An overview of 1D integral imaging convolutional neural networks applied in underwater optical signal detection under degraded environments

Yinuo Huang; Gokul Krishnan; Timothy O'Connor; Rakesh Joshi; Bahram Javidi

doi:10.1117/12.3013527

7 June 2024 An overview of 1D integral imaging convolutional neural networks applied in underwater optical signal detection under degraded environments

Yinuo Huang, Gokul Krishnan, Timothy O'Connor, Rakesh Joshi, Bahram Javidi

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Proceedings Volume 13041, Three-Dimensional Imaging, Visualization, and Display 2024; 130410H (2024) https://doi.org/10.1117/12.3013527
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

In this paper, we overview the previously reported underwater signal detection system using 1D integral imaging convolutional neural networks (1DInImCNN). The 1DInImCNN system comprises cameras arranged in a one-dimensional configuration for optical signal collection and the 1DInImCNN approach for signal detection. The 1D camera array is used to capture the spatial and temporal information, encoded using Gold code and transmitted by a Light-emitting Diode (LED). Various turbidities and occlusions are created in a water tank to test the performance of the proposed method under such degradations. The 1DInImCNN method is compared to the previously proposed 3D integral imaging (3D InIm) with Convolutional neural network (CNN) and Bi-Long Short-term memory (Bi-LSTM) approach. The results suggest that the 1DInImCNN-based approach outperforms the previously proposed 3D InIm with the CNN-BiLSTM approach in terms of computation costs and detection performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yinuo Huang, Gokul Krishnan, Timothy O'Connor, Rakesh Joshi, and Bahram Javidi "An overview of 1D integral imaging convolutional neural networks applied in underwater optical signal detection under degraded environments", Proc. SPIE 13041, Three-Dimensional Imaging, Visualization, and Display 2024, 130410H (7 June 2024); https://doi.org/10.1117/12.3013527

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