A detection method, device and storage medium for the state of a robot arm in an assembly line operation

Abstract

The present application relates to a detection method for the state of a robotic arm in an assembly line operation. The method first obtains an original image of the operating robotic arm, then pre-processes the original image of the operating robotic arm to obtain a pre-processed image, then converts the pre-processed image from RGB space to HSV space to obtain an HSV space image, then performs binary color gamut segmentation on the HSV space image to obtain a MASK binary image, then obtains the minimum bounding box of the MASK binary image, and calculates the side information of the minimum bounding box, and finally calculates the jitter range of the current side information and the side information at the previous moment, and uses the jitter range to judge the state of the robotic arm and issue a corresponding alarm. Thus, the recognition of the state of the robotic arm can be stably guaranteed, thereby reducing the workload and cost consumption.

Description

Technical Field

[0001] The present application relates to the technical field of state detection of a robot arm for assembly line operations, and in particular to a method, device and storage medium for detecting the state of a robot arm for assembly line operations. Background Art

[0002] In recent years, with the development and progress of science and technology, automation and information technology are the most talked about in intelligent manufacturing. Many local governments and enterprises vividly call it replacing people with machines. However, the use of robotic arms is accelerating this process, and judging the operating status of robotic arms is the top priority of this process.

[0003] In assembly line operations, the robot arm performs specific actions in operation. In order to confirm the operation status of the robot arm, the staff connects the controller through an oscilloscope to obtain the posture information in the controller, thereby judging the operation status of the robot arm. However, the oscilloscope is relatively expensive, and the application of machine vision technology provides an important guarantee for the normal operation of the robot arm. In actual operation scenarios, assembly line robot arms often work 24 hours a day. Manual inspection alone is affected by subjective factors and consumes a lot of manpower and material resources. Summary of the invention

[0004] In view of the technical problem that in the actual operation scenario, the assembly line robot arm often works non-stop for 24 hours, and manual inspection alone is affected by subjective factors and consumes a lot of manpower and material resources, this application proposes a detection method, device and storage medium for the status of the assembly line operation robot arm.

[0005] In a first aspect, the present application proposes a method for detecting the state of a robot arm for assembly line operation, comprising the following steps:

[0006] S1: Get the original image of the operating robot arm;

[0007] S2: preprocessing the original image of the operating robot arm to obtain a preprocessed image;

[0008] S3: Convert the preprocessed image from the RGB space to the HSV space to obtain an HSV space image;

[0009] S4: performing binary color gamut segmentation on the HSV space image to obtain a MASK binary image;

[0010] S5: Obtain the minimum bounding box of the MASK binary image, and calculate the edge information of the minimum bounding box;

[0011] S6: Calculate the jitter range of the current side information and the side information at the previous moment, use the jitter range to judge the state of the robot arm and issue a corresponding alarm.

[0012] By adopting the above technical solution, the state of the robot arm is judged using the jitter range, and each frame of the image is identified and judged by applying machine vision, so that the robot arm state recognition can be stably guaranteed, thereby reducing the workload and cost consumption.

[0013] Preferably, S1 specifically includes: using OpenCV to obtain the original image of the operating robot arm taken by the Hikvision network surveillance camera through the IP address.

[0014] Preferably, S2 specifically includes: performing Gaussian filtering on the acquired image to remove random noise of the image to obtain a preprocessed image.

[0015] Preferably, the S5 specifically includes:

[0016] S51: using an open operation to remove interference noise from the MASK binary image to obtain a new Mask image;

[0017] S52: Obtain the minimum bounding box of the new Mask image, and calculate the edge information of the minimum bounding box.

[0018] Preferably, the S6 specifically includes:

[0019] S61: Calculate the jitter ranges of the current side information and the side information at the previous moment, and calculate the number N of the side information whose jitter ranges exceed a preset first threshold;

[0020] S62: Compare N with a preset second threshold value. If N is greater than the second threshold value, it is determined that the robot arm is in a working state, and S63 is executed; if N is less than the second threshold value, it is determined that the robot arm is in a stopped state, and S64 is executed;

[0021] S63: Set the detection frame counter value = 0, and execute S65;

[0022] S64: Set the detection frame counter value +1, and execute S65;

[0023] S65: Determine whether the value of the detection frame counter is greater than a preset threshold stagnation frame number. If the detection frame counter is greater than the threshold stagnation frame number, it is determined that the robot arm is in a stagnant state, and an alarm message is sent to the background, and S66 is executed; if the detection frame counter is less than the threshold stagnation frame number, it is determined that the robot arm is in a working state, and no alarm message is sent to the background, and S66 is executed;

[0024] S66: Adjust the detection frequency of the detection method of the assembly line operation robot arm state;

[0025] S67: End the detection task or return to step S1.

[0026] Preferably, in S6, the algorithm for calculating the jitter range of the current side information and the side information at the previous moment is specifically:

[0027]

[0028] box=[top,left,bottom,right];

[0029] Among them, top represents the x-coordinate of the upper left corner, left represents the y-coordinate of the upper left corner, bottom represents the x-coordinate of the lower right corner, and right represents the y-coordinate of the lower right corner.

[0030] Preferably, the S65 specifically includes: judging whether the value of the detection frame counter is greater than a preset threshold number of stagnation frames; if the detection frame counter is greater than the threshold number of stagnation frames, judging that the robotic arm is in a stagnant state, and sending an alarm message to the background; then setting the value of the detection frame counter equal to the current value of the detection frame counter minus the preset alarm interval frame number, and executing S66; if the detection frame counter is less than the threshold number of stagnation frames, judging that the robotic arm is in a working state, and not sending an alarm message to the background, and executing S66.

[0031] Preferably, the S66 specifically includes:

[0032] S661: Set the detection frequency counter value = 0, and execute S662;

[0033] S662: Determine whether the value of the detection frame counter is greater than the detection frequency accumulation threshold. If the value of the detection frame counter is greater than or equal to the detection frequency accumulation threshold, execute S67; otherwise, execute S663.

[0034] S663: Set the detection frequency counter value to +1 and return to step S662.

[0035] In a second aspect, the present application further proposes a device for detecting the state of a robot arm for assembly line operation, the device comprising:

[0036] Image acquisition module: configured to obtain the original image of the operating robot arm;

[0037] Image processing module: configured to pre-process the original image of the working robot arm, convert the pre-processed image from RGB space to HSV space to obtain HSV space image, and perform binary color gamut segmentation on the HSV space image to obtain a MASK binary image;

[0038] Robotic arm state detection module: It is configured to obtain the minimum bounding box of the MASK binary image, calculate the edge information of the minimum bounding box, calculate the jitter range of the current edge information and the edge information at the previous moment, and use the jitter range to judge the state of the robotic arm and issue a corresponding alarm.

[0039] In a third aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the detection method as described in the first aspect.

[0040] The present application proposes a method for detecting the state of a robotic arm in an assembly line operation. The method first obtains an original image of the operating robotic arm, then preprocesses the original image of the operating robotic arm to obtain a preprocessed image, then converts the preprocessed image from RGB space to HSV space to obtain an HSV space image, then performs binary color gamut segmentation on the HSV space image to obtain a MASK binary image, then obtains the minimum bounding box of the MASK binary image, and calculates the side information of the minimum bounding box, and finally calculates the jitter range of the current side information and the side information at the previous moment, and uses the jitter range to judge the state of the robotic arm and issue a corresponding alarm. This can stably ensure the recognition of the robotic arm state, thereby reducing the workload and cost consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present application. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0042] Figure 1 It is a flow chart of a method for detecting the state of a robot arm for assembly line operations in one embodiment of the present application.

[0043] Figure 2 It is a flowchart of a specific embodiment of a method for detecting the state of a robot arm for assembly line operations according to the present application.

[0044] Figure 3 It is a schematic diagram of the original image of the robotic arm in one embodiment of the present application.

[0045] Figure 4 It is a schematic diagram of a MASK binary image in one embodiment of the present application.

[0046] Figure 5 It is a schematic diagram of obtaining the minimum bounding box of the MASK binary image in one embodiment of the present application.

[0047] Figure 6 It is a structural schematic diagram of a device for detecting the state of a robot arm for assembly line operations in one embodiment of the present application.

[0048] Figure 7 It is a structural diagram of a computer system suitable for implementing an electronic device of an embodiment of the present application. DETAILED DESCRIPTION

[0049] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] Figure 1 A flow chart of a method for detecting the state of a robot arm for assembly line operation in the present application is shown. Figure 2 A schematic diagram showing a specific embodiment of a method for detecting the state of a robot arm for assembly line operation that can be applied to the present application is shown, with reference to Figure 1 and Figure 2 , the method specifically comprises the following steps:

[0052] S1: Get the original image of the operating robot arm;

[0053] In a specific embodiment, refer to Figure 3 , S1 specifically includes: using OpenCV to obtain the original image of the operating robot arm taken by the Hikvision network monitoring camera through the IP address.

[0054] In a specific embodiment, before obtaining the original image of the operating robot arm, the detection frame counter is initialized to zero, and the frequency of sampling frames is set. In this embodiment, the frequency of sampling frames is once per second.

[0055] S2: preprocessing the original image of the operating robot arm to obtain a preprocessed image;

[0056] In a specific embodiment, S2 specifically includes: performing Gaussian filtering on the acquired image to remove random noise of the image to obtain a preprocessed image.

[0057] S3: Convert the preprocessed image from the RGB space to the HSV space to obtain an HSV space image;

[0058] S4: performing binary color gamut segmentation on the HSV space image to obtain a MASK binary image;

[0059] In a specific embodiment, refer to Figure 4,According to the color, saturation and transparency of the robot arm, the HSV space is screened to obtain a MASK binary image.

[0060] S5: Obtain the minimum bounding box of the MASK binary image, and calculate the edge information of the minimum bounding box;

[0061] In a specific embodiment, the S5 specifically includes:

[0062] S51: Use an opening operation to remove interference noise from the MASK binary image to obtain a new Mask image.

[0063] S52: Obtain the minimum bounding box of the new Mask image, and calculate the edge information of the minimum bounding box. Figure 5 shown.

[0064] S6: Calculate the jitter range of the current side information and the side information at the previous moment, use the jitter range to judge the state of the robot arm and issue a corresponding alarm.

[0065] In a specific embodiment, the S6 specifically includes:

[0066] S61: Calculate the jitter ranges of the current side information and the side information at the previous moment, and calculate the number N of the side information whose jitter ranges exceed a preset first threshold;

[0067] In a specific embodiment, in S6, the algorithm for calculating the jitter range of the current side information and the side information at the previous moment is specifically:

[0068]

[0069] box=[top,left,bottom,right];

[0070] Among them, f is a judgment function that exceeds a threshold. In this embodiment, the first threshold is set to 5; top represents the x-coordinate of the upper left corner, left represents the y-coordinate of the upper left corner, bottom represents the x-coordinate of the lower right corner, and right represents the y-coordinate of the lower right corner.

[0071] S62: Compare N with a preset second threshold value. If N is greater than the second threshold value, it is determined that the robot arm is in a working state, and S63 is executed; if N is less than the second threshold value, it is determined that the robot arm is in a stopped state, and S64 is executed;

[0072] In a specific embodiment, the second threshold is set to 3. Since there are four coordinate points in box = [top, left, bottom, right], if three of them exceed the first threshold of 5, it can be said that the robotic arm is working.

[0073] S63: Set the detection frame counter value = 0, and execute S65;

[0074] S64: Set the detection frame counter value +1, and execute S65;

[0075] S65: Determine whether the value of the detection frame counter is greater than a preset threshold stagnation frame number. If the detection frame counter is greater than the threshold stagnation frame number, it is determined that the robot arm is in a stagnant state, and an alarm message is sent to the background, and S66 is executed; if the detection frame counter is less than the threshold stagnation frame number, it is determined that the robot arm is in a working state, and no alarm message is sent to the background, and S66 is executed;

[0076] In an optional embodiment, S65 specifically includes determining whether the value of the detection frame counter is greater than a preset threshold stagnation frame number. If the detection frame counter is greater than the threshold stagnation frame number, it is determined that the robot arm is in a stagnant state, and an alarm message is sent to the background, and then the detection frame counter value is set equal to the current detection frame counter value minus the preset alarm interval frame number, and S66 is executed; if the detection frame counter is less than the threshold stagnation frame number, it is determined that the robot arm is in a working state, no alarm message is sent to the background, and S66 is executed. By setting the detection frame counter value equal to the current detection frame counter value minus the preset alarm interval frame number, the system is prevented from being in an alarm state all the time.

[0077] S66: Adjust the detection frequency of the detection method of the assembly line operation robot arm state;

[0078] In a specific embodiment, the S66 specifically includes:

[0079] S661: Set the detection frequency counter value = 0, and execute S662;

[0080] S662: Determine whether the value of the detection frame counter is greater than the detection frequency accumulation threshold. If the value of the detection frame counter is greater than or equal to the detection frequency accumulation threshold, execute S67; otherwise, execute S663.

[0081] S663: Set the detection frequency counter value to +1 and return to step S662.

[0082] Step S66 is used to adjust the detection frequency to the detection frequency accumulation threshold. The detection frequency accumulation threshold is modified accordingly according to the actual situation. Different devices are set with different detection frequency accumulation thresholds, which can improve the sensitivity of detection.

[0083] S67: End the detection task or return to step S1.

[0084] Further references Figure 6As an implementation of the above method, the present application provides an embodiment of a detection device for the state of a robot arm in an assembly line operation. The system embodiment is Figure 1 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0085] refer to Figure 6 , a detection device for the state of a robot arm for assembly line operation, comprising:

[0086] Image acquisition module 101: configured to acquire original images of the operating robot arm;

[0087] Image processing module 102: configured to pre-process the original image of the working robot arm, convert the pre-processed image from the RGB space to the HSV space to obtain an HSV space image, and perform binary color gamut segmentation on the HSV space image to obtain a MASK binary image;

[0088] Robotic arm state detection module 103: configured to obtain the minimum bounding box of the MASK binary image, calculate the edge information of the minimum bounding box, calculate the jitter range of the current edge information and the edge information at the previous moment, and use the jitter range to judge the state of the robot arm and issue a corresponding alarm.

[0089] Reference below Figure 7 , which shows a schematic diagram of the structure of a computer system 100 suitable for implementing an electronic device of an embodiment of the present application. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] like Figure 7 As shown, the computer system 100 includes a central processing unit (CPU) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage part 108 into a random access memory (RAM) 103. In the RAM 103, various programs and data required for the operation of the system 100 are also stored. The CPU 101, the ROM 102, and the RAM 103 are connected to each other through a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0091] The following components are connected to the I / O interface 105: an input section 106 including a keyboard, a mouse, etc.; an output section 107 including a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 108 including a hard disk, etc.; and a communication section 109 including a network interface card such as a LAN card, a modem, etc. The communication section 109 performs communication processing via a network such as the Internet. A drive 110 is also connected to the I / O interface 105 as needed. A removable medium 111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 110 as needed, so that a computer program read therefrom is installed into the storage section 108 as needed.

[0092] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 109, and / or installed from a removable medium 111. When the computer program is executed by a central processing unit (CPU) 101, the above-mentioned functions defined in the method of the present application are executed.

[0093] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device implements the following when executing. Figure 1 The method shown in .

[0094] It should be noted that the computer-readable storage medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that 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 computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0095] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0096] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0097] The above describes the specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0098] In the description of the present application, it should be understood that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. The wording 'comprising' does not exclude the presence of elements or steps not listed in the claims. The wording 'one' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The simple fact that certain measures are recorded in mutually different dependent claims does not indicate that a combination of these measures cannot be used for improvement. Any reference symbols in the claims should not be interpreted as limiting the scope.

Claims

1. A method for detecting the state of a robot arm for assembly line operation, characterized in that: The method comprises the following steps: S1: Get the original image of the operating robot arm; S2: preprocessing the original image of the operating robot arm to obtain a preprocessed image; S3: Convert the preprocessed image from the RGB space to the HSV space to obtain an HSV space image; S4: performing binary color gamut segmentation on the HSV space image to obtain a MASK binary image; S5: Obtain the minimum bounding box of the MASK binary image, and calculate the edge information of the minimum bounding box; S6: Calculate the jitter range of the current side information and the side information at the previous moment, use the jitter range to judge the state of the robot arm and issue a corresponding alarm; The S6 specifically includes: S61: Calculate the jitter ranges of the current side information and the side information at the previous moment, and calculate the number N of the side information whose jitter ranges exceed a preset first threshold; S62: Compare N with a preset second threshold value. If N is greater than the second threshold value, it is determined that the robot arm is in a working state, and S63 is executed; if N is less than the second threshold value, it is determined that the robot arm is in a stopped state, and S64 is executed; S63: Set the detection frame counter value = 0, and execute S65; S64: Set the detection frame counter value +1, and execute S65; S65: Determine whether the value of the detection frame counter is greater than a preset threshold stagnation frame number. If the detection frame counter is greater than the threshold stagnation frame number, it is determined that the robot arm is in a stagnant state, and an alarm message is sent to the background, and S66 is executed; if the detection frame counter is less than the threshold stagnation frame number, it is determined that the robot arm is in a working state, and no alarm message is sent to the background, and S66 is executed; S66: Adjust the detection frequency of the detection method of the assembly line operation robot arm state; S67: End the detection task or return to step S1.

2. A method for detecting the state of a robot arm for assembly line operation according to claim 1, characterized in that: The S1 specifically includes: using OpenCV to obtain the original image of the operating robot arm taken by the Hikvision network monitoring camera through the IP address.

3. The method for detecting the state of a robot arm for assembly line operation according to claim 1, characterized in that: The S2 specifically includes: performing Gaussian filtering on the acquired image to remove random noise of the image to obtain a preprocessed image.

4. The method for detecting the state of a robot arm for assembly line operation according to claim 1, characterized in that: The S5 specifically includes: S51: using an open operation to remove interference noise from the MASK binary image to obtain a new Mask image; S52: Obtain the minimum bounding box of the new Mask image, and calculate the edge information of the minimum bounding box.

5. The method for detecting the state of a robot arm for assembly line operation according to claim 1, characterized in that: In S6, the algorithm for calculating the jitter range of the current side information and the side information at the previous moment is specifically: box=[top,left,bottom,right]; Among them, top represents the x-coordinate of the upper left corner, left represents the y-coordinate of the upper left corner, bottom represents the x-coordinate of the lower right corner, and right represents the y-coordinate of the lower right corner.

6. A method for detecting the state of a robot arm for assembly line operation according to claim 1, characterized in that: The S65 specifically includes: judging whether the value of the detection frame counter is greater than the preset threshold stagnation frame number; if the detection frame counter is greater than the threshold stagnation frame number, judging that the robot arm is in a stagnant state, and sending an alarm message to the background; then setting the value of the detection frame counter equal to the current detection frame counter value minus the preset alarm interval frame number, and executing S66; if the detection frame counter is less than the threshold stagnation frame number, judging that the robot arm is in a working state, and not sending an alarm message to the background, and executing S66.

7. A method for detecting the state of a robot arm for assembly line operation according to claim 6, characterized in that: The S66 specifically includes: S661: Set the detection frequency counter value = 0, and execute S662; S662: Determine whether the value of the detection frame counter is greater than the detection frequency accumulation threshold. If the value of the detection frame counter is greater than or equal to the detection frequency accumulation threshold, execute S67; otherwise, execute S663. S663: Set the detection frequency counter value to +1 and return to step S662.

8. A detection device for the state of a robot arm in an assembly line operation, characterized in that: The device comprises: Image acquisition module: configured to obtain the original image of the operating robot arm; Image processing module: configured to pre-process the original image of the working robot arm, convert the pre-processed image from RGB space to HSV space to obtain HSV space image, and perform binary color gamut segmentation on the HSV space image to obtain a MASK binary image; Robotic arm state detection module: It is configured to obtain the minimum bounding box of the MASK binary image, calculate the edge information of the minimum bounding box, calculate the jitter range of the current edge information and the edge information at the previous moment, and use the jitter range to judge the state of the robotic arm and issue a corresponding alarm.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the detection method according to any one of claims 1 to 7 is implemented.

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