Temperature measurement method and system for portable temperature inspection

Abstract

The embodiments of the present disclosure disclose a temperature measurement method and system for portable temperature inspection. The specific implementation of the method includes: obtaining multiple frames of thermal images in the inspection route; performing screening operations on the multiple frames of thermal images to extract thermal image information including black body objects and / or automatically selected reference objects, wherein multiple black bodies are distributed on the inspection route at preset intervals, and multiple reference objects are between two adjacent black bodies; using the thermal image information and temperature information of the black body object or the reference object as a reference, the temperature information of the object to be measured can be obtained. This implementation enables the staff to perform temperature detection on the personnel on the inspection route by holding or wearing a portable infrared thermal imager by setting a black body in the inspection route and automatically selecting a reference object. While improving the flexibility of use of the temperature measurement system, the cost of the temperature measurement system is greatly reduced due to the reduction in the number of black bodies.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of infrared temperature measurement technology, and specifically to a temperature measurement method and system for portable temperature inspection. Background Art

[0002] Infrared temperature measurement technology is widely used in many fields, such as medical treatment, electrical equipment fault diagnosis and body temperature detection.

[0003] Taking body temperature detection as an example, in order to improve the detection accuracy, the infrared thermal imager needs a black body that can maintain a constant temperature as a reference. The positions of the black body and the infrared thermal imager are usually fixed. When the person to be detected is tested for body temperature, the person needs to be close to the black body to ensure that the person and the black body are in the same thermal image. When there are many people to be detected, everyone has to be close to the black body, resulting in unnecessary gathering and flow of people. Another solution is to deploy a large number of black bodies, but this will result in excessively high costs for body temperature detection. In addition, due to the fixed positions of the black body and the infrared thermal imager, the existing temperature measurement system also has problems with flexibility and poor maneuverability.

[0004] Therefore, this field needs a new infrared temperature measurement system to solve the above problems. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose a temperature measurement method and system for portable temperature inspection to solve the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a portable temperature inspection temperature measurement method, the method comprising: acquiring multiple frames of thermal images in an inspection route; performing screening operations on the multiple frames of thermal images to extract thermal image information including black body objects and / or automatically selected reference objects, wherein a plurality of the above black bodies are distributed to the inspection route at preset intervals, and a plurality of the above reference objects are located between two adjacent black bodies; determining the temperature of the object to be measured based on the temperature information and thermal imaging information of the above black body objects or the above reference objects.

[0008] In a second aspect, some embodiments of the present disclosure provide a portable temperature inspection temperature measurement system, the temperature measurement system comprising: an acquisition unit, configured to acquire multiple frames of thermal images in an inspection route; an extraction unit, configured to perform screening operations on the multiple frames of thermal images, and extract thermal image information including black body objects and / or automatically selected reference objects, wherein a plurality of the above black bodies are distributed on the inspection route at preset intervals, and a plurality of reference objects are located between two adjacent black bodies; a determination unit, configured to determine the temperature of the object to be measured based on the thermal imaging information and temperature information of the above black body objects or the above reference objects.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0011] One of the above-mentioned embodiments of the present disclosure has the following beneficial effects: by setting a black body in the inspection route and automatically selecting a reference object, the staff can detect the temperature of the personnel on the inspection route by holding or wearing a portable infrared thermal imager. The measured temperature is corrected by the black body and the reference object. Thus, the flexibility of the temperature measurement system is improved. At the same time, the number of black bodies is reduced, and the cost of the temperature measurement system is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of the temperature measurement method for portable temperature inspection according to the present disclosure;

[0014] Figure 2 It is a schematic diagram of the structure of some embodiments of the portable temperature inspection temperature measurement system according to the present disclosure;

[0015] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0018] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0022] See first Figure 1 , Figure 1 This is a process 100 of some embodiments of the temperature measurement method for portable temperature inspection according to the present disclosure.

[0023] The portable temperature inspection temperature measurement method comprises the following steps:

[0024] Step 101, obtaining multiple frames of thermal images in the inspection route.

[0025] In some embodiments, the execution subject of the above-mentioned acquisition of multiple frames of thermal images of the inspection route can be a portable infrared thermal imager. The above-mentioned portable infrared thermal imager can acquire thermal imaging videos in the inspection route. The above-mentioned thermal imaging video may include multiple frames of thermal images. Specifically, the staff or devices with mobile functions such as intelligent robots can wear or be equipped with the above-mentioned portable infrared thermal imager, and the above-mentioned thermal imaging sequence can be acquired during the inspection along the inspection route. It should be noted that the above-mentioned inspection route can be random or preset. For example, the staff can hold the above-mentioned portable infrared thermal imager for inspection according to the location and density of the personnel to be measured. For another example, the above-mentioned intelligent robot and other devices with mobile functions can be inspected along a random route, or one or more preset inspection routes can be embedded in the above-mentioned device, and the above-mentioned device can select the corresponding inspection route for inspection according to different time periods.

[0026] Step 102 , performing a screening operation on multiple frames of thermal images to extract thermal image information including black body objects and / or automatically selected reference objects.

[0027] In some embodiments, the execution subject of the above-mentioned extraction of thermal images including black body objects and / or automatic extraction of reference objects may be a processor. The above-mentioned execution subject detects the objects in the above-mentioned multiple frames of thermal images and obtains the object information of the above-mentioned objects. The above-mentioned object information may also include features such as the imaging range of the object that can identify the type of the object. Further, as an example, the above-mentioned object information can be compared with the sample reference information and the sample black body information pre-stored in the database. In response to the above-mentioned object information matching the sample reference information or the sample black body information, the thermal image information including the above-mentioned object information is subjected to extraction operation.

[0028] Next, the process of automatically selecting a reference object is described.

[0029] In response to the above object information matching the sample reference object information or the sample black body information, the object including the above object information is given priority as a reference object, and its temperature information is obtained. For other objects that meet the reference object requirements, their temperature is calculated based on the reference object just selected, and then used as an alternative reference object and saved in combination with the frame number information for the next frame. In this way, the execution subject can transmit the reference object in multiple frames of thermal images. The above reference object can be a reference object pre-set near the black body, such as a benchmark. It can also be selected by the execution subject according to certain constraints. The reference object can be of various types, such as a seat, a wall, a book, etc.

[0030] In some optional implementations of some embodiments, when extracting a thermal image including a black body object and / or an automatically selected reference object, object detection may be performed on each of the multiple frames of thermal images to extract a feature vector of the object. The feature vector may include a shape feature vector, a texture feature vector, etc. The feature vector is compared with a feature vector of a black body or a reference object that has been feature extracted and stored in a database. In response to the feature vector of the object including a feature vector of a black body or a reference object in the database, the thermal image is extracted. As an example, feature extraction may be performed using a SIFT algorithm (Scale-invariant feature transform).

[0031] Furthermore, the reference object may be an object with obvious edge features and a relatively average grayscale value of the thermal image generated by the reference object. For example, it may be a wall, an iron seat, etc. By taking the obvious degree of edge features as the first factor for determining the reference object, the execution subject can quickly and clearly identify the reference object, thereby improving the detection efficiency. At the same time, taking the average degree of grayscale value as the second factor for determining the reference object, the temperature of such an object changes little over time and space, which can improve the detection accuracy. In addition, the thermal image information of the reference object may only select an area inside the reference object. Specifically, the edge features of the reference object to be determined in the thermal image may be extracted by a deep learning method or a deep learning image segmentation network. The deep learning detection method may include, but is not limited to, at least one of the following: SSD algorithm (Single Shot MultiBox Detector, target detection algorithm), R-CNN algorithm (Region-Convolutional Neural Networks, target detection algorithm), Fast R-CNN algorithm (Fast Region-Convolutional Neural Networks, target detection algorithm). The above-mentioned deep learning image segmentation network may include but is not limited to at least one of the following: FCN network (Fully Convolutional Networks), Mask-RCNN network (Mask-Region-CNN, image instance segmentation network). The grayscale value of the above-mentioned thermal image can be determined by black-and-white processing of the thermal image, thereby determining the grayscale value of the reference object to be determined in the thermal image.

[0032] The determination of the reference object can also be obtained by analyzing the edge feature information and gray value information of the reference object through a machine learning model, wherein the machine learning model is trained through a training sample set. The edge feature information of the reference object can be determined by the deep learning detection method or deep learning image segmentation network mentioned above.

[0033] As an example, the machine learning model can be obtained by performing the following training steps based on the training sample set: taking the majority of the randomly selected ones in the training sample set as the training set and the remaining ones as the test set, inputting the edge feature information of the whole frame thermal image of the training set and the gray value information of the sample reference object into the input end of the initial machine learning model respectively, and obtaining the edge feature information of the thermal image of the sample reference object and the gray value information of the thermal imaging reference area of ​​the sample reference object through network calculation; comparing the edge feature information of the thermal image predicted by each sample reference object in the above training sample and the gray value information of the thermal imaging reference area of ​​the sample reference object with the real information of the corresponding sample reference object, adjusting the parameters of the above initial machine learning model according to the comparison result, and iterating this process continuously until the prediction accuracy is greater than the preset accuracy threshold; in response to determining that the above accuracy is not greater than the above preset accuracy threshold, adjusting the network structure of the above initial machine learning model, using the adjusted initial machine learning model as the initial machine learning model, and performing the above training steps again. It can be understood that after the above training, the machine learning model can be used to predict and characterize the correspondence between the edge feature information of the thermal image of the reference object, the gray value information of the thermal imaging reference area of ​​the reference object, and the reference determination result information. The machine learning model mentioned above can be a neural network model.

[0034] As an example, the machine learning model can predict the edge feature information of the thermal image of the reference object, the gray value information of the reference area of ​​the thermal imaging information of the reference object, and the corresponding relationship table. After the actual device is running, a large amount of such information is stored. In this way, the edge feature information of the thermal image of the reference object, the gray value information of the reference area of ​​the thermal imaging information of the reference object, and the edge feature information of the thermal image of the reference object in the corresponding relationship table are compared with the edge feature information of the thermal image of the reference object and the gray value information of the thermal imaging reference area of ​​the reference object. If the edge feature information of the thermal image of a reference object to be selected and the gray value information of the reference object are the same as the edge feature information of the thermal image of the reference object in the historical record and the gray value information of the reference area of ​​the thermal imaging information of the reference object, then the object is selected as the reference object for subsequent temperature determination. If the reference object is new, it can be added to the reference object historical record.

[0035] As another example, the above-mentioned initial machine learning model can be an untrained deep learning model or a trained deep learning model, and each layer of the initial deep learning model can be set with initial parameters, and the parameters can be continuously adjusted during the training process of the deep learning model. The initial deep learning model can be various types of untrained or untrained artificial neural networks or a model obtained by combining multiple untrained or untrained artificial neural networks. For example, the initial deep learning model can be an untrained convolutional neural network, or an untrained recurrent neural network, or a model obtained by combining an untrained convolutional neural network, an untrained recurrent neural network, and an untrained fully connected layer. In this way, the edge feature information of the thermal image containing the reference object and the gray value information of the reference object can be input from the input side of the deep learning model, processed by the parameters of each layer in the deep learning model in turn, and output from the output side of the deep learning model. The information output by the output side is the reference object determination result information.

[0036] It should be noted that the temperature information of the reference object is calibrated by the temperature information of the black body object or the reference object that has been calibrated by the black body object. For example, the temperature information of the reference object can be calibrated by the known constant temperature information of the black body object in the same thermal image. It can also be calibrated by the temperature information of the reference object in the previous thermal image, wherein the reference in the previous thermal image can be directly calibrated by the known constant temperature of the black body object, or it can be calibrated by the temperature information of other reference objects that have been calibrated by the known constant temperature of the black body object. Specifically, the execution subject can select a reference object in the thermal image, calibrate the temperature information of the reference object in the next thermal image by the temperature information of the reference object, and then continuously transmit it.

[0037] In addition, the plurality of black bodies are distributed on the inspection route at preset intervals, and the plurality of reference objects are located between two adjacent black bodies. Those skilled in the art can conduct experiments based on a large amount of detected temperature data to determine the appropriate preset interval between the two black bodies and the number of reference objects.

[0038] Step 103: determining the temperature of the object to be measured based on the thermal imaging information and temperature information of the black body object or the reference object.

[0039] In some embodiments, the hot and cold state of the black body object or the reference object can be displayed on a thermal image by a portable infrared thermal imager. The temperature information can include a temperature value and can also include a thermal imaging grayscale value showing the degree of hot and cold. Those skilled in the art can determine the temperature of the object to be measured based on the relevant prior art or common knowledge, taking the temperature and imaging grayscale value of the black body as a reference.

[0040] In some optional implementations of some embodiments, the temperature measurement method may further include: in response to detecting that a black body object is included in the thermal image during the inspection process, the temperature of the object to be measured is determined preferentially based on the known constant temperature information of the black body object; specifically, since the black body is a common means of calibration in the current relevant infrared temperature measurement technology and can reduce temperature measurement errors. Therefore, when both the black body and the reference object appear in the thermal image, the black body is preferentially used for calibration, and the reference object serves as an intermediate process to replace the black body calibration. Furthermore, in order to avoid the problem of reference drift when the reference object is used for calibration, a new black body can be used for adjustment to ensure the optimal trade-off between global error and system cost.

[0041] Some embodiments of the present disclosure provide methods that enable workers to detect the temperature of people on the inspection route by using a handheld or portable infrared thermal imager by setting a black body and automatically selecting a reference object in the inspection route. The measured temperature is corrected by the black body and the reference object. This improves the flexibility of the temperature measurement system. At the same time, the number of black bodies is reduced, and the cost of the temperature measurement system is reduced.

[0042] In some optional implementations of some embodiments, the temperature measurement method may further include: a first step, in response to the thermal image including a reference object and a next black body object, comparing a first numerical value representing the temperature information of the reference object with a second numerical value representing the temperature information of the reference object calibrated with the known constant temperature information of the next black body object.

[0043] Specifically, since the temperature information of the reference object may be calibrated by the temperature information of the reference object that has been calibrated in the previous thermal image, calibration reference drift may occur in the process of continuous reference selection and calibration. Therefore, the first value represented by the temperature information of the reference object can be compared with the second value represented by the temperature information of the reference object calibrated with the known constant temperature information of the next black body object to determine the reference drift.

[0044] In the second step, in response to the comparison result of the first value and the second value exceeding the preset threshold, the distribution distance of the black body is adjusted or the reference object is reselected. The preset threshold can be determined by a person skilled in the art through a large number of experiments. Since the distance between two adjacent black bodies is too long, the selection and calibration process of the reference object will be more, so it is easy to cause the calibration reference drift. In this way, the detection accuracy can be improved.

[0045] Next Read Figure 2 , Figure 2 It is a schematic diagram of the structure of some embodiments of the portable temperature inspection temperature measurement system according to the present disclosure.

[0046] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a portable temperature inspection temperature measurement system. These system embodiments are Figure 1 Corresponding to the method embodiments shown, the system can be specifically applied to various electronic devices.

[0047] like Figure 2 As shown, some embodiments of the portable temperature inspection temperature measurement system 200 include: an acquisition unit, configured to acquire multiple frames of thermal images in the inspection route; an extraction unit, configured to perform a screening operation on the above multiple frames of thermal images, and extract thermal image information including black body objects and / or automatically selected reference objects, wherein a plurality of the above black bodies are distributed to the inspection route at preset intervals, and a plurality of reference objects are located between two adjacent black bodies; a determination unit, configured to determine the temperature of the object to be measured based on the thermal image information and temperature information of the above black body objects or the above reference objects.

[0048] In an optional implementation of some embodiments, the temperature information of the reference object is calibrated by the temperature information of a blackbody object, the known constant temperature information of the blackbody object, or the temperature information of the reference object calibrated by the temperature information of the blackbody object.

[0049] In an optional implementation of some embodiments, the selection of the above-mentioned reference object is obtained by analyzing the edge feature information and grayscale value reference area information of the above-mentioned reference object through a machine learning model, wherein the above-mentioned machine learning model is trained through a training sample set.

[0050] In an optional implementation of some embodiments, the training sample set includes edge feature information of the thermal image of the sample reference object, grayscale value information of the sample reference object and sample reference object determination result information, and the machine learning model is trained using the sample output result information as the expected output.

[0051] In an optional implementation of some embodiments, the extraction unit is further configured to: detect an object in the thermal image based on each of the thermal images in the multiple frames of thermal images; extract a feature vector of the object; and perform an extraction operation on the thermal image in response to the feature vector including a feature vector of the black body or the reference object.

[0052] In an optional implementation of some embodiments, the system further includes: in response to a black body object being included in the thermal image, the temperature of the object to be measured is determined preferentially based on the temperature information of the black body object.

[0053] In an optional implementation of some embodiments, the system further includes: in response to the thermal image including a reference object and a next black body object, comparing a first value represented by the temperature information of the reference object with a second value represented by the temperature information of the reference object calibrated by the next calibration; in response to the comparison result between the first value and the second value exceeding a preset threshold, adjusting the distribution distance of the black body.

[0054] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The terminal devices in some embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0055] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0056] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a memory card; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0057] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0058] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0059] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0060] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains multiple frames of thermal images in the inspection route; performs screening operations on the multiple frames of thermal images to extract thermal images including black body objects and / or automatically selected reference objects, wherein the multiple black bodies are distributed to the inspection route at preset intervals, and the multiple reference objects are located between two adjacent black bodies; and determines the temperature of the object to be measured based on the temperature information of the black body objects or the reference objects.

[0061] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0062] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0063] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, an extraction unit, and a determination unit. The names of these units do not constitute a limitation on the units themselves in some cases, for example, the acquisition unit may also be described as a "unit for acquiring multiple frames of thermal images in the inspection line".

[0064] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0065] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A portable temperature inspection temperature measurement method, comprising: Acquire multiple frames of thermal images in the inspection route; The plurality of frames of thermal images are screened to extract thermal image information including black body objects and / or automatically selected reference objects, wherein the plurality of black bodies are distributed on the inspection route at preset intervals, and the plurality of reference objects are located between two adjacent black bodies. The process of automatically selecting reference objects includes: in response to the object information matching the sample reference information or the sample black body information, the object including the object information is preferentially used as the reference object, and its temperature information is obtained. For other objects that meet the reference object requirements, their temperatures are calculated based on the reference object just selected, and then they are used as candidate reference objects and saved in combination with the frame number information for the next frame. The reference object is pre-set near the black body; Determining the temperature of the object to be measured according to the thermal image information and temperature information of the black body object or the reference object; In response to the thermal image including a black body object, preferentially determining the temperature of the object to be measured; In response to the thermal image including a reference object and a next black body object, comparing a first value calibrated by the temperature information of the reference object with a second value represented by the temperature information of the reference object calibrated by the temperature information of the next black body object; In response to a comparison result between the first value and the second value exceeding a preset threshold, the distribution distance of the black body is adjusted.

2. The temperature measurement method according to claim 1, wherein: The temperature information of the reference object is calibrated by the temperature information of the black body object or the temperature information of the reference object that has been calibrated by the temperature information of the black body object.

3. The temperature measurement method according to claim 1, wherein: The reference object is selected by analyzing the edge feature information of the reference object and the gray value information of the thermal image through a machine learning model, wherein the machine learning model is trained through a training sample set.

4. The method according to claim 3, wherein: The training sample set includes edge feature information of the thermal image of the sample reference object, gray value information of the thermal image of the sample reference object and sample reference object determination result information, and the machine learning model is trained using the sample output result information as the expected output.

5. The temperature measurement method according to claim 1, wherein: The screening operation on the multiple frames of thermal images to extract thermal image information including a black body or a reference object includes: Based on each of the thermal images in the multiple frames of thermal images, detecting an object in the thermal image; extracting a feature vector of the object; In response to the feature vector including the feature vector of the black body or the reference object, an extraction operation is performed on the thermal image.

6. A portable temperature inspection temperature measurement system, comprising: An acquisition unit is configured to acquire multiple frames of thermal images in the inspection route; The extraction unit is configured to perform a screening operation on the multiple frames of thermal images to extract thermal image information including black body objects and / or automatically selected reference objects, wherein the multiple black bodies are distributed on the inspection route at preset intervals, and the multiple reference objects are located between two adjacent black bodies. The process of automatically selecting reference objects includes: in response to the object information matching the sample reference information or the sample black body information, the object including the object information is preferentially used as the reference object, and its temperature information is obtained. For other objects that meet the reference requirements, their temperatures are calculated based on the reference object just selected, and then they are used as candidate reference objects and saved in combination with the frame number information for the next frame. The reference object is pre-set near the black body; A first determining unit is configured to determine the temperature of the object to be measured according to the thermal imaging information and the temperature information of the black body object or the reference object; A second determination unit is configured to preferentially determine the temperature of the object to be measured in response to the thermal image including a black body object; a comparison unit configured to compare, in response to the thermal image including a reference object and a next black body object, a first value calibrated by the temperature information of the reference object with a second value represented by the temperature information of the reference object calibrated by the temperature information of the next black body object; The adjustment unit is configured to adjust the distribution distance of the black body in response to a comparison result between the first value and the second value exceeding a preset threshold.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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