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Open AccessArticle

A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN

1
School of Mechanical Engineering Sichuan University, Chengdu 610041, Sichuan, China
2
College of Letters and Science, University of Wisconsin Madison, Madison, WI 53707, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2360; https://doi.org/10.3390/s20082360
Received: 6 March 2020 / Revised: 5 April 2020 / Accepted: 14 April 2020 / Published: 21 April 2020
In this paper, a complete system based on computer vision and deep learning is proposed for surface inspection of the armatures in a vibration motor with miniature volume. A device for imaging and positioning was designed in order to obtain the images of the surface of the armatures. The images obtained by the device were divided into a training set and a test set. With continuous experimental exploration and improvement, the most efficient deep-network model was designed. The results show that the model leads to high accuracy on both the training set and the test set. In addition, we proposed a training method to make the network designed by us perform better. To guarantee the quality of the motor, a double-branch discrimination mechanism was also proposed. In order to verify the reliability of the system, experimental verification was conducted on the production line, and a satisfactory discrimination performance was reached. The results indicate that the proposed detection system for the armatures based on computer vision and deep learning is stable and reliable for armature production lines. View Full-Text
Keywords: armature; computer vision; deep learning; surface inspection armature; computer vision; deep learning; surface inspection
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Feng, T.; Liu, J.; Fang, X.; Wang, J.; Zhou, L. A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN. Sensors 2020, 20, 2360.

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