A method and system for detecting the rope grooves of an elevator traction sheave based on machine vision

Abstract

The present invention discloses a method and system for detecting the rope grooves of an elevator traction sheave based on machine vision, including: an image acquisition module, an image processing module, a data acquisition module, an image judgment module, and a result calculation module. Among them, the image judgment module includes: a first image generation unit, a data judgment unit, and a second image generation unit. The present invention can realize non-contact measurement of the elevator traction sheave and can measure every part of the rope groove, ensuring the detection efficiency.

Description

Technical Field

[0001] The present invention relates to the field of elevator traction sheave groove detection, and particularly to a method and system for detecting elevator traction sheave grooves based on machine vision. Background Art

[0002] The traction sheave is the rope wheel on the traction machine, also known as the traction rope wheel or the drive rope wheel; it is a device for transmitting the traction power of the elevator, and uses the friction between the traction steel wire rope and the rope groove on the edge of the traction sheave to transmit power.

[0003] The existing methods for detecting traction sheave grooves are usually the vernier caliper measurement method: detecting by using a vernier caliper, with low detection efficiency and unable to detect every part of the traction sheave groove. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method and system for detecting elevator traction sheave grooves based on machine vision to solve the above problems.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for detecting elevator traction sheave grooves based on machine vision, comprising the following steps:

[0007] S1. Obtain a traction sheave image collected by a camera, wherein the traction sheave image includes n sequentially connected rope groove images;

[0008] S2. Process the obtained traction sheave image;

[0009] S3. Extract one rope groove image from the traction sheave image, extract the radius of the inscribed circle of the polygon in one rope groove image and the depth of the rope groove image, and traverse to extract the leftmost image point and the rightmost image point in the rope groove image;

[0010] S4. According to the radius of the inscribed circle and the depth of the rope groove image, move the leftmost image point to the left to obtain a first left moving point, move the rightmost image point to the right to obtain a first right moving point, and move the first left moving point and the first right moving point downward respectively to obtain a first lower left moving point and a first lower right moving point, and the first left moving point, the first right moving point, the first lower left moving point and the first lower right moving point form a first image;

[0011] S5. Extract the radius of the inscribed circle of the first image, and determine whether the radius of the inscribed circle of the first image is equal to the depth of the rope groove. When they are not equal, execute step S6; when they are equal, execute step S7;

[0012] S6. Subtract the depth of the rope groove from the radius of the inscribed circle of the first image, and use the result of the subtraction as the downward stepping value of the first lower left moving point and the first lower right moving point to obtain the second lower left moving point and the second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point, and the first lower right moving point form a second image. Replace the second image with the first image and repeat step S5;

[0013] S7. Traverse to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, calculate the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image to obtain distance information, and repeat step S3 for the next rope groove image.

[0014] Further, step S2 specifically includes the following sub-steps:

[0015] S201. Perform a filtering operation on n rope groove images to remove noise;

[0016] S202. Use a closing operation of first dilating and then eroding to separate the n rope groove images.

[0017] Further, the specific calculation of the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image is: Calculate the distance information through trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image.

[0018] Further, step S1 is specifically: Through camera calibration, collect n rope groove images inside the elevator traction sheave.

[0019] A machine vision-based elevator traction sheave rope groove detection system includes:

[0020] An image acquisition module for acquiring n rope groove images collected by a camera;

[0021] An image processing module for processing the obtained n rope groove images;

[0022] A data acquisition module for extracting the radius of the inscribed circle of the polygon inside a rope groove image and the depth of the rope groove image, and traversing to extract the leftmost image point and the rightmost image point in the rope groove image;

[0023] An image judgment module for judging whether the radius of the inscribed circle is equal to the depth of the rope groove according to the radius of the inscribed circle of the polygon inside a rope groove image, the depth of the rope groove image, the leftmost image point, and the rightmost image point;

[0024] The result calculation module traverses to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, calculates the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, and obtains distance information;

[0025] Among them, the image judgment module includes:

[0026] The first image generation unit moves the leftmost image point and the rightmost image point according to the radius of the inscribed circle and the depth of the rope groove image to form the first image;

[0027] The data judgment unit extracts the radius of the inscribed circle of the first image and judges whether the radius of the inscribed circle of the first image is equal to the depth of the rope groove;

[0028] The second image generation unit performs a secondary movement on the moved leftmost image point and the rightmost image according to the depth of the rope groove and the radius of the inscribed circle of the first image to form the second image, and replaces the second image with the first image.

[0029] Furthermore, the image processing module includes:

[0030] The filtering processing unit performs a filtering operation on n rope groove images to remove noise points;

[0031] The image separation unit separates n rope groove images using a closing operation of first dilating and then eroding.

[0032] Furthermore, the first image generation unit specifically moves the leftmost image point to the left according to the radius of the inscribed circle and the depth of the rope groove image to obtain the first left moving point, moves the rightmost image point to the right to obtain the first right moving point, and moves the first left moving point and the first right moving point downward respectively to obtain the first lower left moving point and the first lower right moving point. The first left moving point, the first right moving point, the first lower left moving point, and the first lower right moving point form the first image.

[0033] Furthermore, the second image generation unit specifically subtracts the depth of the rope groove from the radius of the inscribed circle of the first image, and uses the subtracted result as the downward step value of the first lower left moving point and the first lower right moving point to obtain the second lower left moving point and the second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point, and the first lower right moving point form the second image; replaces the second image with the first image.

[0034] Furthermore, the specific calculation of the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image is: calculating the distance information through trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image.

[0035] Advantages of the present invention:

[0036] The present invention enables the realization of non-contact measurement of the elevator traction sheave and can measure every part of the rope groove, ensuring the detection efficiency. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a method flow chart of a method for detecting the rope groove of an elevator traction sheave based on machine vision proposed in Embodiment 1 of the present invention;

[0039] Figure 2 It is a system structure diagram of a system for detecting the rope groove of an elevator traction sheave based on machine vision proposed in Embodiment 2 of the present invention;

[0040] Figure 3 It is an image of the rope groove collected by the camera proposed in Embodiment 2 of the present invention;

[0041] Figure 4 It is a schematic diagram of the image of the rope groove collected by the camera proposed in Embodiment 2 of the present invention;

[0042] Figure 5 It is a schematic diagram of the expanded image of the rope groove proposed in Embodiment 2 of the present invention;

[0043] Figure 6 It is a schematic diagram of the eroded image of the rope groove proposed in Embodiment 2 of the present invention;

[0044] Figure 7 It is a schematic diagram of determining the inscribed circle of a single rope groove proposed in Embodiment 2 of the present invention;

[0045] Figure 8 It is a schematic diagram of the leftmost image point and the rightmost image point proposed in Embodiment 2 of the present invention;

[0046] Figure 9 It is a schematic diagram of the first image proposed in Embodiment 2 of the present invention;

[0047] Figure 10 It is a schematic diagram of the inscribed circle of the first image proposed in Embodiment 2 of the present invention;

[0048] Figure 11 It is a schematic diagram of the inscribed circle of the second image proposed in Embodiment 2 of the present invention;

[0049] Figure 12 Schematic diagram of the inscribed circle when the radius of the inscribed circle of the first image proposed in Embodiment 2 of the present invention is equal to the depth of the rope groove

[0050] Figure 13 Schematic diagram of the structure of a terminal device for detecting the rope grooves of an elevator traction sheave based on machine vision proposed in Embodiment 3 of the present invention

[0051] Figure 14 Schematic diagram of the structure of a computer-readable storage medium for implementing a method for detecting the rope grooves of an elevator traction sheave based on machine vision proposed in Embodiment 4 of the present invention Detailed implementation manners

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the embodiments and the accompanying drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and are not intended to limit the present invention

[0053] Embodiment 1

[0054] As Figure 1 shown in, this embodiment proposes a method for detecting the rope grooves of an elevator traction sheave based on machine vision, including the following steps

[0055] S1. Obtain the traction sheave image collected by the camera, wherein the traction sheave image includes n sequentially connected rope groove images

[0056] S2. Process the obtained traction sheave image

[0057] S3. Extract a rope groove image from the traction sheave image, extract the radius of the inscribed circle of the polygon in a rope groove image and the depth of the rope groove image, and traverse and extract the leftmost image point and the rightmost image point in the rope groove image

[0058] S4. According to the radius of the inscribed circle and the depth of the rope groove image, move the leftmost image point to the left to obtain the first left moving point, move the rightmost image point to the right to obtain the first right moving point, and move the first left moving point and the first right moving point downward respectively to obtain the first lower left moving point and the first lower right moving point. The first left moving point, the first right moving point, the first lower left moving point and the first lower right moving point form the first image

[0059] S5. Extract the radius of the inscribed circle of the first image, and determine whether the radius of the inscribed circle of the first image is equal to the depth of the rope groove. If not, execute step S6; if equal, execute step S7

[0060] S6. Subtract the depth of the rope groove from the radius of the inscribed circle of the first image, and use the result of the subtraction as the downward stepping value for the first lower left moving point and the first lower right moving point to obtain the second lower left moving point and the second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point, and the first lower right moving point form a second image; replace the second image with the first image, and repeat step S5;

[0061] S7. Traverse to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, calculate the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image to obtain distance information, and repeat step S3 for the next rope groove image.

[0062] Further, the step S2 specifically includes the following sub-steps:

[0063] S201. Perform a filtering operation on the n rope groove images to remove noise;

[0064] S202. Use a closing operation of first dilating and then eroding to separate the n rope groove images.

[0065] Further, the calculation of the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image is specifically: calculate the distance information through trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image.

[0066] Further, the step S1 is specifically: through camera calibration, collect n rope groove images inside the elevator traction sheave.

[0067] Embodiment 2

[0068] On the basis of Embodiment 1, this embodiment further proposes a rope groove detection system for an elevator traction sheave based on machine vision, including:

[0069] An image acquisition module, configured to acquire n rope groove images collected by a camera;

[0070] An image processing module, configured to process the obtained n rope groove images;

[0071] A data acquisition module, extracting the radius of the inscribed circle of the polygon and the depth of the rope groove image within one rope groove image, and traversing to extract the leftmost image point and the rightmost image point in the rope groove image;

[0072] An image judgment module, judging whether the radius of the inscribed circle is equal to the depth of the rope groove according to the radius of the inscribed circle of the polygon, the depth of the rope groove image, the leftmost image point, and the rightmost image point within one rope groove image;

[0073] The result calculation module traverses to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, calculates the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, and obtains distance information;

[0074] Among them, the image judgment module includes:

[0075] The first image generation unit moves the leftmost image point and the rightmost image point according to the radius of the inscribed circle and the depth of the rope groove image to form the first image;

[0076] The data judgment unit extracts the radius of the inscribed circle of the first image and judges whether the radius of the inscribed circle of the first image is equal to the depth of the rope groove;

[0077] The second image generation unit performs a secondary movement on the moved leftmost image point and the rightmost image according to the depth of the rope groove and the radius of the inscribed circle of the first image to form the second image, and replaces the second image with the first image.

[0078] Furthermore, the image processing module includes:

[0079] The filtering processing unit performs a filtering operation on n rope groove images to remove noise points;

[0080] The image separation unit separates n rope groove images using a closing operation of first dilating and then eroding.

[0081] Furthermore, the first image generation unit specifically moves the leftmost image point to the left according to the radius of the inscribed circle and the depth of the rope groove image to obtain the first left moving point, moves the rightmost image point to the right to obtain the first right moving point, and moves the first left moving point and the first right moving point downward respectively to obtain the first lower left moving point and the first lower right moving point. The first left moving point, the first right moving point, the first lower left moving point, and the first lower right moving point form the first image.

[0082] Furthermore, the second image generation unit specifically subtracts the depth of the rope groove from the radius of the inscribed circle of the first image, and uses the subtracted result as the downward step value of the first lower left moving point and the first lower right moving point to obtain the second lower left moving point and the second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point, and the first lower right moving point form the second image; replaces the second image with the first image.

[0083] Furthermore, the specific calculation of the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image is: calculating the distance information through trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image.

[0084] Among them, the specific implementation principle process of this embodiment is as follows:

[0085] 1. Obtain the rope groove image collected by the camera as Figure 3 , and further, represent it in the form of a schematic diagram, such as Figure 4 (taking two rope grooves as an example).

[0086] 2. Perform a filtering operation on the image to remove noise;

[0087] 3. As can be seen from Figure 4 , the rope grooves in the obtained rope groove image are connected. Therefore, in order to obtain the connected domains of each deep groove respectively, a closing operation of first dilating and then eroding is used to separate each rope groove, which is convenient for separate calculation. Since it is necessary to ensure that the effective information of the image will not be lost in the result of the closing operation, the parameters of erosion and dilation cannot differ too much. After dilation, it is as shown in Figure 5 ; after erosion, it is as shown in Figure 6 . At this time, the connected domains of each rope groove are obtained.

[0088] 4. According to the concept of the inscribed circle: a polygon has at most one inscribed circle. That is to say, for a polygon, its inscribed circle, if it exists, is unique. Therefore, as shown in Figure 7 , sides ①, ②, and ③ can determine a unique inscribed circle (it can be seen from the finally obtained image Figure 12 that the inscribed circle made in the finally obtained image is determined by sides ①, ② and the sides obtained by translating points ④ and ⑤). When the radius of the inscribed circle is equal to the radius of the steel wire rope, in this case, the position of the inscribed circle is the combination situation of the real steel wire rope and the deep groove (one: the uniqueness of the inscribed circle; two: in the actual situation, the radius of the inscribed circle is determined by sides ① and ③). Therefore, in order to obtain an inscribed circle that conforms to the actual radius of the steel wire rope, in the image, keep the positions and lengths of sides ① and ③ unchanged, translate side ② downward, and continuously make an inscribed circle in the new graphic area, so as to obtain an inscribed circle with the same radius as the steel wire rope.

[0089] For example, as shown in Figure 8 , after extracting a deep groove, traverse to obtain the leftmost and rightmost points ④ and ⑤ in the image, and translate point ④ to the left, point ⑤ to the right, and then move them downward to obtain a new graphic, as shown in Figure 9 , and then make an inscribed circle to obtain Figure 10 ; at this time, an inscribed circle is obtained, and the radius of the inscribed circle can be obtained. After obtaining the radius, compare it with the radius of the steel wire rope. At this time, subtract the radius of the inscribed circle from the radius of the steel wire rope, and use the result as the downward step value of points ④ and ⑤ to obtain a new graphic, and then make an inscribed circle, and so on in a loop until the radius of the inscribed circle is equal to the radius of the steel wire rope. At this time, it is the combination situation of the actual steel wire rope and the rope groove. As shown in Figure 11 .

[0090] After cycling, the final obtained image is as Figure 12 .

[0091] After obtaining the image where the inscribed circle has the same radius as the wire rope, traverse to obtain the uppermost point ⑧ of the inscribed circle. At this time, with the uppermost point ⑥ of the deep groove image, perform trigonometric function operations on point ⑧ and point ⑥ to obtain the vertical distance between the two points, such as line segment ⑦, which is the distance from the wire rope to the bottom of the rope groove. After obtaining the detection result, perform the same operation on the second deep groove to obtain the detection result of the second rope groove (starting from step four).

[0092] Among them, the premise of this embodiment is that the obtained image should be able to correspond to the rope groove information in the actual situation. This problem is achieved by camera calibration technology (since camera calibration technology is an existing technology, it will not be described. The purpose of this technology is to enable the distances of each pixel point in the image of the detected object collected by the camera at a certain distance to correspond to the actual distances).

[0093] Embodiment 3

[0094] As Figure 13 , on the basis of Embodiment 1, this embodiment proposes a terminal device for detecting the rope grooves of an elevator traction sheave based on machine vision. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0095] The memory 210 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 211 and / or cache memory 212, and may further include read-only memory (ROM) 213.

[0096] Among them, the memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned methods for detecting the rope grooves of an elevator traction sheave based on machine vision in the embodiments of the present application. Its specific implementation manner is consistent with the implementation manner and the achieved technical effects described in the embodiments of the above method, and some contents will not be repeated. The memory 210 may further include a program / utilities 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0097] Correspondingly, the processor 220 can execute the above computer program and can also execute the program / utilities 214.

[0098] The bus 230 can represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus structures.

[0099] The terminal device 200 can also communicate with one or more external devices 240 such as a keyboard, a pointing device, a Bluetooth device, etc., and can also communicate with one or more devices capable of interacting with the terminal device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the terminal device 200 to communicate with one or more other computing devices. Such communication can be carried out through the input / output (I / O) interface 250. Moreover, the terminal device 200 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 260. The network adapter 260 can communicate with other modules of the terminal device 200 through the bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0100] Embodiment 4

[0101] Based on Embodiment 1, this embodiment proposes a computer-readable storage medium for elevator traction sheave groove detection based on machine vision. Instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, they implement any one of the above-mentioned methods for elevator traction sheave groove detection based on machine vision. Its specific implementation manner is consistent with the implementation manner and the achieved technical effects recorded in the embodiments of the above method, and some contents will not be elaborated again.

[0102] Figure 14Fig. 0 shows the program product 300 provided by this embodiment for implementing the above method. It may be a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. The program product 300 may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] A computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0104] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the rope grooves of an elevator traction sheave based on machine vision, characterized in that, It includes the following steps: S1. Obtain the image of the traction wheel collected by the camera, where the image of the traction wheel includes n successively connected sheave groove images; S2. Process the obtained image of the traction wheel; S3. Extract one sheave groove image from the image of the traction wheel, extract the inradius of the polygon in one sheave groove image and the depth of the sheave groove image, and traverse to extract the leftmost image point and the rightmost image point in the sheave groove image; S4. According to the inradius and the depth of the sheave groove image, move the leftmost image point to the left to obtain the first left moving point, move the rightmost image point to the right to obtain the first right moving point, and move the first left moving point and the first right moving point downward respectively to obtain the first lower left moving point and the first lower right moving point. The first left moving point, the first right moving point, the first lower left moving point, and the first lower right moving point form the first image; S5. Extract the inradius of the first image and determine whether the inradius of the first image is equal to the radius of the steel rope. When they are not equal, execute step S6; when they are equal, the position of the inscribed circle is the combination of the steel rope and the sheave groove in the actual situation, and execute step S7; S6. Subtract the depth of the sheave groove from the inradius of the first image, and use the subtracted result as the downward stepping value of the first lower left moving point and the first lower right moving point to obtain the second lower left moving point and the second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point, and the first lower right moving point form the second image; Replace the second image with the first image and repeat step S5; S7. Traverse to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the sheave groove image, calculate the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the sheave groove image to obtain distance information, and repeat steps S3 - S7 for the next sheave groove image.

2. The elevator traction sheave groove detection method based on machine vision according to claim 1, wherein Step S2 specifically includes the following sub - steps: S201. Perform a filtering operation on the n sheave groove images to remove noise; S202. Use a closing operation of first dilating and then eroding to separate the n sheave groove images.

3. A method for detecting the rope grooves of an elevator traction sheave based on machine vision according to claim 1, characterized in that, Calculating the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the sheave groove image specifically is: Calculate the distance information through trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the sheave groove image.

4. A method for detecting the rope grooves of an elevator traction sheave based on machine vision according to claim 1, characterized in that, Step S1 is specifically: Through camera calibration, collect n sheave groove images inside the elevator traction wheel.

5. A machine vision-based elevator traction sheave rope groove detection system, characterized in that It includes: An image acquisition module, used to obtain the image of the traction wheel collected by the camera, where the image of the traction wheel includes n successively connected sheave groove images; An image processing module, used to process the obtained image of the traction wheel; A data acquisition module, used to extract one sheave groove image from the image of the traction wheel, extract the inradius of the polygon in one sheave groove image and the depth of the sheave groove image, and traverse to extract the leftmost image point and the rightmost image point in the sheave groove image; An image judgment module, used to generate the first image, extract the inradius of the first image, and determine whether the inradius of the first image is equal to the radius of the steel rope; A result calculation module, when the radius of the inscribed circle of the first image is equal to the radius of the steel rope, is used to traverse to obtain the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image, and calculate the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image to obtain distance information; Among them, the image judgment module includes: A first image generation unit, according to the radius of the inscribed circle and the depth of the rope groove image, moves the leftmost image point to the left to obtain a first left moving point, moves the rightmost image point to the right to obtain a first right moving point, and moves the first left moving point and the first right moving point downward respectively to obtain a first lower left moving point and a first lower right moving point. The first left moving point, the first right moving point, the first lower left moving point and the first lower right moving point form the first image; A data judgment unit, extracts the radius of the inscribed circle of the first image, and judges whether the radius of the inscribed circle of the first image is equal to the radius of the steel rope; A second image generation unit, when the radius of the inscribed circle of the first image is not equal to the radius of the steel rope, subtracts the depth of the rope groove from the radius of the inscribed circle of the first image, and uses the subtracted result as the downward step value of the first lower left moving point and the first lower right moving point to obtain a second lower left moving point and a second lower right moving point. The first left moving point, the first right moving point, the second lower left moving point and the first lower right moving point form the second image, and replaces the second image with the first image, extracts the radius of the inscribed circle of the first image, and judges whether the radius of the inscribed circle of the first image is equal to the radius of the steel rope.

6. The elevator traction sheave groove detection system based on machine vision according to claim 5, characterized in that, The image processing module includes: A filtering processing unit, performs a filtering operation on n successively connected rope groove images to remove noise points; An image separation unit, uses a closing operation of first dilating and then eroding to separate n successively connected rope groove images.

7. A machine vision-based elevator traction sheave rope groove detection system according to claim 5, characterized in that Specifically, calculating the distance between the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image is: calculating the distance information by using trigonometric functions for the uppermost point of the inscribed circle of the first image and the uppermost point of the rope groove image.

Images