A hybrid Bi-LSTM and RBM approach for advanced underwater object detection

• Published in: PloS one Vol. 19; no. 11; p. e0313708
• Main Authors: S., Manimurugan, P., Karthikeyan, C., Narmatha, Aborokbah, Majed M., Paul, Anand, Ganesan, Subramaniam, T., Rajendran, Ammad-Uddin, Mohammad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.11.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Animals; Artificial intelligence; Classification; Computer vision; Datasets; Deep sea; Design and construction; Detectors; Equipment and supplies; Fish; Fourier transforms; Long short-term memory; Machine vision; Marine resources; Neural networks; Neural Networks, Computer; Resource development; Underwater; Underwater exploration; Saudi Arabia
• DOI: 10.1371/journal.pone.0313708
• ISSN: 1932-6203

This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). Bi-LSTM excels at capturing long-term dependencies and processing sequences bidirectionally to enhance comprehension of both past and future contexts. The model benefits from effective feature learning, aided by RBMs that enable the extraction of hierarchical and abstract representations. Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. Comprehensive evaluations on brackish, and URPC 2020 datasets demonstrate superior performance, with the BiLSTM-RBM model showcasing notable accuracies, such as big fish 98.5 for the big fish object in the brackish dataset and 98 for the star fish object in the URPC dataset. Overall, these findings underscore the BiLSTM-RBM model’s suitability for UOD, positioning it as a robust solution for effective underwater object detection in challenging underwater environments.