Enhancing Underwater Imagery with AI/ML and IoT in ROV Technology

Remotely operated vehicles (ROVs) enable underwater exploration and research but face imaging challenges from environmental conditions. This chapter examines how emerging technologies can enhance underwater imagery captured by ROVs. Specifically, the integration of artificial intelligence, machine learning (AI/ML), and Internet of Things (IoT) capabilities can automate and optimize image pre-processing, reduce noise, correct colors, and enable real-time analysis. Relevant algorithms and models are reviewed, including convolutional neural networks, color equalization, and utilization of RGB color proportions. This chapter begins by outlining the inherent environmental and technical challenges involved in capturing high-quality underwater images, particularly those obtained using remotely operated vehicles (ROVs). The discussion then shifts to the role of AI/ML in image pre-processing, with a specific focus on the models developed to improve image quality by reducing noise, adjusting color, and enhancing clarity. Various case studies are being carried out to examine the application of Internet of Things (IoT)-enabled sensors and cameras that are installed on remotely operated vehicles (ROVs), along with automated image pre-processing and real-time data transmission. This study showcases the practicality of these technologies in marine biology, underwater exploration, and environmental monitoring through a diverse range of illustrations. Furthermore, the chapter highlights the importance of maintaining a harmonious equilibrium between environmental and ethical factors when utilizing state-of-the-art underwater technology. This chapter concludes by presenting a perspective on future advancements and proposing recommendations for the optimal incorporation of AI/ML and the Internet of Things into ROV technology.