Abstract: Passive underwater acoustic target recognition (UATR) involves analyzing acoustic waves captured by passive sonar to extract valuable information about submerged targets. The underwater acoustics community has increasingly turned its attention to deep learning techniques, owing to their remarkable success in image recognition tasks. This study presents a comprehensive overview of the evolution of UATR techniques, categorizing them into three distinct groups: early methods, conventional machine learning approaches, and modern deep learning-based techniques. Additionally, it provides an in-depth summary of the recognition process utilizing deep learning, detailing various deep network architectures, classifiers specifically designed for underwater acoustic target recognition, and different data input modalities. Finally, the study synthesizes current research findings and outlines potential future directions for advancements in this field, emphasizing opportunities for innovation across these three categories.

Song Yifei and Mohamad Farhan Mohamad Mohsin, “Comparative Analysis of Deep Learning Techniques for Passive Underwater Acoustic Target Recognition: Overview, Challenges, and Future Directions” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160615

@article{Yifei2025,
title = {Comparative Analysis of Deep Learning Techniques for Passive Underwater Acoustic Target Recognition: Overview, Challenges, and Future Directions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160615},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160615},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Song Yifei and Mohamad Farhan Mohamad Mohsin}
}