An automated system and method for detecting defects in vehicle lights

Abstract

The present invention discloses an automated system and a detection method for detecting defects in vehicle lights, and belongs to the field of vehicle light detection technology. The system includes a PC computer, on which host computer software, PLC software and an offline detection module are installed; an online acquisition module, which is used to perform defect detection on vehicle lights; an interface module, which is used to provide a human-computer interaction interface for the PC computer; and a power supply, which is used to power the PC computer and the online acquisition module. The present invention proposes an automated system and a detection method for detecting defects in vehicle lights, which supports online and offline, bright field and dark field combined detection. Compared with previous optical detection products, it can synchronously obtain bright field and dark field images, and shoot samples through a camera, thereby greatly improving detection efficiency. It has the characteristics of low hardware cost, high system integration, strong reusability and convenient iteration.

Description

Technical Field

[0001] The invention relates to an automated system and a detection method for vehicle lamp defect detection, belonging to the technical field of vehicle lamp detection. Background Art

[0002] During the production process of car lights, the factory needs to detect defects in car light products. The appearance defects of low beam and adaptive headlight products include bubbles, black spots, spots, scratches and white fog, so the above defects need to be detected.

[0003] At present, there are two main methods for detecting defects in headlights: the first method uses a method with a low degree of intelligent industrialization, that is, inspectors manually observe and sort to detect defective products. The second method uses a method with a high degree of intelligent industrialization, that is, an industrial computer combined with a machine vision inspection device is used to inspect product quality. The inspection equipment uses the software on the industrial computer to implement algorithm detection, data analysis and image processing.

[0004] The first method is manual inspection by quality inspectors, who observe the headlights with their eyes to see if there are any defects such as bubbles, black spots, spots, scratches, and white mist. Manual inspection is easily affected by individual subjective factors, and different quality inspectors may have different criteria for judging product defects, resulting in inconsistent results. Long-term repetitive manual inspections can easily cause fatigue to quality inspectors and affect the accuracy of inspections, and may even lead to missed inspections or false inspections. Secondary damage may also be caused to the samples in the process of taking samples. Manual inspection requires a long time and manpower costs, is not suitable for large-scale production, cannot meet the needs of efficient production, and may lead to low production efficiency of the production line. In addition, manual inspection cannot monitor and record product defects in real time, makes it difficult to analyze and improve the production process, and cannot detect potential problems in a timely manner.

[0005] The second method is to use an industrial computer combined with a machine vision detection device. The current detection device has a good detection effect on flat or simple diffuse reflective workpieces. For mirrored or highly reflective sample surfaces, due to the complex structure or reflection of the material itself, efficient and accurate detection cannot be achieved, and the detection of headlight defects lacks accuracy and stability. In addition, the current detection systems on the market use the imaging characteristics of bright field and dark field to detect different product defects. Surface defects such as scratches and black spots on the object being tested can be detected by bright field lighting. Tiny particles and protrusions on the surface of the object being tested can be detected under dark field lighting. Therefore, existing methods usually require multiple different flashing light sources and detectors to obtain scanning detection images under bright field and dark field respectively. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art. The present invention proposes an automated system and detection method for vehicle lamp defect detection, which supports online and offline, bright field and dark field combined detection. Compared with previous optical detection products, it can simultaneously obtain bright field and dark field images, and shoot samples through a camera, thereby greatly improving the detection efficiency. It has the characteristics of low hardware cost, high system integration, strong reusability and convenient iteration. At present, it can detect defects in low beams and adaptive headlights, improve the detection speed and accuracy of production line products, facilitate the monitoring of the production process, and promote the development of intelligent manufacturing.

[0007] In order to solve the above technical problems, the technical solution of the present invention is:

[0008] In one aspect, the present invention provides an automated system for detecting defects in vehicle lamps, comprising:

[0009] A PC computer, on which host computer software, PLC software and an offline detection module are installed;

[0010] An online acquisition module, wherein the online acquisition module is used to perform defect detection on the vehicle lamp;

[0011] An interface module, the interface module is used to provide a human-computer interaction interface for a PC;

[0012] A power supply, which is used to supply power to the PC and the online acquisition module;

[0013] The host computer software is used to build a test environment, switch the bright and dark field detection algorithms to perform lamp defect detection, display the detection log in real time, and display and record the detection results;

[0014] The PLC software is used to control the online acquisition module to perform detection actions;

[0015] The offline detection module is used to provide the upper computer software with pictures in the sample library.

[0016] Furthermore, the online acquisition module includes an industrial computer, a camera, a height adjustment bracket, a robotic arm, a light source, a storage table, a tray and a material table;

[0017] The light source is arranged on the material table, and the storage table is located above the light source;

[0018] The height adjustment bracket is arranged on the material table, the camera is fixed on the height adjustment bracket, and the camera is located above the storage table;

[0019] The tray is arranged on the material table, and the tray is used to place the vehicle lamp to be tested;

[0020] The mechanical arm is arranged on the material table, and is used to carry the vehicle lamp to be tested to the storage table;

[0021] The camera is connected to an industrial computer, and the industrial computer is connected to a PC.

[0022] Furthermore, it also includes an external input device, which is connected to the interface module.

[0023] Furthermore, the peripheral input device includes an external keyboard and a mouse.

[0024] Another aspect of the present invention provides a detection method of an automated system for detecting defects in vehicle lamps, comprising the following steps:

[0025] Step S1, executing the host computer software and PLC software on the PC;

[0026] Step S2, host computer software user login;

[0027] Step S3: switch the bright field and dark field detection algorithms as needed. If online detection is required, enter the online detection mode; if offline detection is required, enter the offline detection mode.

[0028] Furthermore, the online detection mode specifically includes the following steps:

[0029] Step S41, build the hardware environment after successful login;

[0030] Step S42, selecting an administrator or operator to log in. When the user is an administrator, the bright and dark field detection algorithm type is selected and the algorithm parameters are set; when the user is an operator, the upper computer software directly detects the status of the camera and the robotic arm;

[0031] Step S43: according to the signal sent by the PLC software, the online acquisition module is started to perform light and dark field detection on the headlights, and then the pictures collected by the camera are sent to the computer PC, the upper computer software pre-processes the pictures, and then the light and dark field detection algorithm is switched to obtain the detection results;

[0032] Step S44: According to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection.

[0033] Further, in step S42, after successful login, the hardware environment is set up, which specifically includes the following steps:

[0034] Building the hardware environment includes installing and adjusting the industrial camera, calibrating the robot arm, and adjusting the light source. The host computer software detects the status of the camera and light source.

[0035] Furthermore, the offline detection mode specifically includes the following steps:

[0036] Step S51, after successful login, select administrator or operator to log in. When the user is an administrator, the administrator has the authority to select the algorithm type and the address for reading offline files; when the user is an operator, the operator does not have the authority to select the algorithm type and the address for reading offline files;

[0037] Step S52: After clicking the detection button, the host computer software calls the pre-stored pictures in the sample library of the offline detection module and sends them to the computer PC. The host computer software pre-processes the pictures and then switches the bright and dark field detection algorithms to obtain the detection results.

[0038] Step S53: According to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection.

[0039] Furthermore, the switching of the bright and dark field detection algorithm to obtain the detection result specifically includes the following steps:

[0040] First, the bright field test is carried out. If the bright field test result is qualified, the dark field test is carried out. If the dark field test is qualified, the headlight is judged to be a qualified part;

[0041] If the bright field test is qualified but the dark field test is unqualified, the headlight is judged as a defective part;

[0042] If the bright field test fails, the headlight will be directly judged as a failed part and no dark field test will be performed.

[0043] By adopting the above technical solution, the present invention has the following beneficial effects:

[0044] (1) The present invention includes multiple functions: PLC software sends signals, online acquisition of light and dark field images, data processing, detection and output of results. It has the characteristics of fast response and real-time processing, small size and light weight, which reduces costs while improving efficiency.

[0045] (2) The hardware device of the present invention is easy to build, and the camera height and light source angle can be changed according to different algorithm conditions or actual production line conditions.

[0046] (3) The online detection mode of the present invention simulates the process of the production line. The stand is simple and easy to build, and can send alarms or reminders to the operator in time to reduce the defective product generation rate.

[0047] (4) The online detection mode of the present invention takes pictures and detects both the bright field and the dark field of the same vehicle lamp sample during online detection. The sample is qualified only when both are qualified, thereby improving the accuracy of detection.

[0048] (5) The offline mode of the present invention can access a large number of sample libraries without the need to build an imaging environment. It has high flexibility and can be adjusted and optimized at any time according to needs.

[0049] (6) The present invention will output the data of the detected products, such as yield, algorithm time, test time, etc., and deal with problems in a timely manner, optimize the production process and procedures in a targeted manner, and improve product quality and yield.

[0050] (7) The present invention can output data such as algorithm time and test time. Developers can analyze and evaluate these data to quickly identify algorithm performance shortcomings and improvement directions, thereby improving algorithm performance and quality and ensuring the accuracy and stability of the detection system.

[0051] (8) The present invention can realize two kinds of algorithm detection, low beam and adaptive headlight. In the future, it will also support the integration of other detection algorithms and be able to adapt to a variety of different types of industrial cameras to meet production lines with different production needs. In addition, the system has strong portability and can achieve efficient and accurate detection.

[0052] (9) The present invention supports the detection log display function and saves the log locally, which can help track problems and faults on the production line and help quickly locate and solve abnormal conditions in production. In the later stage, it can also provide product quality monitoring function as important evidence of potential problems.

[0053] (10) The display result record column of the present invention displays the qualified and unqualified information of the current headlight sample after the test is completed, so that the operator can find problems in time and take relevant measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a principle block diagram of an automated system for detecting defects in vehicle lights according to the present invention;

[0055] Figure 2 It is a structural schematic diagram of the online acquisition module of the present invention;

[0056] Figure 3 The present invention is a flow chart of a detection method of an automated system for detecting vehicle lamp defects. DETAILED DESCRIPTION

[0057] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0058] Embodiment 1

[0059] like Figure 1 As shown, this embodiment provides an automated system for detecting defects in vehicle lamps, which includes:

[0060] PC computer, on which the host computer software, PLC software and offline detection module are installed;

[0061] Online acquisition module, which is used to detect defects on vehicle lights;

[0062] Interface module, the interface module is used to provide a human-computer interaction interface for the PC;

[0063] Power supply, which is used to power the PC and online acquisition module;

[0064] Peripheral input device, the peripheral input device is connected to the interface module, and the peripheral input device includes an external keyboard and a mouse.

[0065] Among them, the host computer software is used to build the test environment, switch the light and dark field detection algorithm to detect the defects of the headlights, display the detection log in real time, and display and record the detection results. The PLC software is used to control the online acquisition module to perform detection actions. The offline detection module is used to provide the host computer software with pictures in the sample library.

[0066] like Figure 2 As shown, the online acquisition module of this embodiment includes an industrial computer 1, a camera 2, a height adjustment bracket 3, a mechanical arm 4, a light source 5, a storage table 6, a tray 7 and a material table 8;

[0067] The light source 5 is arranged on the material table 8, and the storage table 6 is located above the light source 5;

[0068] The height adjustment bracket 3 is arranged on the material platform 8, and the camera 2 is fixed on the height adjustment bracket 3, and the camera 2 is located above the storage platform 6;

[0069] The tray 7 is arranged on the material table 8, and the tray 7 is used to place the vehicle lamp 9 to be tested;

[0070] The mechanical arm 4 is disposed on the material table 8, and is used to carry the vehicle lamp 9 to be tested to the storage table 6;

[0071] The camera 2 is connected to the industrial computer 1, and the industrial computer 1 is connected to the PC.

[0072] When collecting pictures online, the hardware installation must meet the following requirements:

[0073] 1. The light source 5 is parallel to the plane of the lamp to be tested 9, with a distance of 15 cm.

[0074] 2. The plane distance between the camera 2 lens and the lamp 9 to be tested is 20 cm.

[0075] 3. The size of surface light source 5 is ≥15cm*15cm.

[0076] 4. The lens of camera 2 is perpendicular to the shooting area.

[0077] After the camera 2 collects the image data, it is transmitted to the industrial computer 1 through the camera connection line. The height adjustment bracket 3 can adjust the height of the camera 2. The robotic arm 4 takes the headlight 9 to be tested and flips the angle. The light source 5 can provide both bright field and dark field environments. The material table 8 is used to fix the position of the light source 5.

[0078] Embodiment 2

[0079] like Figure 3 As shown, this embodiment is a detection method of an automated system for detecting vehicle lamp defects as in the first embodiment, which comprises the following steps:

[0080] Step S1, executing the host computer software and PLC software on the PC;

[0081] Step S2, host computer software user login;

[0082] Step S3: switch the bright field and dark field detection algorithms as needed. If the detection is performed on the production line, select online detection and enter the online detection mode; if the detection is performed for debugging, select offline detection and enter the offline detection mode.

[0083] The online detection mode of this embodiment specifically includes the following steps:

[0084] Step S41, after successful login, build the hardware environment. After the camera 2 and the light source 5 are connected, if the page of the host computer software displays a green light, it means that the connection is successful. If the page of the host computer software displays a red light, it means that the connection fails and needs to be readjusted and installed. Among them, building the hardware environment specifically includes the following steps: building the hardware environment includes installing and adjusting the industrial camera 2, calibrating the robot arm 4 and adjusting the light source 5, and the host computer software detects the status of the camera 2 and the light source 5;

[0085] Step S42, select an administrator or operator to log in. When the user is an administrator, the type of light and dark field detection algorithm is selected and the algorithm parameters are set. The algorithm parameters include initial brightness, width, height, light and dark field difference, and light and dark field detection range. Then, the host computer software detects the status of the camera 2 and the robotic arm 4. When the user is an operator, the host computer software directly detects the status of the camera 2 and the robotic arm 4. If the camera 2 and the robotic arm 4 cannot be detected, an error prompt box will pop up on the page of the host computer software. Only when the detection is successful can the next process be entered.

[0086] Step S43, according to the signal sent by the PLC software, the online acquisition module is started, the robot arm 4 moves the lamp 9 to be tested on the tray 7 to the storage table 6, the light source 5 is turned on during bright field detection, and the light source 5 is turned off during dark field detection, the lamp is tested in bright and dark fields, and then the pictures collected by the camera 2 are sent to the computer PC, the upper computer software pre-processes the pictures, and then switches the bright and dark field detection algorithms to obtain the test results;

[0087] Step S44, according to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection. In addition to displaying the judgment results and logs, the host computer software will also display the total number, yield, test time, and algorithm time. These data can provide support for subsequent algorithm performance evaluation.

[0088] The offline detection mode of this embodiment specifically includes the following steps:

[0089] Step S51, after successful login, select administrator or operator to log in. When the user is an administrator, the administrator has the authority to select the algorithm type and the address for reading offline files; when the user is an operator, the operator does not have the authority to select the algorithm type and the address for reading offline files; both administrators and operators can perform detection operations;

[0090] Step S52: After clicking the detection button, the host computer software calls the pre-stored pictures in the sample library of the offline detection module and sends them to the computer PC. The host computer software pre-processes the pictures and then switches the bright and dark field detection algorithms to obtain the detection results.

[0091] Step S53, according to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection. In addition to displaying the judgment results and logs, the host computer software will also display the total number, yield, test time, and algorithm time. These data can provide support for subsequent algorithm performance evaluation.

[0092] The switching bright and dark field detection algorithm in the online detection mode and the offline detection mode obtains the detection result, which specifically includes the following steps:

[0093] First, the bright field test is carried out. If the bright field test result is qualified, the dark field test is carried out. If the dark field test is qualified, the headlight is judged to be a qualified part;

[0094] If the bright field test is qualified but the dark field test is unqualified, the headlight is judged as a defective part;

[0095] If the bright field test fails, the headlight is directly judged as a failed part and no dark field test is performed;

[0096] The bright field test results, dark field test results, and test results are all displayed in the log column and result record column of the host computer software.

[0097] The specific embodiments described above further illustrate the technical problems, technical solutions and beneficial effects solved by the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An automated system for detecting defects in vehicle lights, characterized in that: It includes: A PC computer, on which host computer software, PLC software and an offline detection module are installed; An online acquisition module, the online acquisition module is used to perform defect detection on the vehicle lamp; An interface module, the interface module is used to provide a human-computer interaction interface for a PC; A power supply, which is used to supply power to the PC and the online acquisition module; The host computer software is used to build a test environment, switch the bright and dark field detection algorithms to perform lamp defect detection, display the detection log in real time, and display and record the detection results; The PLC software is used to control the online acquisition module to perform detection actions; The offline detection module is used to provide the upper computer software with pictures in the sample library; The online acquisition module comprises an industrial computer (1), a camera (2), a height adjustment bracket (3), a mechanical arm (4), a light source (5), a storage table (6), a tray (7) and a material table (8); The light source (5) is arranged on the material table (8), and the storage table (6) is located above the light source (5); The height adjustment bracket (3) is arranged on the material table (8), the camera (2) is fixed on the height adjustment bracket (3), and the camera (2) is located above the storage table (6); The tray (7) is arranged on a material table (8), and the tray (7) is used to place the vehicle lamp (9) to be tested; The mechanical arm (4) is arranged on the material table (8), and the mechanical arm (4) is used to move the vehicle lamp (9) to be tested to the storage table (6); The camera (2) is connected to an industrial computer (1), and the industrial computer (1) is connected to a PC; The detection method of the automated system for detecting defects in vehicle lamps comprises the following steps: Step S1, executing the host computer software and PLC software on the PC; Step S2, host computer software user login; Step S3, switching the bright and dark field detection algorithms as needed, if online detection is required, entering the online detection mode; if offline detection is required, entering the offline detection mode; The online detection mode specifically includes the following steps: Step S41, build the hardware environment after successful login; Step S42, selecting an administrator or an operator to log in. When the user is an administrator, the type of bright and dark field detection algorithm is selected and the algorithm parameters are set; when the user is an operator, the upper computer software directly detects the status of the camera (2) and the robotic arm (4); Step S43: according to the signal sent by the PLC software, start the online acquisition module to perform light and dark field detection on the vehicle lights, and then send the pictures collected by the camera (2) to the computer PC, where the upper computer software pre-processes the pictures and then switches the light and dark field detection algorithm to obtain the detection results; Step S44, according to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection; The offline detection mode specifically includes the following steps: Step S51, after successful login, select administrator or operator to log in. When the user is an administrator, the administrator has the authority to select the algorithm type and the address for reading offline files; when the user is an operator, the operator does not have the authority to select the algorithm type and the address for reading offline files; Step S52: After clicking the detection button, the host computer software calls the pre-stored pictures in the sample library of the offline detection module and sends them to the computer PC. The host computer software pre-processes the pictures and then switches the bright and dark field detection algorithms to obtain the detection results. Step S53: According to the next step instruction obtained by the PLC software, choose to start the next round of detection or end the detection; The switching of the bright and dark field detection algorithm to obtain the detection result specifically includes the following steps: First, the bright field test is carried out. If the bright field test result is qualified, the dark field test is carried out. If the dark field test is qualified, the headlight is judged to be a qualified part; If the bright field test is qualified but the dark field test is unqualified, the headlight is judged as a defective part; If the bright field test fails, the headlight will be directly judged as a failed part and no dark field test will be performed.

2. The automated system for detecting defects in vehicle lamps according to claim 1, characterized in that: It also includes an external input device, which is connected to the interface module.

3. The automated system for detecting defects in vehicle lamps according to claim 2, characterized in that: The peripheral input device includes an external keyboard and a mouse.

4. The automated system for detecting defects in vehicle lamps according to claim 1, characterized in that: In step S42, after successful login, the hardware environment is set up, which specifically includes the following steps: Building the hardware environment includes installing and adjusting the industrial camera (2), calibrating the robotic arm (4) and adjusting the light source (5), and the upper computer software detects the status of the camera (2) and the light source (5).

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