A method, apparatus and electronic device for detecting a performance indicator

Abstract

The application relates to a method and device for detecting a performance index and an electronic device, which are used for accurately detecting the performance index of a collection device to meet the high performance requirements of some specific application scenarios. The method comprises the following steps: acquiring n frames of images containing a target object, wherein the n frames of images are a plurality of images continuously collected by the collection device within a preset time period, the target object remains in a stationary state within the preset time period; determining the object positions of the target object in each frame of the n frames of images; respectively calculating the distances between the object positions corresponding to the n frames of images and the object position corresponding to the first frame of image; and selecting a distance meeting a preset condition from the plurality of calculated distances as the performance index of the collection device. Based on the above method, the performance index of the collection object can be accurately detected.

Description

Technical Field

[0001] This application relates to the field of performance testing technology, and in particular to a method, apparatus and electronic device for testing performance indicators. Background Technology

[0002] Industrial cameras have advantages such as high image stability, high data transmission, and high anti-interference performance. With the development of industrial manufacturing, images acquired by industrial cameras can be combined with machine vision to obtain corresponding analysis results.

[0003] In high-performance applications, if the industrial camera's performance is substandard or inaccurate, the stability of the images captured by the camera will deteriorate due to environmental factors, leading to unreliable results from image analysis. Therefore, a precise method for testing the performance indicators of industrial cameras is urgently needed. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for detecting performance indicators, used to accurately detect the performance indicators of acquisition equipment.

[0005] In a first aspect, this application provides a method for detecting performance indicators, the method comprising:

[0006] Acquire n frames of images containing the target object; wherein the n frames are multiple images continuously acquired by the acquisition device within a preset time period, and the target object remains stationary during the preset time period;

[0007] Determine the object position of the target object in each frame of the n frames;

[0008] Calculate the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame;

[0009] Among the calculated distances, the distance that meets the preset conditions is selected as the performance index of the acquisition device.

[0010] Based on the above method, the performance indicators of the acquisition equipment can be accurately detected. These performance indicators characterize the performance changes of the acquisition equipment under time-varying factors, environmental factors, and operational factors.

[0011] In one possible design, determining the object position of the target object in each of the n frames of images includes: determining a target region in each of the n frames of images; wherein the target region is the region where the target object is located; calculating the center position of the target region in each frame of images, and using the center position as the object position of the target object in each frame of images.

[0012] The above design allows for a more accurate determination of object location. Since the location is based on the center of the target area, even if the target object shifts or the target area changes, the center of the target object can still reflect the direction and trend of the shift.

[0013] In one possible design, determining the target region in each frame of the n frames includes: traversing each pixel in each frame of the n frames to obtain the grayscale value of each pixel in each frame; filtering the target pixels in each frame and taking the region formed by the target pixels as the target region in each frame; wherein the grayscale value of the target pixel is within a preset range.

[0014] Through the above design, pixel-based traversal can ensure the accuracy of the final determined target area, which in turn helps to improve the accuracy of the performance indicators of the acquisition device calculated later.

[0015] In one possible design, calculating the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame includes: responding to the fact that each of the n frames contains m target objects; wherein the m target objects are separated from each other, and m is an integer greater than or equal to 1; calculating the distance between the object position of the i-th target object in each of the n frames and the object position of the i-th target object in the first frame; wherein i is a positive integer less than or equal to m; and using the calculated distance as the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame.

[0016] Through the above design, it is possible to calculate not only the case where a single frame image contains a single target object, but also the case where a single frame image contains multiple target objects. Since multiple target objects are separate, different target objects can represent different acquisition fields of view of the acquisition device. This helps to improve the global field of view of the acquisition device. The distance calculated based on this can better reflect the changes in the acquisition performance of the acquisition device with the acquisition time or environment.

[0017] In one possible design, selecting a distance that meets preset conditions from the calculated multiple distances as the performance index of the acquisition device includes: determining the images corresponding to each of the calculated multiple distances; determining the acquisition temperature corresponding to each of the multiple distances based on the acquisition temperature of each frame image; filtering out multiple candidate distances from the multiple distances whose acquisition temperatures are within a preset acquisition temperature range; and determining the largest candidate distance among the multiple candidate distances as the performance index of the acquisition device.

[0018] Based on the above design, since some scenarios also have temperature requirements for the acquisition temperature of the acquisition device, the maximum distance in the preset acquisition temperature range that meets the temperature requirements is selected as the performance index of the acquisition device. The performance index obtained in this way can adapt to more specific practical application scenarios, thereby achieving the technical effect of improving the applicability of the performance index.

[0019] In one possible design, selecting a distance that meets preset conditions from multiple calculated distances as the performance index of the acquisition device includes: selecting the maximum distance from multiple calculated distances as the performance index of the acquisition device.

[0020] The above design yields a precise performance metric, which represents the maximum deviation of the acquisition device over time and in response to environmental changes, i.e., the worst-case performance state. In other words, this performance metric accurately measures the minimum performance of the acquisition device. Therefore, this performance metric is applicable to some application scenarios that impose performance requirements on the acquisition device.

[0021] In one possible design, after selecting a distance that meets preset conditions as the performance index of the acquisition device, the method further includes: determining whether the performance index of the acquisition device is less than a preset threshold; if yes, then determining that the performance index of the acquisition device meets the business requirements; if no, then determining that the performance index of the acquisition device does not meet the business requirements.

[0022] Based on the above design, data acquisition devices that meet business requirements and those that do not are selected by judgment.

[0023] Secondly, this application provides an apparatus for detecting performance indicators, the apparatus comprising:

[0024] The acquisition unit acquires n frames of images containing the target object; wherein the n frames of images are multiple images continuously acquired by the acquisition device within a preset time period, and the target object remains stationary during the preset time period;

[0025] The determining unit determines the object position of the target object in each frame of the n-frame image;

[0026] The calculation unit calculates the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame;

[0027] The selection unit selects a distance that meets preset conditions from among the calculated distances as the performance index of the acquisition device.

[0028] In one possible design, the determining unit is specifically used to determine the target region in each frame of the n frames; wherein the target region is the region where the target object is located; calculate the center position of the target region in each frame, and use the center position as the object position of the target object in each frame.

[0029] In one possible design, the determining unit is specifically used to traverse each pixel in each of the n frames of images to obtain the grayscale value of each pixel in each frame of images; filter the target pixels in each frame of images and take the region formed by the target pixels as the target region in each frame of images; wherein the grayscale value of the target pixel is within a preset range.

[0030] In one possible design, the computing unit is specifically configured to respond to the fact that each of the n frames contains m target objects, wherein the m target objects are separated from each other, and m is an integer greater than or equal to 1; calculate the distance between the object position of the i-th target object in each of the n frames and the object position of the i-th target object in the first frame, wherein i is a positive integer less than or equal to m; and use the calculated distance as the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame.

[0031] In one possible design, the selection unit is specifically used to determine the images corresponding to each of the calculated multiple distances, determine the acquisition temperature corresponding to each of the multiple distances based on the acquisition temperature of each frame image, filter out multiple candidate distances from the multiple distances whose acquisition temperatures are within a preset acquisition temperature range, and determine the largest candidate distance among the multiple candidate distances as the performance index of the acquisition device.

[0032] In one possible design, the selection unit is specifically used to select the maximum distance from among the calculated multiple distances as the performance index of the acquisition device.

[0033] In one possible design, the selection unit further includes: determining whether the performance index of the acquisition device is less than a preset threshold; if yes, determining that the performance index of the acquisition device meets the business requirements; if no, determining that the performance index of the acquisition device does not meet the business requirements.

[0034] Thirdly, this application provides an electronic device, the electronic device comprising:

[0035] Memory, used to store computer programs;

[0036] When a processor executes a computer program stored in the memory, it implements the above-described method steps for detecting performance indicators.

[0037] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method steps for detecting performance indicators.

[0038] For the various aspects of the second to fourth aspects mentioned above, and the technical effects that each aspect may achieve, please refer to the above description of the technical effects that can be achieved for the first aspect or the various possible solutions in the first aspect, which will not be repeated here. Attached Figure Description

[0039] Figure 1 A schematic diagram of a system for detecting performance indicators provided in this application;

[0040] Figure 2 A flowchart of a method for detecting performance indicators provided in this application;

[0041] Figure 3 A schematic diagram illustrating an image acquisition method provided in this application;

[0042] Figure 4 A schematic diagram of another image acquisition method provided in this application;

[0043] Figure 5 A schematic diagram of a device for detecting performance indicators provided in this application;

[0044] Figure 6 A schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operating methods in the method embodiments can also be applied to the device embodiments or system embodiments.

[0046] In the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A connected to B can represent: A and B directly connected, or A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.

[0047] To test the performance indicators of industrial cameras, this application provides a system for testing performance indicators, which can be as follows: Figure 1 The system shown may include a platform module 110, an object module 120, a light source module 130, a data acquisition module 140, a sensing module 150, and a processing module 160.

[0048] Among them, the light source module 130 is fixed on the platform module 110, the object module 120 is fixed on the light source module 130, the acquisition module 140 is fixed on the platform module 110, and the acquisition module 140 is located at the position of the acquisition object module 120.

[0049] Platform module 110 consists of a test platform, which includes a physical platform and a fixed support frame fixed to the physical platform. The physical platform is generally constructed from a heavy object, such as a marble weighing up to 1 ton. Here, using a marble platform can achieve integrated fixation of the test platform structure, thus eliminating structural factors such as loose fixing screws and external vibrations that could lead to inaccurate performance test results.

[0050] The object module 120 consists of a test object, which can be a glass calibration plate. The background on the glass calibration plate can be set to white, and black calibration circles are evenly distributed on the white background.

[0051] The light source module 130 consists of a light source module that can provide supplemental lighting for the test object. The light source module specifically includes a backlight and a light source controller.

[0052] The acquisition module 140 consists of acquisition devices, which are used to acquire test objects placed on the light source module and generate images containing the test objects. The acquisition devices specifically include various cameras used for acquiring images.

[0053] The sensing module 150 includes a first temperature probe and a second temperature probe. The first temperature probe is used to acquire the ambient temperature of the test environment, and the second temperature probe is used to acquire the operating temperature of the acquisition device.

[0054] It is worth noting that the sensing module 150 can be used not only to collect temperature but also other information, such as humidity. When collecting humidity, the sensing module 150 includes a first humidity probe and a second humidity probe. The first humidity probe is used to obtain the ambient humidity of the test environment, and the second humidity probe is used to obtain the operating humidity of the data acquisition device. Although this embodiment uses temperature collection as an example, those skilled in the art will know that temperature collection is one possible method, and other information can also be collected. The details will not be repeated below.

[0055] The processing module 160 may include a performance processor, which is used to acquire images acquired by the acquisition device and to acquire temperatures acquired by the first temperature probe and the second temperature probe. Then, the acquired images and temperatures are processed to obtain the performance indicators of the acquisition device.

[0056] Based on the above system, this application provides a method that can be applied to the above system. This method can detect the performance indicators of industrial cameras, achieve accurate verification of the performance indicators of industrial cameras, and thus meet the performance requirements of industrial cameras in some industrial application scenarios with extremely precise positioning requirements.

[0057] It is worth noting that the performance index verification method provided in this application is applicable to various application scenarios, especially those requiring extremely precise positioning, including: high-speed motion scenarios, industrial manufacturing scenarios, microscopic imaging scenarios, machine vision scenarios, scientific research and military scenarios, aerospace scenarios, and intelligent transportation scenarios. For example, in high-speed motion scenarios, the actual performance index of the acquisition device must meet specified requirements; otherwise, when the acquisition device is used in specific high-speed motion scenarios such as sports events and high-speed snapshots, it will cause the target object in the acquired image to shift, thus resulting in analysis errors.

[0058] The method provided in this application will be further described in detail below with reference to the accompanying drawings.

[0059] See Figure 2 As shown, this application provides a method for detecting performance indicators, the specific process of which is as follows:

[0060] Step 201: Obtain n frames of images containing the target object;

[0061] In this embodiment of the application, the performance processor first acquires n frames of images containing the target object. Here, the n frames of images are multiple images continuously acquired by the acquisition device within a preset time period, and the target object remains stationary within the preset time period.

[0062] Specifically, the acquisition device is used to acquire objects within a specified area. These objects can be calibration circles, calibration polygons, etc., and optionally, the objects are evenly distributed within the specified area. When acquiring objects within the specified area, the target object is the specified single or multiple objects.

[0063] like Figure 3As shown, this is an image acquired by the acquisition device for a target image. Multiple black-filled calibration circles are evenly distributed throughout the image; these circles represent objects within a specified region. The five marked calibration circles are located at the top left, bottom left, center, top right, and bottom right corners of the image, respectively. These five marked calibration circles represent the target objects within the specified region. The selection of the target objects will affect the accuracy of the subsequent detection and acquisition device's performance indicators. Therefore, the selection of the target object positions here mainly considers the following two factors: Factor 1, the edge positions of the image acquired by the acquisition device vary relatively greatly near the four corners as the acquisition device's stability changes; Factor 2, the image quality at the center of the image acquired by the acquisition device is better as the acquisition device's stability changes. Therefore, based on these two factors, selecting the five objects at the top left, bottom left, center, top right, and bottom right corners of the image as target objects helps to cover the acquisition device's field of view, thereby improving the accuracy of the subsequent detection and acquisition device's performance indicators.

[0064] Optionally, the acquisition device needs to cool down to room temperature for a certain period of time before it can perform actual acquisition work. The acquisition device acquires images of the target object according to a preset cycle until it has acquired multiple frames of images continuously within the preset time period, and then the image acquisition work is completed.

[0065] For example, using industrial cameras to observe... Figure 3 The target object is then sampled. First, the industrial camera is fixed on the test platform, and the second temperature probe is attached to the lens of the industrial camera, with the probe and camera tightly sealed together using insulating tape. Additionally, the first temperature probe is placed in ambient indoor air. The test object is positioned directly in front of the camera lens, and a backlight module is placed under the object, maintaining a constant backlight output to keep the temperature constant. Then, the industrial camera is activated for image acquisition. The aperture and focus are adjusted to ensure a clear image. The current camera configuration is saved to the default startup, and the camera is powered off and allowed to cool for at least 30 minutes. After the camera has cooled for a certain period, actual image acquisition begins. The camera saves one image every 6 seconds until it reaches thermal stability after 2.5 hours of acquisition, completing the image acquisition process.

[0066] It is worth noting that the above image is considered to be in a clear state when the number of pixels occupied by a single target object in the image is greater than a preset pixel threshold. For example, a single target object occupies at least 65 pixels in the image.

[0067] By following the steps above, n frames of images containing the target object are obtained.

[0068] Step 202: Determine the object position of the target object in each frame of the n frames;

[0069] In this embodiment, the target region, i.e., the region where the target object is located, is determined in each of the n frames of images. Then, the center position of the target region in each frame of images is calculated, and the center position of each frame of images is used as the object position of the target object in each frame of images. Here, a single frame of images may include multiple target objects. If there are two target objects in a single frame of images, then the positions of the two objects can be determined in a single frame of images.

[0070] Specifically, when the target object is a calibration circle, the position of a single target object is the position coordinate of the calibration circle in the image.

[0071] In some implementations, the grayscale value of the target object differs from that of non-target objects. The target region in each frame of n frames can be determined as follows: Each pixel in each of the n frames is traversed to obtain its grayscale value. Then, target pixels with grayscale values ​​within a preset range are selected from each frame, and the region formed by these target pixels is taken as the target region in each frame. Optionally, each pixel in a specified region of each frame is traversed. This specified region can be determined based on actual conditions, and the preset range can also be determined according to the specific application.

[0072] For example, a single frame image captured, such as Figure 4 As shown, a single frame image contains five target objects, represented by black marker circles. Non-target objects, which are the background of the single frame image, are represented by a white background. The five target objects are located at the top left, bottom left, center, top right, and bottom right corners of the single frame image. Generally, grayscale values ​​can be in the range of [0-255]. The closer a pixel's grayscale value is to 0, the darker the pixel; the closer a pixel's grayscale value is to 255, the whiter the pixel. Here, if pixels within the range of [0-10] are set as the pixels constituting the target objects, then by traversing all pixels in the single frame image and selecting all pixels within the range of [0-10] as target pixels, the five target regions where each of the five target objects in the single frame image is located can be determined. Furthermore, the center positions of each of the five target regions can be determined, and these five determined center positions are used as the target positions in the single frame image. Specifically, calipers can be used to determine the target area based on the grayscale values ​​of the target object. Calipers determine the target area by measuring the differences in grayscale values.

[0073] In one possible design, the acquired single-frame image is as follows: Figure 3 As shown, the acquired single-frame image includes five target objects, which are black calibration circles located at the top left, bottom left, center, top right, and bottom right corners of the image. Non-target objects include other black calibration circles and the white background. Here, the calibration circle regions of all black calibration circles are determined based on the difference in grayscale values; specifically, these regions can be determined using calipers. Then, based on the location of the target objects, the calibration circle regions at the top left, bottom left, center, top right, and bottom right corners are defined as the target regions.

[0074] By following the steps above, the position of the target object in each of the n frames of the image can be determined.

[0075] Step 203: Calculate the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame;

[0076] In this embodiment, considering that a single frame image may contain a single target object or multiple target objects, if a single frame image contains m target objects, and the m target objects are separated from each other, where m is an integer greater than or equal to 1, then in response to each of the n frames containing m target objects, the distance between the object position of the i-th target object in each of the n frames and the object position of the i-th target object in the first frame is calculated, where i is a positive integer less than or equal to m. Furthermore, the distance between the object position of each target object in each of the n frames and the object position of each target object in the first frame is calculated, and then the calculated distance is used as the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame.

[0077] Specifically, taking a single frame containing a single target object as an example, the object position in the first frame is (x1, y1), the object position in the second frame is (x2, y2), and so on, with the object position in the nth frame being (x1, y1). n ,y n The distance between the object positions in the first frame image and the object positions in the first frame image is calculated as follows: Calculate the distance between the object position in the second frame image and the object position in the first frame image. Similarly, the distance between the object position in the nth frame and the object position in the first frame is calculated as follows:

[0078] It is worth noting that while the acquisition device is acquiring these n frames of images, the first temperature probe and the second temperature probe are also working, meaning that the acquisition temperature corresponding to each of the n frames can be obtained. The acquisition temperature can be a first acquisition temperature or a second acquisition temperature, where the first acquisition temperature is the ambient temperature acquired by the first temperature probe, and the second acquisition temperature is the operating temperature of the acquisition device acquired by the second temperature probe.

[0079] Furthermore, the calculated distance can be used to determine the corresponding acquisition temperature based on the above relationship. For example, the distance d between the object position in the nth frame image and the object position in the first frame image. n Distance d n The corresponding acquisition temperature is the acquisition temperature TEMP of the nth frame image. n Specifically, this includes the first acquisition temperature corresponding to the nth frame image. Second acquisition temperature

[0080] Step 204: Among the calculated distances, select the distance that meets the preset conditions as the performance index of the acquisition device.

[0081] In this embodiment, a fitting curve is constructed based on the calculated collection temperatures corresponding to multiple distances, with distance as the abscissa and collection temperature as the ordinate. Here, the distances in the fitting curve are obtained for a target object and a collection temperature.

[0082] Specifically, if there are two target objects in a single frame image, meaning the single frame image corresponds to two distances: a first distance and a second distance, and the single frame image also corresponds to two acquisition temperatures: a first acquisition temperature and a second acquisition temperature, then four fitting curves will be constructed: the n first distances of n frames image will construct a first fitting curve with the corresponding n first acquisition temperatures; the n first distances of n frames image will construct a second fitting curve with the corresponding n second acquisition temperatures; the n second distances of n frames image will construct a third fitting curve with the corresponding n first acquisition temperatures; and the n second distances of n frames image will construct a fourth fitting curve with the corresponding n second acquisition temperatures.

[0083] The aforementioned fitted curve characterizes the change in offset of the data acquisition device as the acquisition temperature changes. Generally, when the acquisition temperature reaches a certain value, the offset of the data acquisition device will tend to stabilize, that is, the fitted curve will tend to flatten. Based on this, several candidate distances in the flattened portion of the fitted curve are screened, and then the largest distance among these candidate distances is selected as the performance index of the data acquisition device.

[0084] It is worth noting that the performance indicators of the data acquisition equipment are used to characterize the performance level of the equipment: the smaller the performance indicator, the higher the performance of the data acquisition equipment; the larger the performance indicator, the lower the performance of the data acquisition equipment.

[0085] In some implementations, the maximum distance can be directly selected from multiple calculated distances as the performance index of the acquisition device.

[0086] In some implementations, the performance requirements of the acquisition device's temperature under actual application scenarios will also be considered. In this case, a preset acquisition temperature range that meets the application scenario also needs to be set. Specifically, the images corresponding to the calculated multiple distances are determined. Based on the acquisition temperature of each frame image, the acquisition temperature corresponding to each of the multiple distances is determined. Then, multiple candidate distances whose acquisition temperatures are within the preset acquisition temperature range are selected from the multiple distances. Finally, the largest candidate distance is selected from the multiple candidate distances as the performance index of the acquisition device.

[0087] Furthermore, after determining the performance indicators of the data acquisition device, it will be determined whether the performance indicators of the data acquisition device are less than a preset threshold: if yes, it is determined that the performance indicators of the data acquisition device meet the business requirements; if no, it is determined that the performance indicators of the data acquisition device do not meet the business requirements. Here, the preset threshold can be set according to the business requirements of the actual application scenario.

[0088] The method provided in this application embodiment can accurately detect the performance indicators of the acquisition device. These performance indicators characterize the performance changes of the acquisition device under time-varying factors, environmental factors, and operational factors. Specifically, taking the offset of the acquired image caused by the detection of ambient temperature and operating temperature as an example, the detailed process of a method for detecting performance indicators provided in this application embodiment is described in detail.

[0089] Based on the same inventive concept, this application also provides a device for detecting performance indicators, which accurately detects the performance indicators of the acquisition equipment to meet the high-performance requirements of some specific application scenarios. See [link to relevant documentation]. Figure 5 The device includes:

[0090] Acquisition unit 501 acquires n frames of images containing the target object; wherein, the n frames of images are multiple images continuously acquired by the acquisition device within a preset time period, and the target object remains stationary within the preset time period;

[0091] The determining unit 502 determines the object position of the target object in each frame of the n-frame image;

[0092] The calculation unit 503 calculates the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame;

[0093] Unit 504 selects a distance that meets preset conditions from among the multiple calculated distances as the performance index of the acquisition device.

[0094] In one possible design, the determining unit 502 is specifically used to determine the target region in each frame of the n frames; wherein the target region is the region where the target object is located; calculate the center position of the target region in each frame, and use the center position as the object position of the target object in each frame.

[0095] In one possible design, the determining unit 502 is specifically used to traverse each pixel in each frame of the n frames of images to obtain the grayscale value of each pixel in each frame of images; filter the target pixels in each frame of images and take the region formed by the target pixels as the target region in each frame of images; wherein the grayscale value of the target pixels is within a preset range.

[0096] In one possible design, the computing unit 503 is specifically configured to respond to the fact that each frame of the n frames contains m target objects; wherein the m target objects are separated from each other, and m is an integer greater than or equal to 1; calculate the distance between the object position of the i-th target object in each frame of the n frames and the object position of the i-th target object in the first frame; wherein i is a positive integer less than or equal to m; and use the calculated distance as the distance between the object position corresponding to each of the n frames and the object position corresponding to the first frame.

[0097] In one possible design, the selection unit 504 is specifically used to determine the images corresponding to the calculated multiple distances, determine the acquisition temperature corresponding to each of the multiple distances based on the acquisition temperature of each frame image, filter out multiple candidate distances from the multiple distances whose acquisition temperatures are within a preset acquisition temperature range, and determine the largest candidate distance among the multiple candidate distances as the performance index of the acquisition device.

[0098] In one possible design, the selection unit 504 is specifically used to select the maximum distance from the calculated multiple distances as the performance index of the acquisition device.

[0099] In one possible design, the selection unit 504 further includes: determining whether the performance index of the acquisition device is less than a preset threshold; if yes, determining that the performance index of the acquisition device meets the business requirements; if no, determining that the performance index of the acquisition device does not meet the business requirements.

[0100] Based on the above device, the performance indicators of the data acquisition equipment can be accurately detected. These performance indicators characterize the performance changes of the data acquisition equipment under the influence of time-related factors, environmental factors, and operational factors.

[0101] Based on the same inventive concept, this application also provides an electronic device that can perform the function of the aforementioned device for detecting performance indicators. (Refer to...) Figure 6 The electronic device includes:

[0102] At least one processor 601 and a memory 602 connected to at least one processor 601. In this embodiment, the specific connection medium between the processor 601 and the memory 602 is not limited. Figure 6 The example shown is the connection between processor 601 and memory 602 via bus 600. Bus 600 is... Figure 6 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The 600 bus can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 6 The term is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 601 can also be called a controller; there is no restriction on the name.

[0103] In this embodiment, memory 602 stores instructions executable by at least one processor 601. By executing the instructions stored in memory 602, at least one processor 601 can perform the detection performance index method discussed above. Processor 601 can implement... Figure 5 The functions of each unit in the device shown.

[0104] The processor 601 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 602 and calling data stored in memory 602, the processor can perform various functions and process data, thereby monitoring the device as a whole.

[0105] In one possible design, processor 601 may include one or more processing units. Processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 601. In some embodiments, processor 601 and memory 602 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0106] The processor 601 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the detection performance index method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0107] Memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 602 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 602 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 602 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0108] By designing and programming the processor 601, the code corresponding to the detection performance index method described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the code during operation. Figure 2The steps of the detection performance index method in the illustrated embodiment are described. How to design and program the processor 601 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0109] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the aforementioned method for detecting performance indicators.

[0110] In some possible implementations, various aspects of the detection performance index method provided in this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the detection performance index method according to the various exemplary embodiments of this application described above.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of detecting a performance indicator, characterized by, The method comprises: acquiring n frames of images containing a target object; wherein the n frames of images are a plurality of images continuously acquired by an acquisition device within a preset time period, and the target object remains in a stationary state within the preset time period; determining a target region in each frame of image of the n frames of images; wherein the target region is a region where the target object is located; calculating a center position of the target region in each frame of image, and taking the center position as an object position of the target object in each frame of image; calculating distances between the object position corresponding to each of the n frames of images and the object position corresponding to the first frame of image respectively; wherein each frame of image of the n frames of images contains m target objects, the m target objects are separated from each other, and m is an integer greater than or equal to 1; determining images corresponding to the plurality of distances respectively, determining acquisition temperatures corresponding to the plurality of distances respectively according to the acquisition temperatures of the frames of images; screening a plurality of candidate distances from the plurality of distances, wherein the acquisition temperatures corresponding to the plurality of candidate distances respectively are within a preset acquisition temperature interval; determining a maximum candidate distance in the plurality of candidate distances as a performance index of the acquisition device.

2. The method of claim 1, wherein, The determination of the target region in each frame of image of the n frames of images comprises: traversing each pixel point in each frame of image of the n frames of images to obtain a gray value of each pixel point in each frame of image; screening a target pixel point of each frame of image, and taking a region formed by the target pixel point as a target region in each frame of image; wherein the gray value of the target pixel point is within a preset range.

3. The method of claim 1, wherein, The calculation of the distances between the object position corresponding to each of the n frames of images and the object position corresponding to the first frame of image comprises: calculating distances between the object position of an i-th target object in each frame of image of the n frames of images and the object position of the i-th target object in the first frame of image respectively; wherein i is a positive integer less than or equal to m; taking the calculated distances as the distances between the object position corresponding to each of the n frames of images and the object position corresponding to the first frame of image.

4. The method of claim 1, wherein, After determining the maximum candidate distance in the plurality of candidate distances as the performance index of the acquisition device, the method further comprises: determining whether the performance index of the acquisition device is less than a preset threshold; if yes, determining that the performance index of the acquisition device meets a business requirement; if no, determining that the performance index of the acquisition device does not meet the business requirement.

5. An apparatus for detecting a performance indicator, the apparatus comprising: The apparatus comprises: an acquisition unit configured to acquire n frames of images containing a target object; wherein the n frames of images are a plurality of images continuously acquired by an acquisition device within a preset time period, and the target object remains in a stationary state within the preset time period; a determination unit configured to determine a target region in each frame of image of the n frames of images; wherein the target region is a region where the target object is located; and calculate a center position of the target region in each frame of image, and take the center position as an object position of the target object in each frame of image. The computing unit calculates the distance between the object position corresponding to each of the n frames of images and the object position corresponding to the first frame of image respectively; wherein each frame of image of the n frames of images contains m target objects, the m target objects are separated from each other, and m is an integer greater than or equal to 1; The selecting unit determines the image corresponding to each of the plurality of distances, determines the acquisition temperature corresponding to each of the plurality of distances according to the acquisition temperature of each frame of image, selects a plurality of candidate distances from the plurality of distances, wherein the acquisition temperature corresponding to each of the plurality of candidate distances is within a preset acquisition temperature interval, and determines the maximum candidate distance in the plurality of candidate distances as the performance index of the acquisition device.

6. An electronic device, comprising: Comprise: Memory for storing computer programs; The processor is used to execute the computer program stored in the memory, and realizes the method steps of any one of claims 1-4.

7. A computer readable storage medium characterized by The computer program is stored in the computer readable storage medium, and the computer program is executed by the processor to realize the method steps of any one of claims 1-4.

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