Machine vision system for quality control of molded plastic packaging

Lenty, Bartosz

doi:10.1117/12.2536697

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…There can be found multiple application of artificial-intelligence-aided computer vision in a variety of automated manufacturing inspection cases, such as steel [ 20 , 21 , 22 ], wood [ 23 , 24 , 25 , 26 ], and resin/plastic [ 27 , 28 , 29 ]. The mentioned investigations utilize deep neural networks as an algorithm to distinguish defects.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Augustauskas

Lipnickas

Surgailis³

2021

Sensors

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Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.

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Section: Related Workmentioning

confidence: 99%

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Augustauskas

Lipnickas

Surgailis³

2021

Sensors

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“…The paper presents examples of using 3D images in the implementation of tasks to check defects in geometry and surface defects of the product. The advantage of the method is that the three-dimensional image enables the implementation of a much larger number of measurements and analyses, especially in the field of spatial measurements, complementing and extending the currently used vision methods in the video industry based on two-dimensional image analysis [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis

Sioma

2020

Materials

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This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used in the process of image filtering and defect identification are presented. For selected defects, the method of measuring defect parameters and the method of visualization of defects on the surface are also presented. The developed method was tested on defective products to confirm its effectiveness in the field of quick defect detection in automated control systems installed on production lines.

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Tree-ring growth measurements automation using machine vision

Lenty¹

2020

Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2020

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Machine vision system for quality control of molded plastic packaging

Cited by 3 publications

References 4 publications

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis

Tree-ring growth measurements automation using machine vision

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