ADVANCES IN DEEP LEARNING FOR UNDERWATER OBJECT DETECTION AND CLASSIFICATION: CHALLENGES, ARCHITECTURES, AND APPLICATIONS

Abstract

Abstract: The architecture, training techniques, and performance assessment of deep learning models for underwater item detection and classification are the main topics of this review study. It offers a thorough examination of neural network architectures specifically suited for recognising and classifying submerged objects, including artificial structures, detritus, and aquatic life. The study describes the training procedures, emphasising parameter optimisation and prediction performance enhancement, and highlights data pre-treatment techniques that are crucial for optimising model input. It also examines the evaluation measures used to gauge the model's efficacy and accuracy in actual underwater settings. The difficulties specific to underwater imaging are examined closely, including environmental influences and restrictions related to submerged settings. Lastly, useful insights into the practical utility and limitations of these models are provided by discussing their deployment considerations in real-world circumstances. With its thorough synthesis of recent developments in deep learning-based underwater object recognition and classification, this study offers a comprehensive knowledge of the field's achievements and difficulties.