Automated, near real-time inspection of commercial sUAS imagery using deep learning

Kawatsu, Chris; Purman, Benjamin; Zhao, Aaron; Gillies, Andy; Jeffers, Mike; Sheridan, Paul

doi:10.1117/12.2304967

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2023

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“…The explosive increase in Internet data year by year has called for more intelligent and effective data processing [23]. As is well-known, there is a positive correlation between the accuracy of a CNN and the number of parameters [24]. Therefore, it has more stringent requirements on the computing hardware due to the demands of massive data processing and high-precision processing.…”

Section: Introductionmentioning

confidence: 99%

Optical Convolutional Neural Networks: Methodology and Advances (Invited)

Meng,

Shi,

et al. 2023

Applied Sciences

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As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application.

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Section: Introductionmentioning

confidence: 99%