A Review on Computer Vision Applied to Mechanical Tests in Search for Better Accuracy

Baldner, Felipe de Oliveira; Costa, Priscila; Gomes, Jefferson de Oliveira; Leta, Fabiana Rodrigues

doi:10.1007/978-981-13-9806-3_9

Cited by 7 publications

(1 citation statement)

References 58 publications

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“…Numerous studies have delved into the application of machine vision techniques and deep learning for assessing the various attributes of objects [20][21][22][23][24][25]. Deep learning has been used in picture categorization and object detection in a substantial number of these studies.…”

Section: Introductionmentioning

confidence: 99%

Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

Fan,

Wang,

Khokhar

et al. 2023

Energies

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The thickness of the indium tin oxide (ITO) layer is a critical parameter affecting the performance of solar cells. Traditional measurement methods require sample collection, leading to manufacturing interruptions and potential quality issues. In this paper, we propose a real-time, non-contact approach using deep learning and optical interference phenomena to estimate the thickness of ITO layers in solar cells. We develop a convolutional neural network (CNN) model that processes microscopic images of solar cells and predicts the ITO layer thickness. In addition, mean absolute error (MAE) and mean squared error (MSE) loss functions are combined to train the model. Experimental results demonstrate the effectiveness of our approach in accurately estimating the ITO layer thickness. The integration of computer vision and deep learning techniques provides a valuable tool for non-destructive testing and quality control in the manufacturing of solar cells. The loss of the model after training is reduced to 0.83, and the slope of the test value in the scatter plot with the true value of the ellipsometer is approximately equal to 1, indicating the high reliability of the model.

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Section: Introductionmentioning

confidence: 99%

Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

Fan,

Wang,

Khokhar

et al. 2023

Energies

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Biomedical Image Segmentation Using Fuzzy Artificial Cell Swarm Optimization (FACSO)

Chakraborty

Mali

2022

Neural Process Lett

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A validation strategy for a target-based vision tracking system with an industrial robot

Romero

Naufal

Meza

et al. 2020

J. Phys.: Conf. Ser.

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Computer vision tracking systems are used in many medical and industrial applications. Understanding and modeling the tracking errors for a given system aids in the correct implementation and operation for optimal measurement results. This project aims to simulate and experimentally validate a tracking system for medical imaging. In this work, we developed a validation strategy for a target-based vision tracking system with an industrial robot. The simulation results show that the system can be accurately modeled, and the error assessment strategy is robust. Experimental verification with an EPSON C3 robot shows the reliability of the vision tracking system to obtain the target position and pose accurately. The general-purpose performance assessment strategy can be used as a vision tracking evaluation mechanism to ensure the system performance is adequate for a given application.

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A Review on Computer Vision Applied to Mechanical Tests in Search for Better Accuracy

Cited by 7 publications

References 58 publications

Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

Biomedical Image Segmentation Using Fuzzy Artificial Cell Swarm Optimization (FACSO)

A validation strategy for a target-based vision tracking system with an industrial robot

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