NDLI: Optical Image Damage Detection Technology Based on Convolutional Neural Networks

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Optical Image Damage Detection Technology Based on Convolutional Neural Networks

Content Provider	Scilit
Author	Wang, Y. Yu, M. Zhong, S. Wang, L. Xu, M.
Copyright Year	2020
Description	Journal: Iop Conference Series: Earth and Environmental Science In the daily work of technical defense of universities, many optical devices need to be managed, and there are many components that are easily damaged in these devices. Therefore, timely detection of component damage in optical equipment is of great significance for reducing equipment losses and improving work efficiency. The traditional detection method is to use professional equipment for testing after removing the equipment. In this way, for the technical defense work of universities, the real-time online of the equipment cannot be guaranteed and the use cost of the equipment will increase. In the field of computer vision, deep learning and convolutional neural network technology have begun to play an important role with the wave of artificial intelligence. Therefore, this subject attempts to apply this technology to the damage detection of technical defense equipment in universities, thereby improving work efficiency and reducing equipment loss.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/510/2/022051/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/510/2/022051
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	2
Volume Number	510
Language	English
Publisher	IOP Publishing
Publisher Date	2020-06-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Characterization and Testing of Materials Convolutional Neural Network Damage Detection Technical Defense
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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