (en) * | 2016-11-03 | 2017-04-19 | 中云智慧（北京）科技有限公司 | Self-service passing method for health quarantine |
| CN106546338A (en) * | 2016-11-04 | 2017-03-29 | 中云智慧（北京）科技有限公司 | Self-service passing method for health quarantine |
| CN110196103A (en) * | 2019-06-27 | 2019-09-03 | Oppo广东移动通信有限公司 | Thermometry and relevant device |
| CN112444318A (en) * | 2019-08-30 | 2021-03-05 | 梅州市青塘实业有限公司 | Method and device for measuring body temperature in non-contact mode, thermometer and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111681770A (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111681770B (en) | Intelligent detection method and device for abnormal target, computer equipment and storage medium | |
| WO2021174601A1 (en) | Infrared thermal imaging-based human body temperature anomaly detection method and apparatus | |
| CN112444318B (en) | Method and device for measuring body temperature in non-contact mode, thermometer and storage medium | |
| CN110916620A (en) | Body temperature measuring method and terminal | |
| CN105302701B (en) | Method, device and equipment for testing reaction time of terminal user interface | |
| CN111325127A (en) | Abnormal object judgment method, system, machine readable medium and equipment | |
| US11647167B2 (en) | Wearable device for performing detection of events by using camera module and wireless communication device | |
| CN112001953A (en) | Temperature detection method, device, equipment and computer equipment | |
| CN108872780B (en) | Live working detection and system for live exploration and terminal equipment | |
| CN103268399A (en) | Health management method and system based on video processing device | |
| KR102162630B1 (en) | A system and method for providing a result of a object state using a user terminal | |
| CN114642432A (en) | Attention assessment method, device, equipment and storage medium | |
| CN212721763U (en) | Infrared thermal field temperature measurement system based on face recognition | |
| CN111128326A (en) | Community patient monitoring method and system based on target tracking | |
| CN111753636B (en) | Temperature measurement identification method and system based on face shielding condition | |
| CN114159065A (en) | Method and device for evaluating intermittent meditation training based on electroencephalogram signals | |
| US20170245782A1 (en) | Impairment detection with environmental considerations | |
| JP6734487B2 (en) | Regional smile level display system, regional smile level display method and program | |
| CN112633143A (en) | Image processing system, method, head-mounted device, processing device, and storage medium | |
| CN111486990A (en) | Human body state monitoring method, system, device and storage medium | |
| CN112244797A (en) | Body state monitoring method and device and storage medium | |
| CN111261264A (en) | Intelligent physique testing device and method and storage medium | |
| CN111879415B (en) | Temperature measurement management method, device and system | |
| CN111207856B (en) | Method, device and equipment for acquiring accuracy of measured temperature and readable storage medium | |
| CN112927816B (en) | Body temperature detection system, body temperature trend prediction method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |