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

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 mod...