Detection method and device, electronic equipment and storage medium

Abstract

The invention relates to a detection method and device, electronic equipment and a storage medium, a first type of camera and a second type of camera are installed in a target scene, and the first type of camera is used for acquiring a first video image of a first target area in the target scene; the second type camera is used for acquiring a second video image of a second target area in the target scene; the method comprises the following steps: performing first target object detection on the first target area in the target scene based on the first video image; when it is detected that a first target object exists in the first target area, performing second target object anomaly detection on the second target area in the target scene based on the second video image; and when it is detected that the second target object in the second target area is abnormal, generating prompt information. Therefore, by fusing the detection results of the video images obtained by different types of cameras, the security detection can be more accurately carried out on the target scene.

Description

Technical Field

[0001] The present disclosure relates to the field of detection technology, and in particular to a detection method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of network technology, the number and size of computer rooms are also increasing rapidly, and the maintenance and security of computer rooms are of vital importance. The existing computer room security monitoring system usually consists of a software monitoring system for monitoring servers and a hardware monitoring system for monitoring environmental parameters in the computer room.

[0003] In the related technologies, the operation and maintenance personnel of the computer room generally monitor the computer room based on the monitoring images captured by the software monitoring system and the parameters such as temperature or humidity collected by the hardware monitoring system to maintain the normal operation of the computer room. However, in many cases, the layout of the equipment in the computer room is complex and the environmental factors change rapidly, and the related technologies may not be able to accurately and timely obtain the monitoring results of the equipment. Summary of the invention

[0004] The present disclosure provides a detection method, device, electronic device and storage medium.

[0005] According to one aspect of the present disclosure, a detection method is provided, wherein a first type of camera and a second type of camera are installed in a target scene, wherein the first type of camera is used to obtain a first video image of a first target area in the target scene; and the second type of camera is used to obtain a second video image of a second target area in the target scene; the method comprises:

[0006] Performing first target object detection on the first target area in the target scene based on the first video image;

[0007] In the case where a first target object is detected in the first target area, performing second target object abnormality detection on the second target area in the target scene based on the second video image;

[0008] When it is detected that the second target area has an abnormality with the second target object, prompt information is generated.

[0009] According to another aspect of the present disclosure, a detection device is provided, wherein a first type of camera and a second type of camera are installed in a target scene, wherein the first type of camera is used to obtain a first video image of a first target area in the target scene; and the second type of camera is used to obtain a second video image of a second target area in the target scene; the device comprises:

[0010] A first target object detection module, configured to perform first target object detection on the first target area in the target scene based on the first video image;

[0011] A second target object detection module, configured to perform second target object anomaly detection on the second target area in the target scene based on the second video image when a first target object is detected in the first target area;

[0012] The prompt information generating module is used to generate prompt information when it is detected that there is an abnormality in the second target object in the second target area.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0014] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0015] The detection method, device, electronic device and storage medium provided by the embodiments of the present disclosure install a first type of camera and a second type of camera in the target scene, detect the first target object based on the first video image of the first target area obtained by the first type of camera, and perform abnormal detection of the second target object on the second target area in the target scene based on the second video image when the first target object exists in the first target area, and generate prompt information when the second target object is abnormal in the second target area. In the embodiments, by fusing the detection results of video images obtained by cameras of different categories, the target scene can be more accurately detected for safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0017] Figure 1 A flow chart of a detection method provided for an exemplary embodiment of the present disclosure;

[0018] Figure 2 A schematic diagram of a scenario provided for an exemplary embodiment of the present disclosure;

[0019] Figure 3 A background image in a computer room provided as an exemplary embodiment of the present disclosure;

[0020] Figure 4 An image of a leftover object detected in a machine room provided by an exemplary embodiment of the present disclosure;

[0021] Figure 5A schematic block diagram of functional modules of a detection device provided by an exemplary embodiment of the present disclosure;

[0022] Figure 6 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0023] Figure 7 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] 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 described herein, which are instead provided for 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.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0026] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this 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.

[0027] 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".

[0028] 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.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] In order to effectively monitor the computer room, in the embodiment, a camera is installed in the computer room, and the video images collected by the camera are analyzed to obtain monitoring results of whether there are any abnormalities in the computer room. For example, whether there are people entering the computer room; whether the cabinet is invaded; whether there are any items left in the computer room after the people in the computer room leave; whether there are any abnormalities in the lighting in the computer room; whether there are any abnormalities in the target sensors in the computer room, etc.

[0033] In the embodiment, in order to achieve the above monitoring results, multiple types of cameras can be installed in the computer room, such as a camera installed in the computer room to monitor whether there are people passing through the aisle; a camera installed in the area where the cabinet is located to monitor whether the cabinet is invaded by people or tools; a camera installed in front of the target sensor to monitor whether the target sensor is abnormal, and a camera installed in the computer room to monitor whether the light brightness change in the computer room is abnormal. It should be noted that the camera in the embodiment may include a storage medium and a processor, and the memory may be equipped with a computer program of a corresponding image detection or recognition algorithm, and the computer program stored in the memory is executed by the processor in the camera. The camera may also include a data transceiver module, and the execution result of the computer program is sent to the server through the data transceiver module. The server performs fusion processing according to the execution results sent by each camera to obtain the monitoring result of the computer room. According to the monitoring result, if an invasion occurs in the computer room, an invasion prompt information will be generated. At the same time, each camera can also receive control instructions sent by the server.

[0034] In the embodiment, the server may also directly obtain the video surveillance images sent by each camera, and obtain the monitoring result of the computer room by processing the video surveillance.

[0035] In order to achieve accurate monitoring in the target scene, in the embodiment, a first type of camera and a second type of camera are installed in the target scene. The first type of camera is used to obtain a first video image of a first target area in the target scene; the second type of camera is used to obtain a second video image of a second target area in the target scene; the detection method provided in the embodiment of the present disclosure, such as Figure 1 As shown, the method may include the following steps:

[0036] In step S110 , first target object detection is performed on a first target area in a target scene based on the first video image.

[0037] In the embodiment, the target scene may specifically be a computer room scene, or a factory scene, a related office scene, or other similar scenes, etc. In the embodiment, the target scene is described as a scene in a computer room, but the embodiment is not limited thereto.

[0038] The number of the first type of cameras can be multiple. In an embodiment, areas such as corridors in a computer room where people pass or areas in a computer room that can accommodate people can be obtained and used as the first target area. The first target area includes the area where the first target object passes, and the video image of the first target area obtained by the first type of camera is used as the first video image.

[0039] In the embodiment, each camera in the first category of cameras can be equipped with an image monitoring algorithm to jointly monitor whether there are first target objects such as personnel in the computer room, so as to avoid over-reliance on the single-point detection result of a certain camera and cause false detection. For example, during the detection process of a single-point detection of a camera, due to the limitations of the algorithm, missed detection may occur. Through the linkage detection of multiple cameras, it is possible to more comprehensively and accurately monitor whether there are personnel in the computer room.

[0040] In the embodiment, if at least one camera in the first category of cameras detects a human body, the computer room will enter the occupied mode. If all cameras in the first category of cameras do not detect a human body for a period of time, the computer room will enter the unmanned mode.

[0041] It should be noted that the first target object in the embodiment can be a human body, other organisms, or movable objects such as robots. By detecting the first target object, an alarm can be issued when other organisms or robots enter the computer room to avoid endangering the safety of the computer room. In the embodiment, the robot is not limited to drones, unmanned vehicles, etc. For example, there may be illegal personnel who deliberately leave these unmanned devices in the computer room and cause harm to the computer room or steal data through remote control. Therefore, by analyzing the first video image, a detection result of whether the first target object exists in the first target area can be obtained. In the embodiment, the first target object is a person as an example for explanation.

[0042] In the embodiment, Figure 2 As shown, when detecting people through the first video image, recognition detection can be performed through a trained preset model, and the preset model can be a neural network model. For example, by obtaining multiple video images of the first target area in the computer room, the video screen of the video image contains people moving in the first target area, and the people in the video screen can be marked by a rectangular frame to obtain multiple sample images corresponding to the first target area. The preset model is trained using these sample images to obtain a trained preset model. By inputting the first video image into the trained preset model, it can be identified whether there are people in the first target area, and a detection result of whether the first target object exists in the first target area can be obtained.

[0043] In the embodiment, a human body detection model of an ultra-small transport volume can be customized and installed in the first type of camera, so as to quickly detect whether the first target object exists in the first target area.

[0044] In step S120, when it is detected that the first target object exists in the first target area, a second target object abnormality detection is performed on the second target area in the target scene based on the second video image.

[0045] In the embodiment, the second target object may be a cabinet or a safe or other related equipment. The embodiment is described by taking the cabinet as the second target object. If it is detected that there are people in the corridor or other areas in the computer room, a second video image of the cabinet or other equipment in the computer room can be obtained by the second type of camera. The second type of camera is used to obtain the video surveillance screen of the second target area in the computer room. The second target area may be the area where the second target object such as the cabinet is located or the area where it is placed. The number of the second type of cameras may be multiple, and they may be installed in front of or above the cabinet or other equipment. The second video image can be used to determine whether there are people or tools invading the cabinet, for example, whether there are people opening the cabinet or entering the cabinet.

[0046] In the embodiment, when the first target object is detected in the first target area by the first video image acquired by the first type of camera, the second target area can be subjected to abnormal detection of the second target object based on the second target video image. This can improve the monitoring efficiency of the computer room monitoring and avoid the situation where the second target object abnormality detection is still being performed when the presence of a person in the computer room is not detected.

[0047] In step S130, when it is detected that there is an abnormality of the second target object in the second target area, prompt information is generated.

[0048] If an abnormality of the second target object is detected in the second target area, a prompt message will be generated, so that the operation and maintenance personnel or security personnel can be reminded in time to confirm, for example, whether it is a normal inspection by the staff or an operation by an illegal person.

[0049] It should be noted that in the embodiment, in the process of performing abnormal detection of the second target object in the second target area in the computer room based on the second video image, the second video image can be analyzed. If a person or tool operates the cabinet, such as opening or entering the cabinet, it can be determined that there is an abnormality in the second target object. In the embodiment, it is also possible to determine whether the person is a legitimate staff member by obtaining an image of the person operating the cabinet, such as entering a face video, and if the person is not a legitimate staff member, a prompt message is generated; otherwise, if the person is a legitimate staff member, no prompt message is generated.

[0050] The detection method provided by the embodiment of the present disclosure installs a first type of camera and a second type of camera in the computer room, performs first target object detection based on the first video image of the first target area obtained by the first type of camera, performs second target object abnormality detection on the second target area in the computer room based on the second video image when the first target object exists in the first target area, and generates prompt information when the second target object is abnormal in the second target area. In the embodiment, by fusing the detection results of video images obtained by cameras of different categories, the computer room can be more accurately detected for safety.

[0051] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S120 may further include the following steps:

[0052] Step S121, obtaining a reference image of the second target area, wherein the reference image is an image of the second target area obtained before the first target object is detected in the first target area.

[0053] In an embodiment, the timing of performing abnormal detection of the second target object in the second target area may be to perform abnormal detection of the second target object in the second target area when the first target object is detected in the first target area. It may also be to perform frame difference method on the second video image to detect moving objects when the first target object is detected in the first target area, so as to realize the detection of the first target object in the second target area. In this way, when there are many cabinets in the computer room and the number of second-class cameras is relatively large, the efficiency of detection can be improved. For example, by comparing adjacent video frames of the second video image, if there is a change in the current video frame, the number of motion frames can be accumulated, and if the number of motion frames is greater than a threshold, the second target object abnormal detection can be performed on the second target area. In an embodiment, performing abnormal detection of the second target in the second target area may be cabinet intrusion detection on the second target area, etc.

[0054] Step S122, comparing the second video image with the reference image to obtain a comparison result.

[0055] Step S123: when the comparison result shows that the video frame of the second video image is in a changing state, determining that there is an abnormality in the second target object in the second target area.

[0056] In the embodiment, the reference image is equivalent to the base map of the second target area in the computer room, that is, the image obtained when the first target object is not detected in the computer room. By comparing the second video image with the reference image, if there is a difference between the second video image and the reference image, such as a large difference or a difference for several consecutive frames, it can be determined that the video picture of the second video image is in a changing state, otherwise the video picture of the second video image is in a static state. If the video picture of the second video image is in a changing state, it can be determined that there is an abnormality of the second target object in the second target area.

[0057] When it is detected that the video screen of the second video image is in a changing state, prompt information is continuously generated and reported until the video screen of the second video image is in a static state for a period of time, for example, after the video screen of the second video image is in a static state for 60 seconds, the prompt information is stopped from being generated.

[0058] In an embodiment, when it is detected that the image in the second video image changes from a changing state to a static state, the current video image of the second target area is acquired by the second type of camera, and the reference image is updated based on the current video image of the second target area. For example, after the video image of the second video image is in a static state for 60 seconds, the current video image of the target area acquired by the second type of camera is used as the latest reference image to replace the previous reference image, thereby updating the reference image. This facilitates comparison of the subsequently acquired second video image with the updated reference image to determine whether the image in the second video has changed. For example, after the operation and maintenance personnel perform maintenance on the cabinet, they may make changes to the cabinet, etc., so that the video image of the second target area obtained may also change, so it is necessary to update the reference image to avoid false alarms.

[0059] Based on the above embodiment, in another embodiment provided by the present disclosure, the method may further include the following steps:

[0060] Step S140 , when it is detected that the first target area does not have the first target object, the first video image is compared with the background image of the first target area to obtain a comparison result.

[0061] Step S150, when the comparison result includes a comparison difference, determining the location information of the comparison difference, and generating a legacy prompt information including the comparison difference and the location information.

[0062] In the embodiment, when there are people in the computer room, most of the time it is the operation and maintenance personnel who maintain the servers in the computer room. The operation and maintenance personnel often carry tool boxes or related equipment, such as servers, and replace or repair certain servers. After the operation and maintenance personnel leave, they may leave related equipment or tools, or even the outer packaging of the equipment in the computer room, so it is necessary to detect the leftovers in the computer room.

[0063] Since the first target area such as the corridor in the computer room is an activity area for personnel, the first video image obtained by the first type of camera can be used to perform a detection of remains to detect whether there are remains in the first target area. In the embodiment, the detection of remains can be specifically implemented by customizing a comparison detection model, storing a background image, and detecting the difference between the current image and the background image in real time through the comparison detection model to accurately locate the difference position.

[0064] like Figure 3 and Figure 4 As shown, Figure 3 is the background image in the first target area in the computer room, Figure 4In order to detect the image of the leftovers in the first target area, by comparing the first video image with the background image, it is possible to accurately detect whether there are leftovers in the first target area of ​​the computer room and the specific location of the leftovers. For example, by determining the location information where there is a comparison difference, and generating leftovers prompt information including the comparison difference and the location information, the staff can take away the leftovers in time.

[0065] Based on the above embodiment, abnormality detection can also be performed on the illumination in the computer room. For example, if the illumination in the computer room suddenly increases or decreases, it means that the power in the computer room may be affected. Therefore, in another embodiment provided by the present disclosure, the method can also include the following steps:

[0066] Step S160, obtaining lighting information within a preset time period in the target scene, wherein the lighting information includes light brightness.

[0067] Step S170, obtaining the brightness change value of the light brightness within a preset time period.

[0068] Step S180: When the brightness change value is greater than a threshold, abnormal lighting prompt information is generated.

[0069] In the embodiment, when the first target object is detected in the first target area, it indicates that there are people in the computer room. The video images can be further obtained through the sensors installed in the computer room to detect whether the illumination information in the computer room has changed over a period of time, such as whether the brightness of the illumination has changed. If the illumination changes, and the brightness change value is greater than a certain threshold, such as the brightness suddenly becomes very bright or the brightness suddenly becomes very dark, it is likely that the people in the computer room have affected the electricity in the computer room, such as the voltage in the computer room may have changed, which may endanger the normal operation of the server, so it is necessary to generate illumination abnormality prompt information to facilitate relevant staff to take timely measures.

[0070] Based on the above embodiment, the target sensor in the machine room can also be monitored. For example, once there is a problem with the alarm system in the target sensor and the pointer in the target sensor is located in an abnormal area, an alarm can be issued in time to facilitate the staff to take timely measures. Therefore, the method can also include the following steps:

[0071] Step S191, obtaining a result display area of ​​the target sensor in the third video image.

[0072] Step S192: When the result display area includes an abnormal area, target sensor abnormality prompt information is generated.

[0073] In an embodiment, a third type of camera may be installed in a target scene such as a computer room, and the third type of camera may be used to obtain a third video image of the target sensor. For example, the third type of camera may be installed above the target sensor to facilitate real-time acquisition of the video image of the target sensor.

[0074] In an embodiment, the target sensor may display the result in the result display area of ​​the dial. For example, when the result display area includes an abnormal area, target sensor abnormality prompt information may be generated; otherwise, target sensor abnormality prompt information may not be generated.

[0075] In an embodiment, the dial of the target sensor generally includes two areas, namely a normal area and an abnormal area. If the pointer is located in the normal area, it means that the data acquired by the target sensor is normal, and the result display area displays normal. If the pointer is located in the abnormal area, the result display area will include the abnormal area, indicating that the data acquired by the target sensor is abnormal, and it is necessary to generate target sensor abnormal prompt information. Therefore, the area where the pointer of the target sensor is located can be obtained by performing regional segmentation on the third video image, that is, the pointer area. If the pointer area overlaps with the abnormal area, it means that the pointer is located in the abnormal area. When it is determined that the pointer area overlaps with the abnormal area, an abnormal alarm of the target sensor is issued. In this way, the sensor of the target sensor can be assisted to issue an alarm. Once there is a problem with the sensor alarm system of the target sensor, a prompt can also be issued through the timely generated target sensor abnormal prompt information.

[0076] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a detection device, which may be a server or a chip applied to a server. Figure 5 A schematic block diagram of the functional modules of a detection device provided for an exemplary embodiment of the present disclosure. A first type of camera and a second type of camera are installed in a target scene, wherein the first type of camera is used to obtain a first video image of a first target area in the target scene; the second type of camera is used to obtain a second video image of a second target area in the target scene; Figure 5 As shown, the detection device comprises:

[0077] A first target object detection module 10, configured to perform first target object detection on the first target area in the target scene based on the first video image;

[0078] A second target object detection module 20 is used to perform second target object abnormality detection on the second target area in the target scene based on the second video image when a first target object is detected in the first target area;

[0079] The prompt information generating module 30 is used to generate prompt information when it is detected that there is an abnormality in the second target object in the second target area.

[0080] In yet another embodiment provided by the present disclosure, the first target area includes an area for the first target object to pass through; and the second target area includes an area for the second target object to be placed.

[0081] In another embodiment provided by the present disclosure, the second target object detection module is specifically used to:

[0082] Acquire a reference image of the second target area, where the reference image is an image of the second target area obtained before the first target object is detected in the first target area;

[0083] Comparing the second video image with the reference image to obtain a comparison result;

[0084] When the comparison result is that the video frame of the second video image is in a changing state, it is determined that there is an abnormality of the second target object in the second target area.

[0085] In another embodiment provided by the present disclosure, the device further includes:

[0086] An image acquisition module, configured to acquire a current video image of the second target area through the second type of camera when detecting that the image frame in the second video image changes from a changing state to a static state;

[0087] A reference image updating module is used to update the reference image based on the current video image of the second target area.

[0088] In another embodiment provided by the present disclosure, the device further includes:

[0089] a comparison module, configured to compare the first video image with a background image of the first target area to obtain a comparison result when it is detected that the first target object does not exist in the first target area;

[0090] The prompt information generating module is used to determine the position information of the comparison difference when the comparison result includes the comparison difference, and generate the legacy prompt information including the comparison difference and the position information.

[0091] In another embodiment provided by the present disclosure, the device further includes:

[0092] A lighting information acquisition module, used to acquire lighting information within a preset time period in the target scene, wherein the lighting information includes light brightness;

[0093] A brightness change value acquisition module, used to acquire the brightness change value of the light brightness within the preset time period;

[0094] The illumination abnormality prompt information generating module is used to generate illumination abnormality prompt information when the brightness change value is greater than a threshold value.

[0095] In another embodiment provided by the present disclosure, a third type of camera is also installed in the target scene, and the third type of camera is used to obtain a third video image of the target sensor; the device also includes:

[0096] A result display area acquisition module, used to acquire the result display area of ​​the target sensor in the third video image;

[0097] The target sensor abnormality prompt information generating module is used to generate target sensor abnormality prompt information when the result display area includes an abnormal area.

[0098] The relevant device part corresponds to the above-mentioned method embodiment. For details, please refer to the description of the method embodiment, which will not be repeated here.

[0099] The detection device provided by the embodiment of the present disclosure installs a first type of camera and a second type of camera in the target scene, performs first target object detection based on a first video image of a first target area obtained by the first type of camera, performs second target object abnormality detection on a second target area in the target scene based on the second video image when the first target object exists in the first target area, and generates prompt information when the second target object is abnormal in the second target area. In the embodiment, by fusing the detection results of video images obtained by cameras of different categories, the target scene can be more accurately detected for safety.

[0100] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0101] Figure 6 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801 , and the processor 1801 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0102] The processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software. The processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.

[0103] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 7 The computer system 1900 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 7 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0104] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] like Figure 7As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 to a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the computer system 1900 can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0106] A plurality of components in the computer system 1900 are connected to the I / O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. The input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 may include, but is not limited to, a disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0107] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1900 via the ROM 1902 and / or the communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0108] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0109] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0110] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0111] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.

[0112] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of 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.

[0113] 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.

[0114] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary 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 chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0117] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A detection method, characterized in that: A first type of camera and a second type of camera are installed in the target scene, wherein the first type of camera is used to obtain a first video image of a first target area in the target scene; The second type of camera is used to obtain a second video image of a second target area in the target scene; the method includes: Performing first target object detection on the first target area in the target scene based on the first video image; In the case where a first target object is detected in the first target area, performing second target object abnormality detection on the second target area in the target scene based on the second video image; When it is detected that the second target area has an abnormality with the second target object, prompt information is generated.

2. The method according to claim 1, characterized in that The first target area includes an area for the first target object to pass through; the second target area includes an area where the second target object is placed.

3. The method according to claim 1 or 2, characterized in that: The performing second target object abnormality detection on the second target area in the target scene based on the second video image includes: Acquire a reference image of the second target area, where the reference image is an image of the second target area obtained before the first target object is detected in the first target area; Comparing the second video image with the reference image to obtain a comparison result; When the comparison result is that the video frame of the second video image is in a changing state, it is determined that there is an abnormality of the second target object in the second target area.

4. The method according to claim 3, characterized in that The method further comprises: When it is detected that the image frame in the second video image changes from a changing state to a static state, acquiring a current video image of the second target area through the second type of camera; The reference image is updated based on the current video image of the second target area.

5. The method according to claim 1, characterized in that The method further comprises: When it is detected that the first target area does not contain the first target object, the first video image is compared with the background image of the first target area to obtain a comparison result; In the case where the comparison result includes a comparison difference, position information of the comparison difference is determined, and legacy prompt information including the comparison difference and the position information is generated.

6. The method according to claim 1, characterized in that The method further comprises: Acquire lighting information within a preset time period in the target scene, the lighting information including light brightness; Obtaining a brightness change value of the light brightness within the preset time period; When the brightness change value is greater than a threshold, abnormal lighting prompt information is generated.

7. The method according to claim 1, characterized in that A third type of camera is also installed in the target scene, and the third type of camera is used to obtain a third video image of the target sensor; the method also includes: Acquire a result display area of ​​the target sensor in the third video image; When the result display area includes an abnormal area, target sensor abnormality prompt information is generated.

8. A detection device, characterized in that: A first type of camera and a second type of camera are installed in the target scene, wherein the first type of camera is used to obtain a first video image of a first target area in the target scene; The second type of camera is used to obtain a second video image of a second target area in the target scene; the device includes: A first target object detection module, configured to perform first target object detection on the first target area in the target scene based on the first video image; A second target object detection module, configured to perform second target object anomaly detection on the second target area in the target scene based on the second video image when a first target object is detected in the first target area; The prompt information generating module is used to generate prompt information when it is detected that there is an abnormality in the second target object in the second target area.

9. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 7.

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