Authors: Dr. A. Punitha, Dr. K. Vijayakumar, Dr. S. Vinoth, Dr. M. Perumal

ISBN: 978-81-999695-2-0

DOI: https://doi.org/10.59646/618

Date of Publication: March 12, 2026

Cite this book: A. Punitha, K. Vijayakumar, S. Vinoth, M. Perumal, (2026), Underwater Object Detection and Tracking: Using Machine Learning and Deep Learning Techniques, San International Scientific Publications, ISBN: 978-81-999695-2-0, https://doi.org/10.59646/618.

Preface

The oceans cover the majority of our planet and contain vast, largely unexplored environments that hold immense scientific, ecological, and strategic importance. Detecting and tracking objects underwater has therefore become a critical task in fields such as marine research, environmental monitoring, underwater exploration, and defense applications. However, underwater environments present unique challenges compared to terrestrial vision systems. Factors such as low visibility, light absorption, scattering, turbidity, color distortion, and dynamic water conditions significantly degrade image quality and make reliable object detection and tracking difficult. With the rapid advancements in computational methods, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools capable of overcoming many of these limitations. The book Underwater Object Detection and Tracking: Using Machine Learning and Deep Learning Techniques is designed to provide a comprehensive understanding of the principles, methodologies, and practical approaches used to detect and track objects in underwater environments using modern intelligent algorithms. The book begins with an introduction to underwater imaging systems and detection technologies, discussing the characteristics of underwater environments, the importance of object detection and tracking, and the applications of these technologies in marine science, defense, and exploration. It also explores optical and acoustic sensing systems, along with the challenges associated with underwater data acquisition and the growing role of autonomous underwater vehicles and robotics. Building on this foundation, the book presents the fundamentals of underwater image processing, including image formation, noise modeling, filtering techniques, image enhancement, color correction, dehazing, segmentation, and preprocessing pipelines required for machine learning and deep learning models. Subsequent chapters focus on the use of traditional machine learning methods such as k-Nearest Neighbors, Support Vector Machines, decision trees, and feature descriptors like SIFT, SURF, and HOG for object detection tasks. The book then transitions to modern deep learning approaches, introducing convolutional neural networks and advanced detection architectures including R-CNN variants, YOLO, and SSD, along with topics such as transfer learning, data augmentation, class imbalance handling, and model optimization for underwater datasets. In addition, the book examines object tracking algorithms including tracking-by-detection frameworks, Kalman filters, particle filters, optical flow methods, and deep learning-based multi-object tracking systems. Recognizing the importance of acoustic sensing in underwater environments, dedicated discussions are provided on sonar imaging, acoustic signal processing, machine learning for sonar data analysis, and the integration of optical and sonar sensing techniques. Finally, the book explores advanced concepts such as sensor fusion and multimodal learning, highlighting how data from multiple sensors—including optical cameras, sonar, and LiDAR—can be combined through deep learning models, temporal fusion strategies, and attention mechanisms to achieve robust and reliable detection under challenging underwater conditions. Overall, this book aims to serve as a comprehensive resource for students, researchers, and professionals interested in underwater computer vision, intelligent sensing systems, and advanced machine learning techniques applied to marine environments.