An obstacle recognition method, device, equipment, medium and weeding robot

Abstract

An embodiment of the present invention discloses an obstacle recognition method, device, equipment, medium and weeding robot. The method includes: obtaining chromaticity information and lightness information of a candidate weeding area image; generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram; determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information. By running the technical solution provided by the embodiment of the present invention, it is possible to solve the problem that in the prior art, the boundary of the weeding area of the weeding robot is usually calibrated by burying a boundary line, which consumes a large amount of manpower and material resources and increases the cost. And because there are limitations in the burying of the boundary line, to a certain extent, the shape of the weeding area is restricted, and the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of the weeding robot is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to computer technology, and in particular, to an obstacle recognition method, device, equipment, medium and weeding robot. Background Art

[0002] With the improvement of living standards, people are increasingly concerned about environmental construction, so the construction of urban greening gardens has received more and more attention. At the same time, efficient greening maintenance, such as daily weeding, has gradually become a need. However, since traditional weeders require manual operation, weeding robots with autonomous working functions have gradually emerged.

[0003] In the prior art, the boundary of the weeding area of the weeding robot is usually calibrated by burying a boundary line, which consumes a lot of manpower and material resources and increases the cost. And due to the limitations of burying the boundary line, for example, the angle of the corner cannot be less than 90 degrees, to a certain extent, the shape of the weeding area is restricted. Summary of the Invention

[0004] The embodiments of the present invention provide an obstacle recognition method, device, equipment, medium and weeding robot to achieve the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of the weeding robot.

[0005] In a first aspect, the embodiments of the present invention provide an obstacle recognition method, which includes:

[0006] Obtain the chromaticity information and lightness information of the candidate weeding area image;

[0007] Generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain the peak information of the target chromaticity histogram;

[0008] Determine whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information.

[0009] In a second aspect, the embodiments of the present invention further provide an obstacle recognition device, which includes:

[0010] An information acquisition module, configured to obtain the chromaticity information and lightness information of the candidate weeding area image;

[0011] A histogram generation module, configured to generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain the peak information of the target chromaticity histogram;

[0012] An obstacle determination module, configured to determine whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes:

[0014] One or more processors;

[0015] A storage device for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the obstacle recognition method as described above.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the obstacle recognition method as described above is implemented.

[0018] In a fifth aspect, an embodiment of the present invention further provides a weeding robot, which includes a robot body and also includes the above-mentioned electronic device.

[0019] In the embodiment of the present invention, by obtaining the chromaticity information and lightness information of a candidate weeding area image; generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram; determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information. It solves the problem in the prior art that usually a boundary line is buried to calibrate the boundary of the weeding area of the weeding robot, which consumes a lot of manpower and material resources and increases the cost. And due to the limitations in burying the boundary line, to a certain extent, it restricts the shape of the weeding area, and realizes the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of the weeding robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of an obstacle recognition method provided by Embodiment 1 of the present invention;

[0021] Figure 2 It is a flowchart of an obstacle recognition method provided by Embodiment 2 of the present invention;

[0022] Figure 3 It is a schematic structural diagram of an obstacle recognition device provided by Embodiment 3 of the present invention;

[0023] Figure 4 It is a schematic structural diagram of an electronic device provided by Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only for explaining the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention rather than all the structures are shown in the accompanying drawings.

[0025] Embodiment 1

[0026] Figure 1 FIG. is a flowchart of an obstacle recognition method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where a weeding robot recognizes obstacles in a candidate weeding area. This method can be executed by the obstacle recognition device provided in the embodiments of the present invention, and this device can be implemented in a software and / or hardware manner. Refer to Figure 1 , the obstacle recognition method provided in this embodiment includes:

[0027] Step 110, obtain the chromaticity information and lightness information of the candidate weeding area image.

[0028] Among them, the candidate weeding area is the possible working area of the weeding robot. It may be entirely weeds to be removed, that is, the weeding area; or it may be an obstacle area of wood chips or wood chip-like substances, especially coarse wood chips.

[0029] The candidate weeding area image can be captured by a camera installed on the weeding robot, and this embodiment does not limit this. The chromaticity information of the candidate weeding area image is the overall chromaticity feature of the candidate weeding area image. The chromaticity-related information in the image can be obtained by acquiring the chromaticity channel image of the candidate weeding area image, such as the chromaticity value of each pixel point in the image, etc., and this embodiment does not limit this.

[0030] The lightness information of the candidate weeding area image is the overall lightness information of the candidate weeding area image. The lightness-related information in the image can be obtained by acquiring the lightness channel image of the candidate weeding area image, and the relationship between the candidate weeding area and the light can be judged through the lightness information.

[0031] Step 120, generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain the peak information of the target chromaticity histogram.

[0032] Generate a target chromaticity histogram of the candidate weeding area image based on the chromaticity information. Here, the target chromaticity histogram is a histogram used to intuitively reflect the chromaticity distribution information in the candidate weeding area image generated based on the chromaticity information. The abscissa of the histogram can be the chromaticity value, and the ordinate can be the frequency, which is used to reflect the chromaticity distribution of the pixel points in the candidate weeding area image. The peak information of the target chromaticity histogram is the information related to the peak in the target chromaticity histogram, which can be the specific value of the peak in the target chromaticity histogram, and this embodiment does not limit this.

[0033] In this embodiment, optionally, generating the target chromaticity histogram of the candidate weeding area image according to the chromaticity information and obtaining the peak information of the target chromaticity histogram includes:

[0034] Perform histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image;

[0035] Perform smoothing processing on the to-be-processed chromaticity histogram to obtain a smoothed chromaticity histogram;

[0036] Generate the target chromaticity histogram according to the smoothed chromaticity histogram and obtain the peak information of the target chromaticity histogram.

[0037] By performing histogram statistics on the chromaticity information, directly generate a to-be-processed chromaticity histogram of the candidate weeding area image. Here, the to-be-processed chromaticity histogram can reflect the specified chromaticity information of the pixel points in the candidate weeding area image. Exemplarily, it is the statistical distribution of the chromaticity values of all pixel points in the candidate weeding area image, and this embodiment does not limit this.

[0038] Perform smoothing processing on the to-be-processed chromaticity histogram to remove the noise in the to-be-processed chromaticity histogram and improve the accuracy of obtaining the peak information in the target chromaticity histogram generated subsequently according to the smoothed chromaticity histogram. Among them, the smoothing processing can be filtering processing, and this embodiment does not limit this.

[0039] The target chromaticity histogram can be generated by screening the data in the smoothed chromaticity histogram and obtaining the peak information of the target chromaticity histogram according to the screened data, so as to improve the pertinence and accuracy of obtaining the peak information.

[0040] In this embodiment, optionally, generating the target chromaticity histogram according to the smoothed chromaticity histogram and obtaining the peak information of the target chromaticity histogram includes:

[0041] Determine a set of target peak points and a set of target valley points in the preset chromaticity interval of the smoothed chromaticity histogram according to the preset screening rules;

[0042] Generate the target chromaticity histogram according to the set of target peak points and the set of target valley points, and obtain the peak information of the target chromaticity histogram.

[0043] Among them, the set of target peak points and the set of target valley points are obtained by screening all peak points and valley points within a preset chromaticity interval in the smoothed chromaticity histogram according to a preset screening rule, so as to generate the target chromaticity histogram. Optionally, the preset chromaticity interval is 15 - 95, and this embodiment does not limit this. Generating the target chromaticity histogram according to the set of target peak points and the set of target valley points improves the pertinence of the generated target chromaticity histogram for obstacle recognition, so as to improve the pertinence and accuracy of obtaining peak information.

[0044] In this embodiment, optionally, the preset screening rule includes:

[0045] Each peak in the set of target peak points is greater than a preset multiple of each valley in the set of target valley points;

[0046] The distance between the target peak points is greater than a preset distance threshold;

[0047] The peak of the target peak point is greater than a preset peak threshold.

[0048] Exemplarily, the peak of the target peak point is greater than K times the valley of the target valley point, where K is a preset multiple. The distance between the target peak points is greater than the preset distance threshold D, and the peak of the target peak point is greater than the preset peak threshold M. The peak points and valley points that may be closely related to obstacle recognition are screened out through the preset screening rule to generate the target chromaticity histogram.

[0049] Step 130: Determine whether there are obstacles in the candidate weeding area image according to the peak information and the brightness information.

[0050] Compare the brightness information of the candidate weeding area image and the peak information of the target chromaticity histogram with the preset information judgment condition. Among them, the preset information judgment condition is related to the brightness information and the peak information. If the preset information judgment condition is satisfied, it is determined that there is an obstacle area in the candidate weeding area, so that the weeding robot can perform subsequent obstacle processing.

[0051] Exemplarily, the identified obstacles are randomly stacked and overlapping wood chips or wood chip-like substances.

[0052] The technical solution provided in this embodiment is to obtain the chromaticity information and lightness information of the candidate weeding area image; generate the target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain the peak information of the target chromaticity histogram; determine whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information, which solves the problem that in the prior art, the boundary of the weeding area of the weeding robot is usually calibrated by burying a boundary line, which consumes a lot of manpower and material resources and increases the cost. And because there are limitations in burying the boundary line, to a certain extent, it restricts the shape of the weeding area, and realizes the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of the weeding robot.

[0053] Embodiment 2

[0054] Figure 2 The figure is a flowchart of an obstacle recognition method provided in Embodiment 2 of the present invention. This technical solution is a supplementary description of the process of determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information. Compared with the above solution, this solution is specifically optimized as follows: the peak information includes: the maximum peak; the lightness information includes: the number of white pixels in the non-exposed state;

[0055] Determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information includes:

[0056] If the maximum peak is greater than the preset maximum peak threshold, and the number of white pixels in the non-exposed state is greater than the preset non-exposed state white pixel threshold, it is determined that there is an obstacle in the candidate weeding area image. Specifically, the flowchart of the obstacle recognition method is as Figure 2 shown:

[0057] Step 210, obtain the chromaticity information and lightness information of the candidate weeding area image; the lightness information includes: the number of white pixels in the non-exposed state.

[0058] Among them, the white pixels in the non-exposed state are the number of pixels that are not in the exposed state but appear white in the candidate weeding area image, such as the pixels of an obstacle that is white itself in the candidate weeding area image. According to the lightness and chromaticity of the pixels in the candidate weeding area image, the number of white pixels in the non-exposed state is obtained. The obtaining method can be to obtain the lightness channel image and chromaticity channel image of the candidate weeding area image, and count the number of pixels whose lightness value is less than the preset threshold, such as 255, and the chromaticity value is less than or equal to the preset threshold, such as 0, in the chromaticity channel image as the number of white pixels in the non-exposed state.

[0059] Step 220: Generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information of the target chromaticity histogram; the peak information includes: the maximum peak.

[0060] Among them, the maximum peak is the maximum value among all peaks in the target chromaticity histogram. This value indicates the concentration degree of chromaticity values in the candidate weeding area image. The larger the value, the more the number of pixels with this chromaticity value, that is, the more concentrated the chromaticity distribution.

[0061] Step 230: If the maximum peak is greater than a preset maximum peak threshold, and the number of white pixels in the non-exposure state is greater than a preset non-exposure state white pixel threshold, it is determined that there is an obstacle in the candidate weeding area image.

[0062] Exemplarily, the maximum peak is denoted as maxH, and the number of white pixels in the non-exposure state is denoted as zeroPix. If the preset maximum peak threshold is 350 and the preset non-exposure state white pixel threshold is 550, the preset information judgment condition is maxH > 350 and zeroPix > 550. When this condition is met, it is determined that there is an obstacle in the candidate weeding area image. The obstacle area may be an area where wood chips or wood chip-like substances are randomly stacked and overlapped, resulting in an area with a brightness intensity of 0 in the candidate weeding area image. Optionally, the preset information judgment condition can also be maxH > 400 and zeroPix > 400, maxH > 500 and zeroPix > 300, maxH > 600 and zeroPix > 250, maxH > 700 and zeroPix > 210, zeroPix > 600. The preset information judgment condition can be adjusted according to the specific judgment scenario, and this embodiment does not limit this.

[0063] In the embodiment of the present invention, whether there is an obstacle in the candidate weeding area image is determined through the maximum peak and the number of white pixels in the non-exposure state, so as to solve the problem that when using wood chips or the like for paving in the boundary system of a weeding robot, it is difficult to identify obstacles simply through chromaticity information or texture information because the color and roughness of wood chips or wood chip-like substances are similar to those of the lawn, and improve the recognition efficiency and accuracy of obstacles such as wood chips or wood chip-like substances in the candidate weeding area of the weeding robot.

[0064] Embodiment III

[0065] Figure 3 It is a schematic structural diagram of an obstacle recognition device provided in Embodiment III of the present invention. This device can be implemented in a hardware and / or software manner, and can execute an obstacle recognition method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. As Figure 3 shown, this device includes:

[0066] An information acquisition module 310, configured to acquire the chromaticity information and lightness information of the candidate weeding area image;

[0067] A histogram generation module 320, configured to generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information of the target chromaticity histogram;

[0068] An obstacle determination module 330, configured to determine whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information.

[0069] In the technical solution provided by this embodiment, by acquiring the chromaticity information and lightness information of the candidate weeding area image; generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram; and determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information, it solves the problem that in the prior art, the boundary of the weeding area of the weeding robot is usually calibrated by burying a boundary line, which consumes a large amount of manpower and material resources and increases the cost. And because there are limitations in burying the boundary line, to a certain extent, it restricts the shape of the weeding area, and realizes the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of the weeding robot.

[0070] Based on the above technical solutions, optionally, the histogram generation module includes:

[0071] A first histogram generation unit, configured to perform histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image;

[0072] A histogram filtering unit, configured to perform smoothing processing on the to-be-processed chromaticity histogram to obtain a smoothed chromaticity histogram;

[0073] A second histogram generation unit, configured to generate the target chromaticity histogram according to the smoothed chromaticity histogram, and obtain peak information of the target chromaticity histogram.

[0074] Based on the above technical solutions, optionally, the second histogram generation unit includes:

[0075] A set determination subunit, configured to determine a target peak point set and a target valley point set in a preset chromaticity interval of the smoothed chromaticity histogram according to a preset screening rule;

[0076] A histogram generation subunit, configured to generate the target chromaticity histogram according to the target peak point set and the target valley point set, and obtain peak information of the target chromaticity histogram.

[0077] Based on the above technical solutions, optionally, the preset screening rules include:

[0078] Each peak in the set of target peak points is greater than a preset multiple of each valley value in the target valley points;

[0079] The distance between the target peak points is greater than a preset distance threshold;

[0080] The peak value of the target peak point is greater than a preset peak threshold.

[0081] Based on the above technical solutions, optionally, the peak information includes: the maximum peak; the brightness information includes: the number of white pixels in the non-exposed state;

[0082] The obstacle determination module includes:

[0083] An obstacle determination unit, configured to determine that there is an obstacle in the candidate weeding area image if the maximum peak is greater than a preset maximum peak threshold and the number of white pixels in the non-exposed state is greater than a preset non-exposed state white pixel threshold.

[0084] Embodiment 4

[0085] Figure 4 The following is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present invention. As Figure 4 shown, the electronic device includes a processor 40, a memory 41, an input device 42, and an output device 43; the number of processors 40 in the electronic device may be one or more. Figure 4 Here, one processor 40 is taken as an example; the processor 40, the memory 41, the input device 42, and the output device 43 in the electronic device may be connected through a bus or other means. Figure 4 Here, taking the connection through a bus as an example.

[0086] The memory 41, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the obstacle recognition method in the embodiments of the present invention. The processor 40 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 41, that is, implements the above obstacle recognition method.

[0087] The memory 41 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 41 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 41 may further include a memory remotely provided with respect to the processor 40, and these remote memories may be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] Embodiment 5

[0089] Embodiment 5 of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute an obstacle recognition method when executed by a computer processor. The method includes:

[0090] Obtain the chromaticity information and lightness information of the candidate weeding area image;

[0091] Generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain the peak information of the target chromaticity histogram;

[0092] Determine whether there are obstacles in the candidate weeding area image according to the peak information and the lightness information.

[0093] Of course, for a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions are not limited to the method operations as described above, and can also execute relevant operations in the obstacle recognition method provided by any embodiment of the present invention.

[0094] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk, or an optical disc of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0095] It should be noted that in the embodiments of the above obstacle recognition device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0096] Embodiment Six

[0097] Embodiment Six of the present invention provides a weeding robot, which includes a robot body and also includes the electronic device described in any embodiment of the present invention.

[0098] Specifically, the electronic device installed on the weeding robot can perform the related operations of a method for recognizing obstacles described in any embodiment of the present invention.

[0099] Among them, the robot body may include two active wheels on the left and right, which can be driven by motors respectively. The motors can be brushless motors with a speed reducer and a Hall sensor. The robot body realizes driving operations such as forward, backward, turning, and arc by controlling the speeds and directions of the two active wheels. The robot body also includes a caster wheel, a camera, and a rechargeable battery. Among them, the caster wheel plays a role in supporting and balancing. The camera is installed at a specified position of the robot and forms a preset angle with the horizontal direction to capture images of the candidate weeding area. The rechargeable battery is used to provide power for the robot to work.

[0100] Note that the above is only the preferred embodiment of the present invention and the applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An obstacle recognition method, characterized in that, including: obtaining the chromaticity information and lightness information of the candidate weeding area image; generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram; determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information; the peak information includes: the maximum peak; the lightness information includes: the number of white pixels in the non-exposed state; determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information includes: if the maximum peak is greater than a preset maximum peak threshold and the number of white pixels in the non-exposed state is greater than a preset non-exposed state white pixel threshold, it is determined that there is an obstacle in the candidate weeding area image.

2. The method according to claim 1, wherein generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram, includes: performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image; performing smoothing processing on the to-be-processed chromaticity histogram to obtain a smoothed chromaticity histogram; generating the target chromaticity histogram according to the smoothed chromaticity histogram, and obtaining peak information of the target chromaticity histogram.

3. The method according to claim 2, characterized in that generating the target chromaticity histogram according to the smoothed chromaticity histogram, and obtaining peak information of the target chromaticity histogram, includes: determining a set of target peak points and a set of target valley points in a preset chromaticity interval of the smoothed chromaticity histogram according to a preset screening rule; generating the target chromaticity histogram according to the set of target peak points and the set of target valley points, and obtaining peak information of the target chromaticity histogram.

4. The method according to claim 3, characterized in that, the preset screening rule includes: each peak in the set of target peak points is greater than a preset multiple of each valley in the set of target valley points; the distance between the target peak points is greater than a preset distance threshold; the peak of the target peak point is greater than a preset peak threshold.

5. An obstacle recognition device, characterized in that, including: an information acquisition module for obtaining the chromaticity information and lightness information of the candidate weeding area image; a histogram generation module for generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram; an obstacle determination module for determining whether there is an obstacle in the candidate weeding area image according to the peak information and the lightness information; the peak information includes: the maximum peak; the lightness information includes: the number of white pixels in the non-exposed state; the obstacle determination module includes: an obstacle determination unit for determining that there is an obstacle in the candidate weeding area image if the maximum peak is greater than a preset maximum peak threshold and the number of white pixels in the non-exposed state is greater than a preset non-exposed state white pixel threshold.

6. The device according to claim 5, characterized in that, the histogram generation module includes: a first histogram generation unit for performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image; a histogram filtering unit for performing smoothing processing on the to-be-processed chromaticity histogram to obtain a smoothed chromaticity histogram; A second histogram generation unit, configured to generate the target chromaticity histogram according to the smoothed chromaticity histogram, and obtain peak information of the target chromaticity histogram.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the obstacle recognition method according to any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, the obstacle recognition method according to any one of claims 1-4 is implemented.

9. A weeding robot, comprising a robot body, characterized in that, It further includes the electronic device according to claim 7.

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