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Deep Learning for Optical Sensor Applications: A Review.

Nagi H Al-Ashwal^1,2, Khaled A M Al Soufy^1,2, Mohga E Hamza¹

¹Department of Physics, The American University in Cairo, New Cairo 11835, Egypt.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

Deep learning (DL) enhances optical sensor accuracy and reduces noise. Integrating DL addresses challenges like large datasets and high costs, paving the way for advanced intelligent sensing applications.

Keywords:

autoencoders convolutional neural network deep learning deep neural network optical sensors

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Area of Science:

Optical sensing technologies
Artificial intelligence
Data processing

Background:

Optical sensors are crucial for intelligent sensing in diverse fields like process monitoring, quality control, and security.
Traditional optical sensors face challenges including large data volumes, slow processing, and high costs.
Deep learning (DL) offers potential solutions to these limitations.

Purpose of the Study:

To review recent studies integrating deep learning algorithms with optical sensor applications.
To highlight promising directions for DL in optical sensing.
To propose future research avenues for DL-enhanced optical sensors.

Main Methods:

Literature review of recent studies on DL in optical sensing.
Analysis of DL algorithm integration with optical sensor data.
Identification of current challenges and future opportunities.

Main Results:

Deep learning significantly improves accuracy and reduces noise in optical sensor data.
Integration of DL mitigates challenges associated with large datasets and processing speeds.
DL applications are expanding across various sectors including pollution monitoring and defense.

Conclusions:

Deep learning is a key technology for advancing optical sensor capabilities.
Further research into DL algorithms can unlock new potentials for intelligent sensing.
Addressing data and cost challenges through DL integration is vital for future optical sensor development.