Deep Learning for Optical Sensor Applications: A Review

Abstract

Over the past decade, deep learning (DL) has 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 a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

“…86,87 However, since the field is constantly expanding and this is beyond the scope of this review, the interested reader is referred to respective reviews. 88…”

“…86,87 However, since the field is constantly expanding and this is beyond the scope of this review, the interested reader is referred to respective reviews. 88 Multivariate calibration. Classical univariate analytical calibration means that a sensor response is solely correlated exclusively with one target compound.…”

Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

“…4) Cloud computing infrastructure: -Cloud computing services such as AWS, Google Cloud, and Azure offer a scalable and cost-effective infrastructure for hosting web applications with image recognition capabilities. Using these services can make it easier to deploy and maintain your environmental monitoring application [25].…”

“…6) Real-time monitoring [25,26]: -Web-based applications can provide real-time monitoring capabilities, allowing users to get instant information about changes in the environment. This is crucial for timely decision-making and intervention in response to emerging environmental issues [27].…”

“…A web application for a neural network is software that allows users to interact with a neural network through a web browser [16][17]. Web applications can be used for various purposes, such as training, testing, and utilizing neural networks [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”