Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Agarwal, Apaar; Verma, Indu; Gupta, Varuna Title A name given to the resource Survey on deep learning techniques used for object identification of underwater forward looking sonar images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3297;Issue;1;Article No.;286501; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0286501" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0286501</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022825480?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022825480?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Agarwal A., Christ University, Karnataka, Bangalore, India; Verma I., Christ University, Karnataka, Bangalore, India; Gupta V., Christ University, Karnataka, Bangalore, India Description An account of the resource 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). Subject The topic of the resource autonomous underwater vehicles (AUVs); Deep learning framework; forward-looking sonar (FLS) image; object detection Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online