Survey on deep learning techniques used for object identification of underwater forward looking sonar images
autonomous underwater vehicles (AUVs); Deep learning framework; forward-looking sonar (FLS) image; object detection
Underwater object identification using forward-looking sonar (FLS) images is crucial for autonomous underwater vehicles (AUVs) for navigation and obstacle avoidance. Deep learning techniques have emerged as powerful tools for object recognition in various domains. This paper surveys deep learning approaches employed for object identification in FLS images. We examine the effectiveness of popular deep learning frameworks such as YOLOv5, EfficientDet, and MobileNet, and transfer learning, data enhancements to improve object recognition performance, and the role of adversaries training. We also examine the potential of focusing and lightweight CNN algorithms developed for FLS images despite these advances, challenges still exist due to the limited number of registered cases. The paper analyzes how deep learning methods address these challenges and highlights their effectiveness in object identification. We aim to provide a comprehensive overview of the current state-of-the-art in deep learning for FLS object identification, paving the way for further research and development in this field. Results of this study show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment. 2025 Author(s).
Agarwal, Apaar; Verma, Indu; Gupta, Varuna
AIP Conference Proceedings;Volume;3297;Issue;1;Article No.;286501;
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https://doi.org/10.1063/5.0286501 https://www.scopus.com/pages/publications/105022825480?origin=resultslist
Agarwal A., Christ University, Karnataka, Bangalore, India; Verma I., Christ University, Karnataka, Bangalore, India; Gupta V., Christ University, Karnataka, Bangalore, India