Rectangular illumination method using LED arrays for machine vision

Zhang, Huailiang; Peng, Ling; Yu, Shengquan

doi:10.1364/ao.384833

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Research, programming, data analysis, and user interface design are part of job description. Information extraction, analysis, and comprehension from an image or a sequence of pictures are the primary goal of computer vision [7]. Video clips, multiple camera views, or data from a medical scanner are all examples of image data.…”

Section: Concepts On the Machine Vision System Design Based On Deep L...mentioning

confidence: 99%

Research on Machine Vision System Design Based on Deep Learning Neural Network

Tao

Ju-jiang

2022

Wireless Communications and Mobile Computing

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A machine vision system (MVS) is a technology that can analyze and recognize still or moving pictures using a computer. It is a branch of computer vision that looks like a security camera but can automatically capture, evaluate, and analyze images. The drawbacks are obvious. In the event of a computer vision system failure, firms must have a team of highly trained people with a thorough understanding of the distinctions. Artificial neural networks with numerous layers between the input and output layers are deep neural networks (DNN). Neurons, synapses, weights, biases, and functions are all part of any neural network, regardless of the kind. Many of the challenges in computer vision revolve around using convolutional neural networks (CNN) to categorize images into predefined categories. Convolutional and pooling layers were utilized to decrease the image’s size before feeding the reduced data to fully connected layers. According to the paper, the MVS-CNN algorithm can analyze a picture and determine the value of various characteristics and objects inside it. It is called convolution when combining two functions to create a third function. It is a fusion of two different datasets. A CNN performs convolution on the input data to build a feature map using a filter or kernel. Using a convolutional neural network, an inverted residual block is introduced as the basic block to balance identification accuracy and processing efficiency. The suggested method’s higher inspection performance is achieved with a huge dataset of photos of faulty and defect-free bottles. The result is obtained from the proposed method, the standard deviation ratio is 83.56%, absolute error ratio is 77.26%, trajectory length difference ratio is 82.35%, source pattern radiation amplitude ratio is 86.25%, classification of accuracy ratio is 83.25%, and finally, overall percentage performance ratio is 90.26%.

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Section: Concepts On the Machine Vision System Design Based On Deep L...mentioning

confidence: 99%

Research on Machine Vision System Design Based on Deep Learning Neural Network

Tao

Ju-jiang

2022

Wireless Communications and Mobile Computing

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“…The lighting system weakens the chaos and amplifies the image features and defects [34]. Diamond grit as a single crystal always results in special reflective phenomena due to its anisotropy, because of which it is often misjudged as a vacancy hole when an ordinary flat panel light source is used.…”

Section: Guaranteeing Filling Rate Of Diamond Grits In Segmentmentioning

confidence: 99%

Blade Segment with a 3D Lattice of Diamond Grits Fabricated via an Additive Manufacturing Process

Chen

Huang

et al. 2020

Chin. J. Mech. Eng.

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Diamond tools with orderly arrangements of diamond grits have drawn considerable attention in the machining field owing to their outstanding advantages of high sharpness and long service life. This diamond super tool, as well as the manufacturing equipment, has been unavailable to Chinese enterprises for a long time due to patents. In this paper, a diamond blade segment with a 3D lattice of diamond grits was additively manufactured using a new type of cold pressing equipment (AME100). The equipment, designed with a rotary working platform and 16 molding stations, can be used to additively manufacture segments with diamond grits arranged in an orderly fashion, layer by layer; under this additive manufacturing process, at least 216000 pcs of diamond green segments with five orderly arranged grit layers can be produced per month. The microstructure of the segment was observed via SEM and the diamond blade fabricated using these segments was compared to other commercial cutting tools. The experimental results showed that the 3D lattice of diamond grits was formed in the green segment. The filling rate of diamond grits in the lattice could be guaranteed to be above 95%; this is much higher than the 90% filling rate of the automatic array system (ARIX). When used to cut stone, the cutting amount of the blade with segments made by AME100 is two times that of ordinary tools, with the same diamond concentration. When used to dry cut reinforced concrete, its cutting speed is 10% faster than that of ARIX. Under wet cutting conditions, its service life is twice that of ARIX. By applying the machine vision online inspection system and a special needle jig with a negative pressure system, this study developed a piece of additive manufacturing equipment for efficiently fabricating blade segments with a 3D lattice of diamond grits.

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“…For the geometry measurement in machine vision, the illumination is critical to automized image optimization while the quality of the feature image and the efficiency of the processing algorithms can be improved with well-tuned illumination patterns and light parameters [9] . Generally, the off-the-shelf LED light sources are used directly for measurement in practice which results in variations of imaging quality between different objects [10].…”

Section: Introductionmentioning

confidence: 99%

Illumination system optimal design for geometry measurement of complex cutting tools in machine vision

Wang

Liu

Zhang

et al. 2022

Int J Adv Manuf Technol

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Precision geometry measurement of the complex cutting tools has a significant impact on the machinability of components in the industry. However, acquisition of high-quality images in machine vision is a challenging problem due to the large slope and complex geometry of the cutting tools. In view of the vital function of illumination, this paper proposed a method of optimal design for the LEDs array. First, the calculation model of irradiance distribution on the measurement plane is established and the properties of the reflected light from the surface of cutting tools is analysed based on the BRDF theory. Then, the optimal parameters are solved through the specific algorithm flow for the LED array design. Finally, a flexible LED light source is fabricated with the optimal parameters for different features of the cutting tools and used in the measurement. The measurement results show that the error of the optimized light source is less than 1%, and compared with the off-the-shelf light, the measurement accuracy is improved by 9.5% on average. Moreover, this method also presents the potential applied in other complex objects.

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Rectangular illumination method using LED arrays for machine vision

Cited by 5 publications

References 22 publications

Research on Machine Vision System Design Based on Deep Learning Neural Network

Research on Machine Vision System Design Based on Deep Learning Neural Network

Blade Segment with a 3D Lattice of Diamond Grits Fabricated via an Additive Manufacturing Process

Illumination system optimal design for geometry measurement of complex cutting tools in machine vision

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