Al-Ashwal, N.H.; Al Soufy, K.A.M.; Hamza, M.E.; Swillam, M.A. Deep Learning for Optical Sensor Applications: A Review. Sensors2023, 23, 6486.
Al-Ashwal, N.H.; Al Soufy, K.A.M.; Hamza, M.E.; Swillam, M.A. Deep Learning for Optical Sensor Applications: A Review. Sensors 2023, 23, 6486.
Al-Ashwal, N.H.; Al Soufy, K.A.M.; Hamza, M.E.; Swillam, M.A. Deep Learning for Optical Sensor Applications: A Review. Sensors2023, 23, 6486.
Al-Ashwal, N.H.; Al Soufy, K.A.M.; Hamza, M.E.; Swillam, M.A. Deep Learning for Optical Sensor Applications: A Review. Sensors 2023, 23, 6486.
Abstract
Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.
Keywords
Deep learning; optical sensors; DNN; CNN; Autoencoder
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
