Model-based target detection effect demonstration method and device and storage medium

Abstract

The invention relates to a model-based target detection effect demonstration method and apparatus, and a storage medium. The method comprises the steps of receiving a task scene input by a user; loading a scene file corresponding to the task scene; calling a first model corresponding to the task scene to detect a target object in the scene file to obtain a first detection result; calling a second model corresponding to the task scene to detect a target object in the scene file to obtain a second detection result; and displaying the first detection result corresponding to the first model in a first area of a comparison display area, and displaying the second detection result corresponding to the second model in a second area of the comparison display area.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more specifically, to a model-based target detection effect demonstration method, device and storage medium. Background Art

[0002] Currently, image recognition using models to detect the presence of target objects in images is used in numerous fields, such as autonomous driving, smart homes, and environmental monitoring. The effectiveness of using models to detect target objects in images depends on the model used. To clarify the detection performance of different models for the same scene image, it is necessary to conduct a comparative analysis of the detection results of different models. Summary of the Invention

[0003] One purpose of the embodiments of the present disclosure is to provide a technical solution for demonstrating the target detection effect of a model.

[0004] According to a first aspect of the present disclosure, a model-based target detection effect demonstration method is provided, comprising:

[0005] Task scenarios that receive user input;

[0006] Loading a scenario file corresponding to the task scenario;

[0007] Calling a first model corresponding to the task scenario to detect a target object in the scenario file to obtain a first detection result; and calling a second model corresponding to the task scenario to detect a target object in the scenario file to obtain a second detection result;

[0008] The first detection result corresponding to the first model is displayed in a first area of ​​the comparison display area, and the second detection result corresponding to the second model is displayed in a second area of ​​the comparison display area.

[0009] Optionally, the task scenario of receiving user input includes:

[0010] Displaying multiple scenes in the demonstration list area; wherein the demonstration list area and the comparison display area are different areas of the same page;

[0011] According to the operation of the user selecting a scene in the demonstration list area, the task scene input by the user is determined.

[0012] Optionally, the multiple scenes include scenes captured in real time by a camera, at least one scene of a natural environment, and at least one scene of a noise interference environment.

[0013] Optionally, the scene file is a video file, and displaying the first detection result corresponding to the first model in a first area of ​​the comparison display area, and displaying the second detection result corresponding to the second model in a second area of ​​the comparison display area, includes:

[0014] At a first demonstration time point, displaying a first detection result of the first model for the first frame image in the scene file in the first area, and displaying a second detection result of the second model for the first frame image in the second area;

[0015] At the second demonstration time point, the first detection result of the first model for the second frame image in the scene file is displayed in the first area, and the second detection result of the second model for the second frame image is displayed in the second area; wherein, the second demonstration time point is after the first demonstration time point, and the acquisition time point of the second frame image is after the acquisition time point of the first frame image.

[0016] Optionally, displaying the first detection result corresponding to the first model in the first area of ​​the comparison display area includes:

[0017] Displaying a detected image of the scene file in the first area, and marking a target object detected by the first model and a category of the target object on the detected image;

[0018] Displaying the second detection result corresponding to the second model in the second area of ​​the comparison display area includes:

[0019] The detected image is displayed in the second area, and the target object detected by the second model and the category of the target object are marked on the detected image.

[0020] Optionally, calling a first model corresponding to the task scenario to detect a target object in the scenario file to obtain a first detection result; and calling a second model corresponding to the task scenario to detect a target object in the scenario file to obtain a second detection result, includes:

[0021] Receiving a start demonstration instruction input by a user in a demonstration control area; wherein the demonstration control area and the comparison display area are different areas of the same page;

[0022] According to the start demonstration instruction, the first model is called to detect the target object in the scene file to obtain a first detection result; and the second model is called to detect the target object in the scene file to obtain a second detection result.

[0023] Optionally, the first model is an anti-interference model obtained by training with image samples in an interference environment, and the second model is a traditional model obtained by training with image samples in a conventional environment.

[0024] Optionally, the method further includes:

[0025] The first area also displays first detection effect description information corresponding to the first detection result, and the second area also displays second detection effect description information corresponding to the second detection result.

[0026] According to the second aspect of the present disclosure, a model-based target detection effect demonstration device is also provided, comprising at least one processor and at least one memory, wherein the memory is used to store a computer program, and the processor is used to execute the target detection effect demonstration method as described in the first aspect of the present disclosure under the control of the computer program.

[0027] According to the third aspect of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the target detection effect demonstration method as described in the first aspect of the present disclosure is implemented.

[0028] According to the target detection effect demonstration method of the embodiment of the present disclosure, the corresponding first model and the second model can be called based on the task scenario selected by the user, and target detection can be performed on the same scene file corresponding to the task scenario respectively. The first detection result corresponding to the first model and the second detection result corresponding to the second model can be displayed in the first area and the second area of ​​the comparison display area respectively, so as to compare the detection results of the two models in real time, and then intuitively understand the detection effects of different models in a specific task scenario.

[0029] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0031] Figure 1 is a hardware configuration diagram of an electronic device that can be used to implement the target detection effect demonstration method of an embodiment of the present disclosure;

[0032] Figure 2 is a flowchart of a method for demonstrating target detection effects according to some embodiments;

[0033] Figure 3is a schematic diagram of a demonstration interface corresponding to a method for demonstrating target detection effects according to some embodiments;

[0034] Figure 4 is based on Figure 3 Schematic diagram showing the test results on the demonstration interface;

[0035] Figure 5 2 is a schematic diagram of the hardware structure of a target detection effect demonstration device according to some embodiments. DETAILED DESCRIPTION

[0036] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application.

[0037] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0038] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0039] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0040] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0041] <Implementation Environment and Hardware Configuration>

[0042] Figure 1 FIG. 1 is a hardware configuration diagram of an electronic device 1000 that can be applied to the target detection demonstration method according to an embodiment of the present disclosure.

[0043] like Figure 1 As shown, the electronic device 1000 may include a processor 1101, a memory 1102, an interface device 1103, a communication device 1104, a display device 1105, an input device 1106, and the like. Figure 1 The hardware configuration shown is merely illustrative and is in no way intended to limit the disclosure, its application, or uses.

[0044] The processor 1101 is used to execute a computer program, which can be written using an instruction set of an architecture such as x86, Arm, RISC, MIPS, or SSE. The memory 1102 includes, for example, ROM (read-only memory), RAM (random access memory), and non-volatile memory such as a hard disk. The interface device 1103 includes, for example, a USB interface, a network cable interface, a headphone jack, and the like. The communication device 1104 is capable of wired or wireless communication. The communication device 1104 may include at least one short-range communication module, such as any module that performs short-range wireless communication based on a short-range wireless communication protocol such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, or LiFi. The communication device 1104 may also include a long-range communication module, such as any module that performs WLAN, GPRS, or 2G / 3G / 4G / 5G long-range communication. The display device 1105 may be, for example, an LCD display or a touch screen display. The input device 1106 may include, for example, a touch screen, a keyboard, a microphone, and the like.

[0045] In this embodiment, the memory 1102 of the electronic device 1000 is used to store a computer program, which is used to control the processor 1101 to operate so as to execute the target detection demonstration method according to any embodiment of the present disclosure.

[0046] <Method Example>

[0047] Figure 2 FIG. 1 shows a flow chart of a target detection demonstration method according to some embodiments. The method may be performed as follows, for example: Figure 1 The electronic device 1000 is shown as an implementation.

[0048] like Figure 2 As shown, the method of this embodiment may include the following steps S210 to S240:

[0049] Step S210: receiving a task scenario input by a user.

[0050] The task scenario represents the scenario in which the model detection task is performed, and may be, for example, a preset natural environment scenario, a preset noise interference environment scenario, or a scenario in which a camera is started to collect data in real time.

[0051] The natural environment can be a regular daytime environment, a low-light environment, a strong light environment against sunlight, a strong light environment when entering or exiting a tunnel, or a haze environment, etc.

[0052] The noise interference environment can be to set motion blur image noise on the basis of the regular daytime environment, set salt and pepper image noise on the basis of the strong light environment against sunlight, apply strong light interference on the basis of the strong light environment of entering and exiting tunnels, apply weak light interference on the basis of the haze environment, add an artificial intelligence anti-interference environment on the basis of the regular daytime environment, add artificial intelligence anti-interference on the basis of the weak light environment, add an artificial intelligence anti-interference environment on the basis of the haze environment, etc.

[0053] In some examples, multiple scenarios may be pre-set for user selection to facilitate user input. In these examples, receiving the task scenario input by the user in step S210 may include the following steps: displaying multiple scenarios in a demonstration list area; and determining the task scenario input by the user based on the user selecting a scenario in the demonstration list area.

[0054] See also Figure 3 As shown, the demonstration page can be provided with a demonstration list area A2 and a comparison display area A1, which are different areas of the same page (i.e., the demonstration page). The demonstration list area A2 displays a scene list, which includes multiple scenes for the user to select. For example, if the user selects the daytime normal scene, the task scene entered by the user is the daytime normal scene.

[0055] The multiple scenes displayed in the demonstration list area A2 may include scenes captured by a camera in real time, at least one scene of a natural environment, and at least one scene of a noise interference environment.

[0056] The at least one natural environment scene includes, for example, at least one scene of a normal daytime environment, a low-light environment, a strong light environment against sunlight, a strong light environment when entering or exiting a tunnel, or a haze environment.

[0057] At least one scenario of a noise interference environment includes, for example, setting motion blur image noise based on a conventional natural environment, setting salt and pepper image noise based on a strong light environment against sunlight, applying strong light interference based on a strong light environment when entering and exiting a tunnel, applying weak light interference based on a haze environment, adding an artificial intelligence anti-interference environment based on a conventional daytime environment, adding artificial intelligence anti-interference based on a weak light environment, adding artificial intelligence anti-interference based on a haze environment, etc.

[0058] Step S220: loading a scene file corresponding to the task scene.

[0059] In step S220, according to the task scene input by the user, a scene file corresponding to the task scene is loaded. The scene file may include at least one frame of image. For example, when the scene file is a video file, it includes multiple frames of image.

[0060] When the task scene is a scene captured by a camera in real time, in step S220 , the camera is started to capture a scene file, and the scene file captured by the camera is loaded.

[0061] The electronic device can pre-store scene files corresponding to natural environments and scene files corresponding to noise interference environments. When the task scenario is a natural environment scene or a noise interference environment scene, the scene file corresponding to the task scenario can be directly loaded from the scene library to be input into the model for recognition.

[0062] Step S230: calling the first model corresponding to the task scenario to detect the target object in the scenario file to obtain a first detection result; and calling the second model corresponding to the task scenario to detect the target object in the scenario file to obtain a second detection result.

[0063] The electronic device 1000 may pre-store at least one set of models, each set including a first model and a second model, to compare the detection effects of the first model and the second model. This embodiment performs brain-like recognition on an image using the first model and the second model, and compares the detection effects of brain-like recognition by displaying the detection results of the two models.

[0064] The first model can be, for example, an anti-interference model, and the second model can be, for example, a traditional model, wherein the anti-interference model is a model obtained by training image samples in an interference environment, such as the image samples in the above-mentioned noise interference environment; the traditional model is a model obtained by training image samples in a conventional environment, such as the image samples in the above-mentioned natural environment, and can be trained based on the YOLOV5 image set, etc.

[0065] A group of models can correspond to one scene, or two or more scenes, without limitation here.

[0066] In step S230, a group of models corresponding to the task scene are called to perform image recognition on the scene file to obtain a first detection result corresponding to the first model and a second detection result corresponding to the second model.

[0067] When the scene file is a video file, step S230 can perform frame-by-frame detection on multiple frames in the video file, or perform cross-frame detection according to a set frame interval. There is no limitation here, but the first model and the second model should respectively detect the same frames of the video file to compare the detection effects.

[0068] Step S240 , displaying the first detection result corresponding to the first model in a first area of ​​the comparison display area, and displaying the second detection result corresponding to the second model in a second area of ​​the comparison display area.

[0069] In step S240, Figure 4 As shown, the first detection result R1 corresponding to the first model and the second detection result R2 corresponding to the second model are displayed in the comparison display area A1 of the demonstration page.

[0070] The first detection result R1 and the second detection result R2 include the location of the target object identified in the image, the category of the target object, etc. For example, the category of the target object includes pedestrians, vehicles, trees, buildings, etc.

[0071] In step S240, displaying the first detection result corresponding to the first model in the first area of ​​the comparison display area may include: displaying the detected image of the scene file in the first area, and marking the target object detected by the first model and the category of the target object on the detected image.

[0072] Correspondingly, displaying the second detection result corresponding to the second model in the second area of ​​the comparison display area may include: displaying the detected image in the second area, and marking the target object detected by the second model and the category of the target object on the detected image.

[0073] See also Figure 4 , the detected target object can be marked with a rectangular frame on the detected image, and the type of the corresponding target object can be displayed around the rectangular frame.

[0074] In some examples, first detection effect description information corresponding to the first detection result may be displayed in the first area of ​​the comparison display area, and second detection effect description information corresponding to the second detection result may be displayed in the second area.

[0075] The first detection effect description information and the second detection effect description information include, for example, the actual total number of target objects, the total number of detected target objects, the total number of missed detections, the detection time, and other information.

[0076] See also Figure 4 , the first detection effect description information T1 can be displayed at the bottom of the first area, and the second detection effect description information T2 can be displayed at the bottom of the second area.

[0077] In an example where the scene file is a video file, step S240 of displaying the first detection result corresponding to the first model in a first area of ​​a comparison display area and displaying the second detection result corresponding to the second model in a second area of ​​the comparison display area may include the following steps S2401 and S2402:

[0078] Step S2401: At a first demonstration time point, a first detection result of a first model for a first frame image in a scene file is displayed in the first area, and a second detection result of a second model for the first frame image is displayed in the second area.

[0079] Step S2402: At the second demonstration time point, the first detection result of the first model for the second frame image in the scene file is displayed in the first area, and the second detection result of the second model for the second frame image is displayed in the second area; wherein, the second demonstration time point is after the first demonstration time point, and the acquisition time point of the second frame image is after the acquisition time point of the first frame image.

[0080] In this example, the first frame image and the second frame image can be two consecutive frame images in the scene file, or can be two frame images extracted from the scene file with a certain number of frames between them, which is not limited here. Figure 4 For video files, the detection results of the two models for different image frames will be dynamically displayed in the comparison display area to dynamically demonstrate the continuous detection of the two models for video files.

[0081] In some examples, a first thread and a second thread can be created, and the first thread calls the first model to perform image detection and output the corresponding first detection result, and the second thread calls the second model to perform image detection and output the corresponding second detection result, etc.

[0082] According to the above steps S210 to S240, in the method of this embodiment, the corresponding first model and the second model are called based on the task scenario selected by the user, and target detection is performed on the same scene file corresponding to the task scenario, and the first detection result corresponding to the first model and the second detection result corresponding to the second model are displayed in the first area and the second area of ​​the comparison display area respectively, so as to compare the detection results of the two models in real time, and then intuitively understand the detection effects of different models in a specific task scenario.

[0083] In some embodiments, a demonstration control area can also be set on the demonstration page, and the demonstration control area and the comparison display area are different areas of the same page. The demonstration control area can provide various demonstration control controls to facilitate the user to control the demonstration process. In these embodiments, in step S230, calling the first model of the corresponding task scene to detect the target object in the scene file to obtain a first detection result; and calling the second model of the corresponding task scene to detect the target object in the scene file to obtain a second detection result, can include: receiving a start demonstration instruction input by the user in the demonstration control area; wherein the demonstration control area and the comparison display area are different areas of the same page; and, according to the start demonstration instruction, calling the first model to detect the target object in the scene file to obtain a first detection result; and calling the second model to detect the target object in the scene file to obtain a second detection result.

[0084] In these embodiments, Figure 3 As shown, the demonstration control area A3 includes, for example, a start control K1 for the user to trigger the start demonstration instruction. The demonstration control area can also provide a pause control K2 and a stop control K3. The pause control K2 allows the user to trigger the pause demonstration instruction. When the pause demonstration instruction is received while the demonstration is in progress, the model can be paused for detection. Accordingly, the comparison display area will maintain the currently output detection results and no longer dynamically update them, so that the user can observe and compare the detection effects of the two models for the current image frame. When the pause demonstration instruction is received while the demonstration is paused, the detection output can continue. The stop control K3 allows the user to trigger the stop demonstration instruction. When the stop demonstration instruction is received, the output in the comparison display area A1 can be turned off, etc.

[0085] <Device Example>

[0086] In this embodiment, a target detection effect demonstration device is also provided. Figure 5 As shown, the target detection effect demonstration device 500 may include a processor 510 and a memory 520, wherein the memory 520 stores a computer program, and when the computer program is run by the processor 510, the target detection effect demonstration method according to any embodiment of the present disclosure is executed.

[0087] The target detection effect demonstration device 500 can be any electronic device with display and computing functions, for example, it can be a PC, a laptop computer, a mobile phone or other terminal device, which is not limited here.

[0088] Target detection effect demonstration device 500 and Figure 1 The electronic device 1000 may have the same or similar hardware structure configuration, or may have a different hardware structure, which is not limited here.

[0089] <Medium Example>

[0090] In this embodiment, a computer-readable storage medium is further provided, on which computer instructions are stored. When the computer instructions are executed by a processor, a target detection effect demonstration method as in any embodiment of the present disclosure is implemented.

[0091] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0092] Computer-readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device.Computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device or any suitable combination thereof.More specific examples (non-exhaustive list) of computer-readable storage medium include: 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), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove having instructions stored thereon, and any suitable combination thereof.Computer-readable storage medium used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[0093] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0094] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0095] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0096] These computer-readable program instructions can be provided to a processor 1100 of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that, when these instructions are executed by the processor 1100 of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. These computer-readable program instructions can also be stored in a computer-readable storage medium. These instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams.

[0097] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0098] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and a part of the module, program segment or instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes 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 box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0099] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A model-based target detection effect demonstration method, characterized in that: include: Task scenarios that receive user input; Loading a scene file corresponding to the task scene; Calling a first model corresponding to the task scenario to detect a target object in the scenario file to obtain a first detection result; and calling a second model corresponding to the task scenario to detect a target object in the scenario file to obtain a second detection result; The first detection result corresponding to the first model is displayed in a first area of ​​the comparison display area, and the second detection result corresponding to the second model is displayed in a second area of ​​the comparison display area.

2. The method according to claim 1, characterized in that The task scenario of receiving user input includes: Displaying multiple scenes in the demonstration list area; wherein the demonstration list area and the comparison display area are different areas of the same page; According to the operation of the user selecting a scene in the demonstration list area, the task scene input by the user is determined.

3. The method according to claim 2, characterized in that The multiple scenes include scenes captured by a camera in real time, at least one scene of a natural environment, and at least one scene of a noise interference environment.

4. The method according to claim 1, characterized in that: The scene file is a video file, and the displaying of the first detection result corresponding to the first model in the first area of ​​the comparison display area, and the displaying of the second detection result corresponding to the second model in the second area of ​​the comparison display area, include: At a first demonstration time point, displaying in the first area a first detection result of the first model for a first frame image in the scene file, and displaying in the second area a second detection result of the second model for the first frame image; At the second demonstration time point, the first detection result of the first model for the second frame image in the scene file is displayed in the first area, and the second detection result of the second model for the second frame image is displayed in the second area; wherein the second demonstration time point is after the first demonstration time point, and the acquisition time point of the second frame image is after the acquisition time point of the first frame image.

5. The method according to claim 1, characterized in that The displaying the first detection result corresponding to the first model in the first area of ​​the comparison display area includes: Displaying the detected image of the scene file in the first area, and marking the target object detected by the first model and the category of the target object on the detected image; The displaying the second detection result corresponding to the second model in the second area of ​​the comparison display area includes: The detected image is displayed in the second area, and the target object detected by the second model and the category of the target object are marked on the detected image.

6. The method according to any one of claims 1 to 5, characterized in that calling a first model corresponding to the task scenario to detect a target object in the scenario file to obtain a first detection result; and calling a second model corresponding to the task scenario to detect the target object in the scenario file to obtain a second detection result, including: Receiving a start demonstration instruction input by a user in a demonstration control area; wherein the demonstration control area and the comparison display area are different areas of the same page; According to the start demonstration instruction, the first model is called to detect the target object in the scene file to obtain a first detection result; and the second model is called to detect the target object in the scene file to obtain a second detection result.

7. The method according to any one of claims 1 to 5, characterized in that The first model is an anti-interference model trained by image samples in an interference environment, and the second model is a traditional model trained by image samples in a conventional environment.

8. The method according to any one of claims 1 to 5, characterized in that The method further comprises: The first area also displays first detection effect description information corresponding to the first detection result, and the second area also displays second detection effect description information corresponding to the second detection result.

9. A model-based target detection effect demonstration device, characterized in that: It includes at least one processor and at least one memory, the memory is used to store a computer program, and the processor is used to execute the target detection effect demonstration method according to any one of claims 1 to 8 under the control of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the target detection effect demonstration method according to any one of claims 1 to 8 is implemented.

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