CN112258507A - Target object detection method and device of internet data center and electronic equipment - Google Patents
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Abstract
The application provides a target object detection method, a target object detection device and electronic equipment of an internet data center, wherein the method comprises the following steps: acquiring a visible light image and an infrared image of a polling area; outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with similarity greater than a preset value in the visible light image and the infrared image; and determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object. The method comprises the steps of identifying a thermal fault area in an infrared image through a network model, identifying a reference object of the visible light image and the infrared image in the visible light image and the infrared image, determining a visible light target object with a thermal fault according to the position relation of the thermal fault area and the reference object, further accurately determining the target object with the thermal fault, and improving inspection precision and efficiency.
Description
Technical Field
The application relates to the technical field of inspection, in particular to a target object detection method and device for an internet data center and electronic equipment.
Background
In order to ensure that areas with dense electric power equipment such as a machine room and the like need daily routing inspection, for example, an Internet Data Center (IDC) has the characteristics of multiple equipment, high density, high power consumption, high running temperature and the like, and for example, the Internet Data Center comprises a network switch, a server group, a memory, Data input and output wiring, a communication area, a network monitoring terminal and the like, the running state and the equipment state of the Internet Data Center need daily routing inspection, especially routing inspection for thermal faults. In the related art, manual inspection is adopted for inspection, however, thermal faults are often difficult to observe through naked eyes. Further, there is a problem in the related art that thermal failure is detected by an infrared thermal imaging technique, for example, in an electric power device, a large number of thermal failure problems in an electric network can be recorded by infrared thermal imaging. With the development of infrared thermal imaging technology, the power equipment thermal fault detection technology based on infrared imaging is more and more widely applied to power equipment. However, in the thermal imaging technology, when the temperature is high, due to a large amount of heat generation, there may be a problem of thermal imaging overexposure, which may cause imaging blur, and since the power equipment tends to be large in size, inspection of the power equipment may be applicable, but the equipment in the IDC tends to be dense and small in size, and a thermal failure may occur in a certain component of the equipment, and when the thermal imaging overexposure occurs, it is difficult to locate a specific thermal failure occurring equipment or component through thermal imaging, which may cause a deviation in a detection result. Therefore, there is a problem in the related art that a deviation occurs in the inspection of the IDC equipment.
Disclosure of Invention
The application provides a target object detection method and device of an internet data center and electronic equipment, and aims to at least solve the problem that deviation occurs in routing inspection of IDC equipment in the related art.
According to an aspect of an embodiment of the present application, there is provided a target object detection method for an internet data center, including: acquiring a visible light image and an infrared image of a polling area; outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with similarity between the visible light image and the infrared image larger than a preset value; and determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
Optionally, the trained network model includes a first network model and a second network model, and a third network model cascaded with the first network model and the second network model; the outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object comprises: inputting the visible light image into a first network model to identify a visible light target object; inputting the infrared image into a second network model to identify an infrared target object and a thermal fault area; inputting the visible light target object and the infrared target object into a third network model to predict the similarity of the visible light target object and the infrared target, and obtaining the reference object.
Optionally, the determining, in the visible light image, the visible light target object in which the thermal failure occurs based on the positional relationship between the thermal failure area and the reference object includes: calculating the center coordinates of the thermal fault area and the reference object coordinates of the reference object; determining a positional relationship of the thermal fault region to the reference based on the center coordinates and the reference coordinates; and mapping the position relation and the reference object coordinate to the visible light image to obtain a visible light target object corresponding to the thermal fault area.
Optionally, the inputting the infrared image to the second network model to identify the infrared target object and the thermal fault region includes: classifying the infrared image according to the thermal distribution based on the second network model to obtain a thermal fault area and a normal area; identifying an infrared target object within the normal area based on the second network model.
Optionally, before outputting the visible light image and the infrared image to the trained network model, the method further comprises: acquiring camera parameters of a visible light image and an infrared image; determining an offset of the visible light image from the infrared image based on the camera parameters; and adjusting the visible light image or the infrared image according to the offset, and aligning the visible light image with the infrared image.
Optionally, acquiring the visible light image and the infrared image of the inspection area includes: acquiring a visible light video and the infrared video; recognizing an infrared key frame with a heating area in the infrared video as the infrared image; and taking the visible light key frame in the visible light video synchronized with the infrared key frame as a visible light image.
According to another aspect of the embodiments of the present application, there is also provided a target object detection apparatus for an internet data center, including: the acquisition module is used for acquiring a visible light image and an infrared image of the inspection area; the identification module is used for outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with similarity between the visible light image and the infrared image larger than a preset value; and the fault detection module is used for determining a visible light target object with a thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used for storing the computer program; a processor for performing the method steps in any of the above embodiments by running the computer program stored on the memory.
According to a further aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method steps of any of the above embodiments when the computer program is executed.
In the embodiment of the application, after the visible light image and the infrared image of the inspection area are obtained, the visible light image and the infrared image are input into the network model, the thermal fault area is identified in the infrared image through the network model, the target object with the similarity larger than the preset value is identified in the visible light image and the infrared image and serves as a reference object of the visible light image and the infrared image, the visible light target object with the thermal fault is determined in the visible light image through the position relation between the thermal fault area and the reference object, the target object with the thermal fault can be accurately determined, the problem that the IDC equipment inspection deviation exists in the related technology is solved, and the inspection precision and the inspection efficiency are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a hardware environment of an alternative target object detection method for an internet data center according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of an alternative target object detection method of an internet data center according to an embodiment of the present application;
fig. 3 is a block diagram illustrating a structure of an alternative target object detection apparatus of an internet data center according to an embodiment of the present disclosure;
fig. 4 is a block diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to one aspect of the embodiment of the application, a target object detection method of an internet data center is provided. Optionally, in this embodiment, the target object detection method of the internet data center may be applied to a hardware environment as shown in fig. 1. As shown in figure 1 of the drawings, in which,
according to one aspect of the embodiment of the application, a target object detection method of an internet data center is provided. Alternatively, in this embodiment, the target object detection method of the internet data center may be applied to a hardware environment formed by the terminal 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal 102 through a network, which may be used to provide services for the terminal or a client installed on the terminal, may be provided with a database on the server or independent from the server, may be used to provide data storage services for the server 104, and may also be used to handle cloud services, and the network includes but is not limited to: the terminal 102 is not limited to a PC, a mobile phone, a tablet computer, etc. the terminal may be a wide area network, a metropolitan area network, or a local area network. The target object detection method of the internet data center according to the embodiment of the present application may be executed by the server 104, or may be executed by the terminal 102, or may be executed by both the server 104 and the terminal 102. The terminal 102 may execute the target object detection method of the internet data center according to the embodiment of the present application, or may execute the target object detection method by a client installed thereon.
Taking the server 104 and/or the terminal 102 to execute the target object detection method of the internet data center in this embodiment as an example, fig. 2 is a schematic flowchart of an optional target object detection method of the internet data center according to an embodiment of this application, and as shown in fig. 2, the flowchart of the method may include the following steps:
and step S202, acquiring a visible light image and an infrared image of the inspection area.
And S204, outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with the similarity between the visible light image and the infrared image larger than a preset value.
Step S206, determining a visible light target object in which a thermal failure occurs in the visible light image based on a positional relationship between the thermal failure region and the reference object.
Through the steps S202 to S206, after the visible light image and the infrared image of the inspection area are obtained, the visible light image and the infrared image are input into the network model, the thermal fault area is identified in the infrared image through the network model, the target object with the similarity greater than the preset value is identified in the visible light image and the infrared image and serves as a reference object of the visible light image and the infrared image, the visible light target object with the thermal fault is determined in the visible light image through the position relation between the thermal fault area and the reference object, and then the target object with the thermal fault can be accurately determined, so that the problem of deviation in inspection of the IDC equipment in the related technology is solved, and the inspection precision and efficiency are improved.
In the technical solution of step S202, a polling device, such as a polling robot, is equipped with a visible light camera and an infrared camera to poll the polling area according to a preset route, for example, the visible light camera captures a visible light image and/or video, and the infrared camera captures an infrared image and/or video. And selecting images meeting preset requirements in the visible light images and/or videos and the infrared images and/or videos as the visible light images and the infrared images of the inspection area. As an exemplary embodiment, a visible light camera and an infrared camera may capture a visible light video and an infrared video of a patrol area, and may detect the visible light video and the infrared video frame by frame after acquiring the visible light video and the infrared video, and identify an infrared key frame in the infrared video, where a heating area exists, as the infrared image; and taking the visible light key frame in the visible light video synchronized with the infrared key frame as a visible light image. For example, after obtaining the infrared image in which the heat generation region exists, the visible light image synchronized with the frame in which the infrared image exists may be obtained based on the time stamp of the frame in which the infrared image exists. As an alternative embodiment, at the time of key frame selection, an infrared image in which the heat generation point is in the central area may be selected so that the complete device or component containing the thermal failure can be identified in the visible light image.
In the technical scheme of step S204, the visible light image and the infrared image are output to a trained network model to obtain a thermal fault region and a reference object, where the reference object is a target object whose similarity between the visible light image and the infrared image is greater than a preset value. As an exemplary embodiment, the network model may be a deep learning image recognition model pre-trained by using a large amount of IDC device visible light images and infrared images, and the deep learning image recognition model may use, but is not limited to, R-CNN, FastR-CNN, Faster R-CNN, YOLO, SSD, and Master R-CNN algorithms. As an exemplary embodiment, the network model may identify devices in the visible light image, may identify thermal failure regions in the infrared image, and may also identify devices in non-thermal failure regions in the infrared image as a reference. As an exemplary embodiment, the reference object may be selected as an object having a heat value or a temperature less than a preset value. As an exemplary embodiment, the trained network model includes a first network model, a second network model, and a third network model cascaded with the first network model and the second network model, where the first network model may identify a target object in a visible light image, and includes IDC equipment or an equipment component, specifically, the first network model may be trained using a large amount of visible light images, mark the equipment or the component in the visible light image in a training sample, and by adjusting parameters of the first network model, when an accuracy of a device or an equipment component that is finally output is greater than a preset value, the training of the first network model is completed.
The second network model can be trained by adopting massive infrared images, areas which are larger than a preset temperature in the infrared images can be marked in a training sample, specific temperature detection can respectively determine a temperature maximum area and a temperature minimum area according to the position of a temperature scale target area, the temperature maximum area is read through an optical recognition program tesseract, and the temperature maximum area in the infrared images is obtained. And marking the maximum temperature value area as a thermal fault area, and marking the infrared target object in the infrared target object identification area by taking other temperature value areas as the identification areas of the infrared target object. For example, when the temperature is lower than the preset value, the infrared light heating value of the target object is small, the edge outline of the target object in the image is displayed clearly, and therefore, when recognition is performed based on the outline/shape/edge and the like, recognition can be more accurate, and therefore, the target object in the area can be marked as the infrared target object. And adjusting parameters of the second network model, and finishing the training of the second network model when the finally output thermal fault area accuracy and the infrared target object identification accuracy are greater than preset values. Classifying the infrared image according to thermal distribution (temperature distribution) based on the second network model to obtain a thermal fault area and a normal area; identifying an infrared target object within the normal area based on the second network model.
The input of the third network model is the output of the first network model and the second network model, after the visible light target object is identified by the first network model, the infrared target object is identified by the second network model, the similarity between the visible light target object and the infrared target object is identified by the third network model, and when the similarity is greater than a preset value, the visible light target object and the infrared target object can be considered as the same target object, and the target object is determined as a reference object. Therefore, the accuracy of reference object selection can be improved. Thereby improving the accuracy of identification of the target object in which the thermal failure occurs.
In the technical solution of step S206, the positional relationship between the thermal failure region and the reference object identified in the infrared image is a positional relationship between the reference object and the target object, and therefore, the visible light target object in which the thermal failure occurs can be specified in the visible light image based on the positional relationship between the thermal failure region and the reference object. Specifically, the center coordinates of the thermal fault region and the reference object coordinates of the reference object are calculated, the specific calculation method of the center coordinates can adopt a k-center point algorithm, the thermal fault region can be fitted into a regular shape, such as a circle, a square, a polygon and the like, and then the center coordinates of the device are calculated; determining a positional relationship of the thermal fault region to the reference based on the center coordinates and the reference coordinates; and mapping the position relation and the reference object coordinate to the visible light image to obtain a visible light target object corresponding to the thermal fault area.
As the focal lengths of the infrared light and the visible light are different, the imaging sizes of the space object on the two images are different, and as an optional embodiment, the pixel difference between the infrared light image and the visible light image between every two circle centers of the calibration plate is calculated, the scaling of the images is obtained, and the sizes of the space object on the two images are unified. According to the obtained scaling factor, the sizes of the space object in the infrared image and the visible light image can be unified. After scaling the infrared and visible images to a uniform size, moving the small size infrared image onto the visible image requires knowledge of the offset distance (x, y). The method comprises the following steps: and calculating the pixel difference of the corresponding point according to the pixel coordinate positions of the circular coordinate positions in the calibration plate in the infrared and visible light images, and aligning the infrared and visible light pixels. Specifically, camera parameters of a visible light image and an infrared image are obtained; in particular, the camera parameters may include resolution, focal length, pixel size, and baseline. Determining an offset of the visible light image from the infrared image based on the camera parameters; and adjusting the visible light image or the infrared image according to the offset, and aligning the visible light image with the infrared image. And determining the visible light target object with the thermal fault based on the position relation between the thermal fault area and the reference object.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, an optical disk) and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the methods according to the embodiments of the present application.
According to another aspect of the embodiment of the application, a target object detection device of an internet data center for implementing the target object detection method of the internet data center is also provided. Fig. 3 is a schematic diagram of an alternative target object detection apparatus of an internet data center according to an embodiment of the present application, and as shown in fig. 3, the apparatus may include:
(1) an obtaining module 302, configured to obtain a visible light image and an infrared image of a patrol inspection area;
(2) the identification module 304 is connected to the acquisition module 302, and is configured to output the visible light image and the infrared image to a trained network model to obtain a thermal fault region and a reference object, where the reference object is a target object whose similarity in the visible light image and the infrared image is greater than a preset value;
(3) and the fault detection module 306 is connected to the identification module 304 and is used for determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
It should be noted that the obtaining module 302 in this embodiment may be configured to execute the step S202, the identifying module 304 in this embodiment may be configured to execute the step S204, and the fault detecting module 306 in this embodiment may be configured to execute the step S206.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may be operated in a hardware environment as shown in fig. 1, and may be implemented by software, or may be implemented by hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the target object detection method of the internet data center, where the electronic device may be a server, a terminal, or a combination thereof.
Fig. 4 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 4, including a processor 402, a communication interface 404, a memory 404, and a communication bus 408, where the processor 402, the communication interface 404, and the memory 406 communicate with each other through the communication bus 408, where,
a memory 406 for storing a computer program;
the processor 402, when executing the computer program stored in the memory 406, performs the following steps:
s1, acquiring a visible light image and an infrared image of the inspection area;
s2, outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object;
and S3, determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
As an example, as shown in fig. 4, the memory 402 may include, but is not limited to, the acquisition module 302, the identification module 304, and the failure detection module 306 of the target object detection apparatus of the internet data center. In addition, the target object detection device may further include, but is not limited to, other module units in the target object detection device of the internet data center, which is not described in detail in this example.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 4 is only an illustration, and the device implementing the method for detecting a target object in an Internet data center may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 4 is a diagram illustrating the structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 4, or have a different configuration than shown in FIG. 4.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in this embodiment, the storage medium may be a program code for executing a method for device screen projection.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
s1, acquiring a visible light image and an infrared image of the inspection area;
s2, outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object;
and S3, determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
An embodiment of the present invention provides an inspection apparatus, including: the visible light camera is used for collecting visible light images; the infrared camera is used for acquiring an infrared image; the electronic device described in the above embodiments. As an exemplary embodiment, the inspection equipment may employ an inspection robot, and inspection may be performed according to a preset route or track.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A target object detection method of an Internet data center is characterized by comprising the following steps:
acquiring a visible light image and an infrared image of a polling area;
outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with similarity between the visible light image and the infrared image larger than a preset value;
and determining the visible light target object with the thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
2. The method of claim 1, wherein the trained network model comprises a first network model and a second network model and a third network model cascaded with the first network model and the second network model;
the outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object comprises:
inputting the visible light image into a first network model to identify a visible light target object;
inputting the infrared image into a second network model to identify an infrared target object and a thermal fault area;
inputting the visible light target object and the infrared target object into a third network model to predict the similarity of the visible light target object and the infrared target, and obtaining the reference object.
3. The method for detecting a target object of an internet data center according to claim 2, wherein determining a visible light target object in which a thermal failure occurs in the visible light image based on a positional relationship between the thermal failure area and the reference object includes:
calculating the center coordinates of the thermal fault area and the reference object coordinates of the reference object;
determining a positional relationship of the thermal fault region to the reference based on the center coordinates and the reference coordinates;
and mapping the position relation and the reference object coordinate to the visible light image to obtain a visible light target object corresponding to the thermal fault area.
4. The method of claim 2, wherein the inputting the infrared image to the second network model to identify the infrared target object and the thermal fault region comprises:
classifying the infrared image according to the thermal distribution based on the second network model to obtain a thermal fault area and a normal area;
identifying an infrared target object within the normal area based on the second network model.
5. The method of claim 1, wherein outputting the visible light image and the infrared image to a trained network model comprises:
acquiring camera parameters of a visible light image and an infrared image;
determining an offset of the visible light image from the infrared image based on the camera parameters;
and adjusting the visible light image or the infrared image according to the offset, and aligning the visible light image with the infrared image.
6. The method of detecting the target object of the internet data center according to claim 1, wherein the acquiring the visible light image and the infrared image of the patrol inspection area includes:
acquiring a visible light video and the infrared video;
recognizing an infrared key frame with a heating area in the infrared video as the infrared image;
and taking the visible light key frame in the visible light video synchronized with the infrared key frame as a visible light image.
7. A target object detection device of an Internet data center is characterized by comprising:
the acquisition module is used for acquiring a visible light image and an infrared image of the inspection area;
the identification module is used for outputting the visible light image and the infrared image to a trained network model to obtain a thermal fault area and a reference object, wherein the reference object is a target object with similarity between the visible light image and the infrared image larger than a preset value;
and the fault detection module is used for determining a visible light target object with a thermal fault in the visible light image based on the position relation between the thermal fault area and the reference object.
8. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor configured to execute the target object detection method steps of the internet data center of any one of claims 1 to 6 by executing the computer program stored on the memory.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the steps of the method for detecting a target object of an internet data center according to any one of claims 1 to 6 when the computer program is run.
10. An inspection device, comprising:
the visible light camera is used for collecting visible light images;
the infrared camera is used for acquiring an infrared image;
the electronic device of claim 8.
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