Approximate ranging method and system, and imager using same

Abstract

The present disclosure provides an approximate ranging method, comprising: identifying a species to which a target object in an imaged image belongs; acquiring a first pixel occupancy count of pixels occupied by a preset area of the target object in the imaged image in a preset direction, wherein the preset direction is a horizontal direction or a vertical direction; on the basis of the first pixel occupancy count, the resolution of a detector image, and the resolution of the imaged image, determining a second pixel occupancy count of pixels occupied by the preset area of the target object in the detector image in the preset direction; on the basis of the second pixel occupancy count and a pixel size of a detector module used for generating the detector image, determining a physical length of the preset area of the target object in the preset direction when mapped onto the detector module; on the basis of the species, querying from a preset database an average actual length of the preset area of the target object in the preset direction; and, on the basis of the physical length, the average actual length, and a shooting focal length, calculating an approximate ranging distance corresponding to the target object.

Description

Approximate distance measurement methods, systems, and imagers Technical Field

[0001] This disclosure relates to the field of ranging technology for imaging devices, and in particular to a general ranging method, system and imager thereof. Background Technology

[0002] Integrating approximate distance measurement functionality into imagers has become a mainstream design. Currently, this function is implemented by adding a button or knob to the imager. Users adjust the spacing of the distance measuring cursor on the screen (controlled by the button or knob) to approximate the distance between a specific organism and the detector. However, this method only measures a single species, and the button / knob adjustment has a time lag, resulting in inaccurate, real-time distance measurement. Summary of the Invention

[0003] To effectively solve at least one of the technical problems existing in the prior art, this disclosure provides a general ranging method, system and imager thereof.

[0004] In a first aspect, this disclosure provides a rough distance measurement method, comprising:

[0005] Identify the species of a target object in an imaging image;

[0006] The number of pixels occupied by a preset region of the target object in the imaging image in a preset direction is obtained, wherein the preset direction is either horizontal or vertical.

[0007] Based on the first pixel occupancy number, the resolution of the detector image, and the resolution of the imaging image, determine the second pixel occupancy number of the preset region of the target object in the detector image in the preset direction;

[0008] Based on the number of pixels occupied by the second pixel and the pixel size of the detector module used to generate the detector image, the physical length of the preset area of ​​the target object in the preset direction when it is mapped onto the detector module is determined.

[0009] Based on the species to which the target object belongs, the average entity length of the target object in a preset direction in a preset region is retrieved from a preset database.

[0010] The approximate distance to the target object is calculated based on the physical length, the average entity length, and the shooting focal length.

[0011] In some embodiments, the resolution of the imaging image is W1*H1, and the resolution of the detector image is W2*H2, where W1 and H1 are the horizontal and vertical pixel counts of the imaging image, respectively, and W2 and H2 are the horizontal and vertical pixel counts of the detector image, respectively.

[0012] The number of pixels occupied by the second pixel is equal to the product of the number of pixels occupied by the first pixel and the scaling factor corresponding to the preset direction, rounded down.

[0013] Wherein, when the preset direction is horizontal, the scaling factor is equal to W2 / W1, and when the preset direction is horizontal, the scaling factor is equal to H2 / H1.

[0014] In some embodiments, the pixel size includes: the physical width of a unit pixel of the detector module in the horizontal direction and the physical height in the vertical direction;

[0015] When the preset direction is horizontal, the physical length is equal to the product of the number of second pixels occupied and the physical width of a unit pixel in the horizontal direction;

[0016] When the preset direction is vertical, the physical length is equal to the product of the number of second pixels occupied and the physical height of the unit pixel in the vertical direction.

[0017] In some embodiments, the approximate distance is equal to the quotient of the product of the average entity length and the shooting focal length and the physical length.

[0018] In some embodiments, the preset area is the entire area of ​​the target object, and the preset direction is the vertical direction.

[0019] In some embodiments, when the species of the target object in the imaging image is an animal, the preset region is the head region of the animal object.

[0020] In some embodiments, prior to the step of identifying the species to which the target object belongs in the imaging image, the method further includes:

[0021] An image recognition model is obtained by training a model using a preset image data training set, which includes multiple training images and their corresponding species labels.

[0022] The step of identifying the species to which the target object belongs in the imaging image includes:

[0023] The trained image recognition model is used to identify target objects in the imaging image to determine the species to which the target object belongs and the regional location information of the target object in the imaging image.

[0024] In a second aspect, embodiments of this disclosure provide a rough ranging system, configured to implement the rough ranging method provided in the first aspect, the rough ranging system comprising:

[0025] The identification module is configured to identify the species to which the target object belongs in the imaging image;

[0026] The acquisition module is configured to acquire the first pixel occupancy number of pixels occupied by a preset region of the target object in the imaging image in a preset direction, wherein the preset direction is a horizontal direction or a vertical direction;

[0027] The first determining module is configured to determine, based on the first pixel occupancy number, the resolution of the detector image, and the resolution of the imaging image, the second pixel occupancy number of the preset region of the target object in the detector image in the preset direction;

[0028] The second determining module is configured to determine the physical length of the preset region of the target object in a preset direction when it is mapped onto the detector module, based on the second pixel occupancy quantity and the pixel size of the detector module used to generate the detector image;

[0029] The query module is configured to query the average entity length of a preset region of the target object in a preset direction from a preset database based on the species to which the target object belongs;

[0030] The calculation module is configured to calculate the approximate distance to the target object based on the physical length, the average entity length, and the shooting focal length.

[0031] Thirdly, embodiments of this disclosure also provide an imager, comprising:

[0032] The detector module is configured to generate a corresponding detector image based on the received optical signal;

[0033] An imaging module configured to convert detector images into imaging images that can be displayed;

[0034] The approximate distance measurement system employs the approximate distance measurement system described in the second aspect.

[0035] In some embodiments, the approximate ranging system is integrated within the imaging module.

[0036] The technical solution provided in this disclosure automatically identifies the species of the target object in the imaging image and the number of first pixels occupied by a preset region of the target object in a preset direction. Then, based on the number of first pixels occupied, it determines the number of second pixels occupied by the preset region of the target object in the detector image in the preset direction. Next, based on the number of second pixels occupied, it determines the physical length of the preset region of the target object when mapped onto the detector module in the preset direction. Finally, it queries a preset database to find the corresponding average entity length based on the species of the target object. Finally, it roughly calculates the distance between the target object and the imager (user) based on the obtained physical length, average entity length, and the imager's focal length. This technical solution can automatically identify the species of the target object and automatically calculate the distance between the target object and the imager. The entire process requires no manual intervention and is rapid and efficient. Attached Figure Description

[0037] Figure 1 is a schematic diagram of the imaging display performed by the imager in an embodiment of this disclosure.

[0038] Figure 2 is a flowchart of a general distance measurement method provided in an embodiment of this disclosure.

[0039] Figure 3 is a schematic diagram of the approximate ranging distance between the target object and the imager determined based on the similarity principle in an embodiment of this disclosure.

[0040] Figure 4 is a structural block diagram of a rough ranging system provided in an embodiment of this disclosure.

[0041] Figure 5 is a structural block diagram of an imaging device provided in an embodiment of this disclosure.

[0042] Figure 6 is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of this disclosure, the disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0045] In the various figures, the same elements are represented by similar reference numerals. For clarity, not all parts in the figures are drawn to scale. Furthermore, some well-known parts may not be shown in the figures.

[0046] Many specific details of this disclosure are described below to provide a clearer understanding of it. However, as those skilled in the art will understand, this disclosure may be implemented without following these specific details.

[0047] In this disclosure, the imager can be an infrared thermal imager, a visible light imager, or a low-light imager, and can also be a sight, gun sight, camera, or other structure or device with imaging capabilities.

[0048] Figure 1 is a schematic diagram of the imaging display performed by the imager in an embodiment of this disclosure. As shown in Figure 1, when a user needs to measure the distance between themselves and a target object (e.g., an animal or a plant), they can observe the target object using a handheld imager. At this time, each pixel in the detector module (e.g., an infrared detector module or a visible light detector module) of the imager generates a corresponding electrical signal based on the received light signal. The electrical signals generated by all pixels can form an electrical signal matrix, which yields a detector image (essentially an electrical signal matrix that cannot be directly displayed; the resolution of the detector image is completely consistent with the resolution of the detector module). The imaging module converts the received detector image into an image that can be displayed. The specific conversion process is conventional technology in the art and will not be described in detail here. The resolution of the image and the resolution of the detector image can be the same or different; this disclosure does not limit this.

[0049] Figure 2 is a flowchart of a rough ranging method provided in an embodiment of this disclosure. As shown in Figure 2, the rough ranging method is applied to an imager, which includes a detector module and an imaging module. The detector module is configured to generate a corresponding detector image based on a received light signal, and the imaging module is configured to convert the detector image into an imaging image that can be displayed. The rough ranging method includes:

[0050] Step S1: Identify the species of the target object in the imaging image.

[0051] In some embodiments, before performing species identification on the imaging images generated by the imaging module, the imaging images can be preprocessed, such as image noise reduction, image enhancement, grayscale processing, etc., to improve the accuracy of species identification in step S1.

[0052] Step S2: Obtain the number of pixels occupied by the first pixel in the preset region of the target object in the imaging image in the preset direction, where the preset direction is either horizontal or vertical.

[0053] Step S3: Based on the number of first pixels occupied, the resolution of the detector image, and the resolution of the imaging image, determine the number of second pixels occupied by the preset area of ​​the target object in the preset direction in the detector image.

[0054] Step S4: Based on the number of second pixels occupied and the pixel size of the detector module, determine the physical length of the preset area of ​​the target object in the preset direction when it is mapped onto the detector module.

[0055] Step S5: Query the preset database to find the average entity length of the preset region of the target object in the preset direction according to the species it belongs to.

[0056] The preset database contains the average length of different entities of different species in different regions in a preset direction (horizontal or vertical).

[0057] Step S6: Calculate the approximate distance between the target object and the imager based on the physical length, average entity length, and the imager's focal length.

[0058] In this disclosure, the species of the target object in the imaging image and the number of first pixels occupied by a preset region of the target object in a preset direction are automatically identified. Then, based on the number of first pixels occupied, the number of second pixels occupied by the preset region of the target object in the detector image in the preset direction is determined. Next, based on the number of second pixels occupied, the physical length of the preset region of the target object mapped onto the detector module in the preset direction is determined. The average entity length corresponding to the species of the target object is retrieved from a preset database. Finally, the distance between the target object and the imager (user) is roughly calculated based on the obtained physical length, average entity length, and the imager's focal length. The technical solution of this disclosure can automatically identify the species of the target object and automatically calculate the distance between the target object and the imager. The entire process requires no manual intervention and the measurement process is rapid and efficient.

[0059] In some embodiments, step S0 is included before step S1.

[0060] Step S0: Train the model using a preset image data training set to obtain an image recognition model.

[0061] In practical applications, images of various species can be collected in advance as training images, and a corresponding species label can be assigned to each training image; for example, images of various animals (deer, horses, sheep, lions, leopards, etc.) or plants (bamboo, mulberry trees, sycamore trees, etc.) can be used to obtain a preset image data training set that can be used to train a deep learning model. The more species types included in the preset image data training set, the more species the image recognition model can identify.

[0062] The model training process generally includes the following two steps: 1) extracting image feature vectors from the training images; 2) training the model based on the image feature vectors. The image features in the image feature vectors can include image edges, textures, color histograms, local feature points, etc. The model used for training can be a classifier model (such as SVM, Adaboost) or a deep learning-based object detection model. Deep learning-based object detection models can be divided into two categories: two-stage algorithms and one-stage algorithms. Two-stage algorithms, such as the R-CNN series, Fast R-CNN, and Faster R-CNN, first generate candidate regions and then classify and regress these regions. One-stage algorithms, such as YOLO, SSD, and DenseBox, achieve end-to-end detection by running convolutional networks.

[0063] At this point, step S1 specifically includes: using the trained image recognition model to identify the target object in the imaging image, so as to determine the species to which the target object belongs and the regional location information of the target object in the imaging image.

[0064] When using a trained image recognition model to identify target objects in an image, it will not only output the species to which the target object belongs, but also obtain the regional location information of the identified target object in the image (usually a rectangular box will identify the target object, or a bounding box formed along the edge of the target object).

[0065] As an optional implementation, the preset area is the entire area of ​​the target object, and the preset direction is the vertical direction. That is to say, what is obtained in step S2 is the overall height of the target object in the imaging image (represented by the number of pixels occupied).

[0066] Considering that in actual distance measurement, the animal's posture directly affects its height in the image, for example, the overall height of an animal standing will be greater than that of an animal crouching (or lying down). Therefore, using the overall height of the target object in the image as the basis for distance measurement in step S2 would result in a significant deviation between the approximate distance measurement and the actual distance.

[0067] To address the aforementioned technical problems, this disclosure selects the head region of the animal object in the imaging image as the measurement target. The preset direction can be horizontal (in which case step S2 corresponds to measuring the head width) or vertical (in which case step S2 corresponds to measuring the head length). Users can preset this direction according to their actual needs. This is because the heads of most animals are spherical or nearly spherical, and the measured length or width of the head remains essentially constant or fluctuates within a small range when the animal object is in different postures. Furthermore, even if the heads of a small number of animals are irregularly shaped, practical experience shows that the head length or width measured from different angles remains essentially constant or fluctuates within a small range.

[0068] As an optional approach, users can pre-select the specific area of ​​the target object to be measured within the preset area, and choose the preset direction as horizontal or vertical, based on the species of the target object. Alternatively, the system can automatically determine the area referred to by the "preset area" and the direction referred to by the "preset direction" after determining the species of the target object in step S1.

[0069] As an example, if a user wants to measure the distance between themselves and a cedar tree, they can pre-configure the preset area as "the whole cedar tree area" and the preset direction as "vertical" before step S1; or, after step S1, a selection option will pop up, allowing the user to select the preset area as "the whole cedar tree area" and the preset direction as "vertical"; or, after the result of step S1, the "preset area" and "preset direction" can be determined by a pre-configured automatic configuration algorithm (a pre-designed algorithm that can determine the "preset area" and "preset direction" used in step S2 based on the species obtained in step S1).

[0070] Therefore, the "preset area" and "preset direction" in this disclosure can be selected or pre-configured by the user, or they can be automatically determined by the system according to the species of the target object through the corresponding configuration algorithm. This disclosure does not limit this.

[0071] In some embodiments, the resolution of the imaging image is W1*H1, and the resolution of the detector image is W2*H2, where W1 and H1 are the horizontal and vertical pixel counts of the imaging image, respectively, and W2 and H2 are the horizontal and vertical pixel counts of the detector image, respectively. The number of second pixels occupied is equal to the product of the number of first pixels occupied and the scaling factor corresponding to the preset direction, rounded down. Wherein, the scaling factor corresponding to the preset direction is horizontal is equal to W2 / W1, and the scaling factor corresponding to the preset direction is horizontal is equal to H2 / H1.

[0072] In other words, in step S3, the number of pixels occupied by the second pixel, L2, can be determined based on the following formula: L2=round(L1*α)

[0073] Where, round() represents the rounding function, L1 represents the number of pixels occupied by the first pixel, and α represents the scaling factor in the preset direction. When the preset direction is horizontal, α = W2 / W1, and when the preset direction is vertical, α = H2 / H1.

[0074] In some embodiments, the pixel size includes: the physical width W3 of a unit pixel in the detector module in the horizontal direction and the physical height H3 in the vertical direction; when the preset direction is horizontal, the physical length is equal to the product of the number of second pixels occupied and the physical width of the unit pixel in the horizontal direction; when the preset direction is vertical, the physical length is equal to the product of the number of second pixels occupied and the physical height of the unit pixel in the vertical direction.

[0075] In other words, the physical length L3 can be determined in step S4 based on the following formula: L3=L2*β

[0076] α represents the unit length in the preset direction, where β = W3 when the preset direction is horizontal and β = H3 when the preset direction is vertical.

[0077] Figure 3 is a schematic diagram illustrating the approximate distance between the target object and the imager determined based on the similarity principle in an embodiment of this disclosure. As shown in Figure 3, the preset direction is vertical. In some embodiments, the approximate distance is equal to the quotient of the product of the average entity length and the shooting focal length and the physical length.

[0078] In this case, triangle ABC is similar to triangle AB'C', and f / D = L in the diagram. avg / L3, f represents the shooting focal length, D represents the approximate distance between the target object and the imager, L avg Let L represent the average entity length, and L3 represent the physical length. Accordingly, we can derive: D = L avg *f / L3.

[0079] Based on the same inventive concept, this disclosure also provides a rough ranging system. Figure 4 is a structural block diagram of a rough ranging system provided in this disclosure. As shown in Figure 4, this rough ranging system can implement the rough ranging method provided in the previous embodiments. This rough ranging system is applied to an imager, which includes a detector module and an imaging module. The detector module is configured to generate a corresponding detector image based on the received light signal, and the imaging module is configured to convert the detector image into an imaging image that can be displayed. The rough ranging system includes an identification module, an acquisition module, a first determination module, a second determination module, a query module, and a calculation module.

[0080] The identification module is configured to identify the species to which the target object in the imaging image belongs.

[0081] The acquisition module is configured to acquire the number of pixels occupied by a preset region of a target object in an image in a preset direction, where the preset direction is either horizontal or vertical.

[0082] The first determining module is configured to determine the second pixel occupancy number of pixels occupied by the preset region of the target object in the preset direction in the detector image based on the first pixel occupancy number, the resolution of the detector image, and the resolution of the imaging image.

[0083] The second determining module is configured to determine the physical length of the target object's preset area mapped onto the detector module in a preset direction based on the number of second pixels occupied and the pixel size of the detector module.

[0084] The query module is configured to retrieve the average entity length of a preset region of a target object in a preset direction from a preset database based on the species it belongs to.

[0085] The calculation module is configured to roughly calculate the distance between the target object and the imager based on the physical length, average entity length, and the imager's shooting focal length.

[0086] For a detailed description of each of the above functional modules, please refer to the corresponding content in the previous method embodiments, which will not be repeated here.

[0087] Based on the same inventive concept, this disclosure also provides an imager. Figure 5 is a structural block diagram of an imager provided in an embodiment of this disclosure. As shown in Figure 5, the imager includes: a detector module, an imaging module, and a rough ranging system.

[0088] The detector module is configured to generate a corresponding detector image based on the received light signal; the imaging module is configured to convert the detector image into an imaging image that can be displayed; the approximate ranging system adopts the approximate ranging system provided in the previous embodiment.

[0089] For a detailed description of the detector module, imaging module, and approximate ranging system, please refer to the relevant content in the preceding method embodiments; it will not be repeated here.

[0090] In some embodiments, the general ranging system is integrated into the imaging module.

[0091] Based on the same inventive concept, this disclosure also provides an electronic device. Figure 6 is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. As shown in Figure 6, the electronic device provided by this disclosure includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the approximate ranging methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0092] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0093] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0094] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0095] According to embodiments of this disclosure, a computer-readable medium is also provided. This computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the approximate ranging methods described above.

[0096] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this disclosure.

[0097] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0099] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A rough distance measurement method, wherein, include: Identify the species of a target object in an imaging image; The number of pixels occupied by a preset region of the target object in the imaging image in a preset direction is obtained, wherein the preset direction is either horizontal or vertical. Based on the first pixel occupancy number, the resolution of the detector image, and the resolution of the imaging image, determine the second pixel occupancy number of the preset region of the target object in the detector image in the preset direction; Based on the number of pixels occupied by the second pixel and the pixel size of the detector module used to generate the detector image, the physical length of the preset area of ​​the target object in the preset direction when it is mapped onto the detector module is determined. Based on the species to which the target object belongs, the average entity length of the target object in a preset direction in a preset region is retrieved from a preset database. The approximate distance to the target object is calculated based on the physical length, the average entity length, and the shooting focal length.

2. The method according to claim 1, wherein, The resolution of the imaging image is W1*H1, and the resolution of the detector image is W2*H2, where W1 and H1 are the horizontal and vertical pixel counts of the imaging image, respectively, and W2 and H2 are the horizontal and vertical pixel counts of the detector image, respectively. The number of pixels occupied by the second pixel is equal to the product of the number of pixels occupied by the first pixel and the scaling factor corresponding to the preset direction, rounded down. Wherein, when the preset direction is horizontal, the scaling factor is equal to W2 / W1, and when the preset direction is horizontal, the scaling factor is equal to H2 / H1.

3. The method according to claim 1, wherein, The pixel size includes: the physical width of a unit pixel in the detector module in the horizontal direction and the physical height in the vertical direction; When the preset direction is horizontal, the physical length is equal to the product of the number of second pixels occupied and the physical width of a unit pixel in the horizontal direction; When the preset direction is vertical, the physical length is equal to the product of the number of second pixels occupied and the physical height of the unit pixel in the vertical direction.

4. The method according to claim 1, wherein, The approximate distance is equal to the quotient of the product of the average entity length and the shooting focal length, and the physical length.

5. The method according to claim 1, wherein, The preset area is the entire area of ​​the target object, and the preset direction is the vertical direction.

6. The method according to claim 1, wherein, When the target object in the imaging image belongs to an animal species, the preset area is the head region of the animal object.

7. The method according to any one of claims 1 to 6, wherein, Prior to the step of identifying the species to which the target object belongs in the imaging image, the method further includes: An image recognition model is obtained by training a model using a preset image data training set, which includes multiple training images and their corresponding species labels. The step of identifying the species to which the target object belongs in the imaging image includes: The trained image recognition model is used to identify target objects in the imaging image to determine the species to which the target object belongs and the regional location information of the target object in the imaging image.

8. A rough distance measurement system, wherein, The approximate ranging system is configured to implement the approximate ranging method as described in any one of claims 1 to 7, the approximate ranging system comprising: The identification module is configured to identify the species to which the target object belongs in the imaging image; The acquisition module is configured to acquire the first pixel occupancy number of pixels occupied by a preset region of the target object in the imaging image in a preset direction, wherein the preset direction is a horizontal direction or a vertical direction; The first determining module is configured to determine, based on the first pixel occupancy number, the resolution of the detector image, and the resolution of the imaging image, the second pixel occupancy number of the preset region of the target object in the detector image in the preset direction; The second determining module is configured to determine the physical length of the preset region of the target object in a preset direction when it is mapped onto the detector module, based on the second pixel occupancy quantity and the pixel size of the detector module used to generate the detector image; The query module is configured to query the average entity length of a preset region of the target object in a preset direction from a preset database based on the species to which the target object belongs; The calculation module is configured to calculate the approximate distance to the target object based on the physical length, the average entity length, and the shooting focal length.

9. An imager, wherein, include: The detector module is configured to generate a corresponding detector image based on the received optical signal; An imaging module configured to convert detector images into imaging images that can be displayed; The approximate distance measurement system employs the approximate distance measurement system as described in claim 8.

10. The imager according to claim 9, wherein, The approximate ranging system is integrated into the imaging module.

Images