Application of image recognition in workpiece classification
With the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion...
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SAGE Publishing
2021-06-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/16878140211026082 |
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doaj-a5b78b6a976b4c8bbf63a10bd3d4947f2021-06-24T22:03:33ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402021-06-011310.1177/16878140211026082Application of image recognition in workpiece classificationHsin-Yi ChienYu-Chen WangGuan-Chen ChenWith the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion to achieve automatic classification. First, we upload captured images to a PC for classification process and use chess patterns to conduct a sampling test. Next, when the system identifies these patterns as proper chess patterns, the robotic arm grabs the objects and moves them to designated locations. The project is divided into two main sections: image recognition and robotic arm motion. In the image recognition section, we use Keras and the Tensorflow open source learning machine to build a convolutional neural network model. Then, we use a learning model network that is a considerably more compact variant of the VGGNet network in the image recognition system. With this model, we achieve a recognition accuracy of 95%. In the robotic arm section, we use a five-axis robotic arm and an Arduino Uno board as the controller. We design the Denavit–Hartenberg parameters of the arm and calculate the direct (inverse) kinematics parameters to plan its trajectory. Thereafter, we use MATLAB software to simulate prototype processes, such as grabbing, moving, and placing. Finally, we import the program into the controller so that the robotic arm can execute classification based on the chess pattern.https://doi.org/10.1177/16878140211026082 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hsin-Yi Chien Yu-Chen Wang Guan-Chen Chen |
spellingShingle |
Hsin-Yi Chien Yu-Chen Wang Guan-Chen Chen Application of image recognition in workpiece classification Advances in Mechanical Engineering |
author_facet |
Hsin-Yi Chien Yu-Chen Wang Guan-Chen Chen |
author_sort |
Hsin-Yi Chien |
title |
Application of image recognition in workpiece classification |
title_short |
Application of image recognition in workpiece classification |
title_full |
Application of image recognition in workpiece classification |
title_fullStr |
Application of image recognition in workpiece classification |
title_full_unstemmed |
Application of image recognition in workpiece classification |
title_sort |
application of image recognition in workpiece classification |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2021-06-01 |
description |
With the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion to achieve automatic classification. First, we upload captured images to a PC for classification process and use chess patterns to conduct a sampling test. Next, when the system identifies these patterns as proper chess patterns, the robotic arm grabs the objects and moves them to designated locations. The project is divided into two main sections: image recognition and robotic arm motion. In the image recognition section, we use Keras and the Tensorflow open source learning machine to build a convolutional neural network model. Then, we use a learning model network that is a considerably more compact variant of the VGGNet network in the image recognition system. With this model, we achieve a recognition accuracy of 95%. In the robotic arm section, we use a five-axis robotic arm and an Arduino Uno board as the controller. We design the Denavit–Hartenberg parameters of the arm and calculate the direct (inverse) kinematics parameters to plan its trajectory. Thereafter, we use MATLAB software to simulate prototype processes, such as grabbing, moving, and placing. Finally, we import the program into the controller so that the robotic arm can execute classification based on the chess pattern. |
url |
https://doi.org/10.1177/16878140211026082 |
work_keys_str_mv |
AT hsinyichien applicationofimagerecognitioninworkpiececlassification AT yuchenwang applicationofimagerecognitioninworkpiececlassification AT guanchenchen applicationofimagerecognitioninworkpiececlassification |
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