A method and device for detecting missing strokes in LED digital tubes

Abstract

A method and device for detecting missing strokes in LED digital tubes is disclosed. The method collects an image of the LED digital tube to be tested when it is lit and compares it with an image of a normal LED digital tube when it is lit. During the comparison, a horizontal projection histogram comparison method is used to subtract the horizontal projection histograms of the normal LED digital tube from the horizontal projection histograms of the LED digital tube to be tested, taking the absolute value. The deviation is then calculated point by point, and the average and variance are then taken for statistical analysis to obtain the brightness deviation. The correlation coefficient of the two horizontal projection histogram curves is then calculated to analyze the similarity of the two horizontal projection histogram curves, obtaining the similarity of the two images. Based on the similarity, it is determined whether the LED digital tube to be tested is missing strokes. The present invention can standardize the qualified detection of LED digital tubes, overcome the shortcomings of the existing manual detection technology, such as strong subjectivity and easy misjudgment, and reduce enterprise production costs.

Description

Technical Field

[0001] The invention belongs to the technical field of electronic components, relates to the detection of LED digital tubes, and is a method and device for detecting missing strokes of LED digital tubes. Background Art

[0002] LED digital tubes are a component widely used in instrument displays. They are generally composed of seven light-emitting diodes. By controlling different combinations of these seven light-emitting diodes, the display of the ten digits 0 to 9 is achieved. In digital tube products, the seven LEDs are required to have nearly uniform brightness for a clear display. Therefore, LED digital tube products generally need to test the brightness and uniformity of the seven LEDs. The current testing method is to light up the LED digital tube and rely on the human eye to judge whether the product's brightness meets the requirements. This method has obvious disadvantages. First, the labor intensity is high. The human eye is prone to visual fatigue when observing the LED digital tube for a long time with high concentration, which may lead to detection errors. Moreover, the judgment standard cannot be digitized and is subjective. As a result, some products tested still fail to meet the standards. Second, with the continuous rise in labor costs, employee wages account for an increasing proportion of product production costs, and the burden on enterprises is also increasing. There is an urgent need to reduce labor costs.

[0003] Recently, there have been efforts to use machine vision to inspect LED digital tubes. Patent Publication No. 104034516A uses an industrial camera to capture LED images and then compare them for quality control. However, this approach is susceptible to interference from ambient light, has poor adaptability to environmental conditions, and is complex and cumbersome. Patent Publication No. CN107976466A also uses machine vision to inspect the quality of LED products. This approach segments the image into different regions, applies a threshold to the pixel values ​​at the product information location, and compares the variance and mean of the module capturing the product information with the comparison module to determine product quality. The description of this approach reveals that selecting the segmentation threshold is difficult. The light intensity of each LED is not a stable quantity. This is due to the fact that LED materials themselves are unlikely to be completely uniform, and the voltage during testing cannot be completely stable. When an LED displays the digits 6 and 9, the displayed areas may simply be reversed, but their variance and mean values ​​can remain the same. This demonstrates a fundamental flaw in the implementation of this method.

[0004] Patent CN104819829B also uses machine vision to detect the quality of light-emitting LED tubes and uses industrial cameras to acquire images for processing. It detects information for brightness and chromaticity detection and classifies them into bins. However, directly using industrial cameras requires special calibration and has high requirements for the surrounding environment and the materials used in the darkroom. Generally, it is difficult for non-specialized industrial cameras and specialized software to directly measure brightness. This patent can also measure chromaticity. Generally, there are two main methods of chromaticity measurement. The first method is to use a photoelectric colorimeter to measure color. The principle of a photoelectric colorimeter is very similar to that of a densitometer, and its appearance, operation method, and even purchase price are quite similar. The second method is to use a spectrophotometer to measure color. Just as a three-filter photoelectric colorimeter can be considered a specialized reflectance measurement instrument, a spectrophotometer can also be viewed in this way. However, unlike a photoelectric colorimeter, a spectrophotometer measures an object's entire visible reflectance spectrum. Instead, it measures point by point across the visible spectrum, performing measurements at discrete points, typically every 10 or 20 nm, for a total of 16 to 31 points within the 400-700 nm range. While some spectrophotometers measure the spectrum continuously, a three-filter photoelectric colorimeter measures only three points. Therefore, a spectrophotometer can provide much more information, measuring at least 16 points. Generally, industrial cameras are used directly without a spectrometer, making parameter measurement and classification difficult. Summary of the Invention

[0005] The problem to be solved by the present invention is that the existing manual detection method for LED digital tubes is inefficient, low-precision and high-cost. A fast and effective objective quantitative detection method and device for LED digital tubes are provided, which can standardize the qualified detection of LED digital tubes and directly determine whether they are qualified. This overcomes the shortcomings of the existing technology of manual detection, such as strong subjectivity and easy misjudgment, and can also reduce the production costs of enterprises.

[0006] The technical solution of the present invention is: a method for detecting missing strokes of an LED digital tube, which collects an image of an LED digital tube to be tested in a light-on state and compares it with an image of a normal LED digital tube in a light-on state. During the comparison, a horizontal projection histogram comparison method is adopted, and the horizontal projection histograms of the normal LED digital tube and the LED digital tube to be tested are subtracted to obtain the absolute value, and the deviation is calculated point by point. Then, the average value and the variance are taken, and statistical analysis is performed to obtain the brightness deviation. According to the deviation, the LED digital tubes are divided into brightness defective products and brightness qualified products. For qualified products, the correlation coefficient of the two horizontal projection histogram curves is further calculated to analyze the similarity of the two horizontal projection histogram curves to obtain the similarity of the two images, and whether the LED digital tube to be tested is missing strokes is judged based on the similarity.

[0007] Furthermore, the image comparison is specifically as follows: assuming that the horizontal histogram function of the normal LED image is f(n), n=1…255, and f'(n) is the horizontal histogram of the LED image to be tested, then the error between the two is E(n)=|f(n)-f'(n)|, and the mean and variance of E(n) are calculated. Here R(n) is between -1 and 1, a perfect match is 1, and a complete mismatch is 0. The similarity of the target is judged based on R(n).

[0008] As a preferred method, after collecting the LED digital tube image, background preprocessing is first performed to remove the background, locate the numbers in the image, and segment the digital image. The segmented digital image is then compared with the image of the normal LED digital tube in the lit state.

[0009] The present invention also provides a device for detecting missing strokes in an LED digital tube, comprising an image processing device and a detection platform. The image processing device is configured with a software program, and the software program implements the above-mentioned detection method when executed.

[0010] As a preferred embodiment, the device of the present invention includes a control host, a conveying device, an image acquisition device, an image processing device and a product sorting device. The conveying device is used to convey the LED digital tube to be inspected to the front of the image acquisition device. A clamp is provided opposite the image acquisition device for clamping the LED digital tube to be inspected. The clamp is provided with a power interface for supplying power when clamping the LED digital tube to be inspected. The image acquisition device completes image acquisition of the LED digital tube to be inspected, and the image is input into the image processing device. The image processing device is configured with a software program. When the software program is executed, the detection method described in any one of claims 1 to 3 is implemented. The clamp releases the LED digital tube for completing image acquisition. The product sorting device sorts the LED digital tube that has completed detection according to the output of the image processing device. The control host sends a control signal to drive the action of the transmission device, the image acquisition device and the clamp.

[0011] As a preferred method, the image acquisition device includes an industrial camera, a filter and a darkroom. The filter is set in front of the lens of the industrial camera to filter out non-LED light. The industrial camera and the filter are set in the darkroom, and the LED digital tube to be inspected completes image acquisition in the darkroom.

[0012] The present invention proposes a method and device for detecting missing strokes in LED digital tubes, thereby realizing automated detection of LED digital tubes. The present invention utilizes the spatial arrangement characteristics of LED light-emitting tubes and the horizontal projection of bright spots to overcome the inability to make corresponding judgments about spatial positions when performing statistics, such as the defect in CN107976466A. The present invention utilizes horizontal projection statistical analysis and correlation analysis to overcome the missing strokes in the image and the misjudgment of the numbers 6 and 9. At the same time, LEDs have high luminous efficiency and emit a discontinuous spectrum. The present invention utilizes a filter to pass the spectrum emitted by the LED while not allowing the non-LED spectrum to pass. This can better reduce interference from ambient light, improve detection accuracy, and enhance the stability of the detection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of the process of the present invention.

[0014] Figure 2 Schematic diagram of a specific embodiment of the device of the present invention.

[0015] Figure 3 This is the horizontal projection histogram of the number 8 obtained by the method of the present invention.

[0016] Figure 4 This is the horizontal projection histogram of the number 6 obtained by the method of the present invention.

[0017] Figure 5 This is the horizontal projection histogram of the number 9 obtained by the method of the present invention. DETAILED DESCRIPTION

[0018] Missing strokes is a serious defect in current LED digital tubes. Missing strokes is defined as when all eight segments are fully illuminated, some or all of the displayed segments fail to illuminate red, preventing them from lighting up properly. The present invention aims to provide a fast and effective objective quantitative inspection system for LED digital tubes. This system can standardize the inspection of qualified LED digital tubes, directly determining whether they are qualified. This overcomes the shortcomings of existing manual inspection techniques, such as strong subjectivity and the tendency for misjudgment, and reduces production costs for enterprises.

[0019] The present invention collects an image of an LED digital tube to be tested in a light-on state, and compares it with an image of a normal LED digital tube in a light-on state. During the comparison, a method of comparing horizontal projection histograms is adopted, and the horizontal projection histograms of the normal LED digital tube and the LED digital tube to be tested are subtracted to obtain the absolute value, and the deviation is calculated point by point. Then, the average value and variance are taken, and statistical analysis is performed to obtain the brightness deviation. When the deviation is large, it will be considered a defective product, which affects the display effect of the digital tube. Such products should be directly eliminated. Only when the brightness display is within a certain range can it be considered that the brightness of the LED meets the requirements. The mean value and variance are used as the basis for the segmentation threshold. For LED digital tubes whose brightness meets the requirements, the correlation coefficient of the two horizontal projection histogram curves is further calculated to analyze the similarity of the two horizontal projection histogram curves, obtain the similarity of the two images, and judge whether the LED digital tube to be tested is missing strokes based on the similarity.

[0020] The image comparison is specifically as follows: let the horizontal histogram function of the normal LED image be f(n), n=1…255, f'(n) be the horizontal histogram of the LED image to be tested, then the error between the two is E(n)=|f(n)-f'(n)|, calculate the mean value and variance of E(n), Here R(n) is between -1 and 1, a perfect match is 1, and a complete mismatch is 0. The similarity of the target is judged based on R(n).

[0021] As a preferred method, Figure 1 As shown, after the LED digital tube image is collected, background preprocessing is first performed to remove the background, locate the numbers in the image, and segment the digital image. The segmented digital image is compared with the image of the normal LED digital tube in the lighting state.

[0022] like Figure 3-5 , which is the horizontal projection histogram of commonly used detection numbers of LED digital tubes obtained by the method of the present invention. It can be seen that the missing strokes of the image can be detected, and the numbers 6 and 9 can also be detected and judged normally.

[0023] The present invention also provides a device for detecting missing strokes in an LED digital tube, comprising an image processing device and a detection platform. The image processing device is configured with a software program, and the software program implements the above-mentioned detection method when executed.

[0024] As a preferred embodiment, the device of the present invention includes a control host, a conveying device, an image acquisition device, an image processing device, and a product sorting device. The conveying device is used to convey the LED digital tube to be inspected to the image acquisition device. A clamp is provided opposite the image acquisition device for clamping the LED digital tube to be inspected. The clamp is provided with a power interface for supplying power when clamping the LED digital tube to be inspected. The image acquisition device completes image acquisition of the LED digital tube to be inspected, and the image is input into the image processing device. The image processing device is configured with a software program that, when executed, implements the aforementioned inspection method. The clamp releases the LED digital tube whose image has been acquired. The product sorting device sorts the LED digital tubes that have completed inspection based on the output of the image processing device. The control host sends a control signal to drive the operation of the transmission device, the image acquisition device, and the clamp. The image acquisition device includes an industrial camera, a filter, and a darkroom. The filter is provided in front of the lens of the industrial camera to filter out non-LED light. The industrial camera and the filter are provided in the darkroom, and the LED digital tube to be inspected completes image acquisition in the darkroom.

[0025] like Figure 2 The figure shows a specific embodiment of the device of the present invention. The conveyor device includes a frame 3, a motor controller 9, a conveyor belt 11, and a motor 16. The LED digital tube to be tested is located in the middle of the conveyor belt. The motor controller 9 is connected to a computer 12, which controls the rotation of the motor 16. The computer 12 serves as the overall control system for the entire device, controlling the movement of the conveyor device, image acquisition device, fixture, and sorting device, and performing image processing. During operation, the motor 16 drives the conveyor belt 11 forward. When it enters the detection position, it is detected by the photoelectric sensor 7, the motor stops, and an industrial camera 5 takes a picture. The industrial camera 5 lens is equipped with a filter 6 that only transmits light from the LED to reduce interference from stray light other than the light from the LED to be tested. To reduce interference from ambient light, image acquisition is performed in a darkroom 4. After the LED to be tested is in place, the fixture 8 clamps the interface of the digital tube power signal. At this time, the LED display and image acquisition can be realized according to the computer's instructions. The fixture 8 is controlled by the computer to move up and down and clamp. After the inspection is complete, the computer sends a command to lift the fixture, release the LED digital tube, and then the motor starts running. After running to a certain position, if the inspected product has quality problems, it needs to be sorted out by a sorting device. The product sorting device is a robot 9 with a suction cup 10 at the front end. This can effectively utilize the flat surface of the LED digital tube and prevent damage to the LED digital tube caused by the use of a joint gripper. The base 13 of the robot can rotate 360 ​​degrees, the robot arm 12 can move up and down, and the robot arm 14 extending from the robot arm 12 can be extended and retracted back and forth in a straight line. In this way, the entire combination can complete the grasping and releasing within any range of the maximum extension amount.

[0026] The standard wavelength emitted by the light emitting diode is around 645nm. Using a 650nm filter (bandwidth 10nm) can significantly reduce the interference of stray light around 645nm.

Claims

1. A method for detecting missing strokes in an LED digital tube, characterized by: An image of the LED digital tube to be tested is collected when it is lit, and compared with an image of a normal LED digital tube when it is lit. The horizontal projection histogram comparison method is used during the comparison. The horizontal projection histograms of the normal LED digital tube and the LED digital tube to be tested are subtracted to obtain the absolute value, the deviation is calculated point by point, and then the average value and variance are calculated for statistical analysis to obtain the brightness deviation. Based on the deviation, the LED digital tubes are divided into brightness defective products and brightness qualified products. For qualified products, the correlation coefficient of the two horizontal projection histogram curves is further calculated to analyze the similarity of the two horizontal projection histogram curves to obtain the similarity of the two images. Based on the similarity, it is determined whether the LED digital tube to be tested is missing strokes. The image comparison is specifically as follows: assuming that the horizontal histogram function of a normal LED image is , n=1…255, is the horizontal histogram of the LED image to be tested, then the error between the two is , find The mean and variance of ,here Between -1 and 1, a perfect match is 1, a complete mismatch is 0, according to To determine the similarity of the target.

2. A method for detecting missing strokes in an LED digital tube according to claim 1, characterized in that After collecting the LED digital tube image, background preprocessing is performed first to remove the background, locate the numbers in the image, and segment the digital image. The segmented digital image is then compared with the image of the normal LED digital tube in the lit state.

3. A device for detecting missing strokes of an LED digital tube, characterized in that The invention comprises an image processing device and a detection platform, wherein the image processing device is provided with a software program, and when the software program is executed, the detection method according to claim 1 or 2 is implemented.

4. The device for detecting missing strokes of an LED digital tube according to claim 3, wherein It includes a control host, a conveying device, an image acquisition device, an image processing device and a product sorting device. The conveying device is used to convey the LED digital tube to be inspected to the front of the image acquisition device. A clamp is provided opposite the image acquisition device for clamping the LED digital tube to be inspected. The clamp is provided with a power interface for supplying power when clamping the LED digital tube to be inspected. The image acquisition device completes image acquisition of the LED digital tube to be inspected, and the image is input into the image processing device. The image processing device is configured with a software program. The clamp releases the LED digital tube for which image acquisition is completed. The product sorting device sorts the LED digital tubes that have completed inspection according to the output of the image processing device. The control host sends a control signal to drive the actions of the transmission device, the image acquisition device and the clamp.

5. The device for detecting missing strokes of an LED digital tube according to claim 4, characterized in that The image acquisition device includes an industrial camera, a filter and a darkroom. The filter is set in front of the lens of the industrial camera to filter out non-LED light. The industrial camera and the filter are set in the darkroom, and the LED digital tube to be inspected completes image acquisition in the darkroom.

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