Deep Learning for Underwater Condition Monitoring of Offshore Energy Installations

Underwater structural health monitoring (SHM) is essential for protecting offshore structures from continuous challenges such as biofouling, corrosion, spalling, and cracks caused by harsh underwater conditions. Traditional inspection methods are time-consuming, expensive, and prone to human error. In this study, we propose an autonomous underwater robotic system integrated with deep learning algorithms to overcome these limitations. The system utilizes autonomous underwater robots equipped with optical cameras to collect high-resolution visual data. Collected images are preprocessed using denoising techniques to address visibility challenges, and defects are detected and classified using a Convolutional Neural Network (CNN) trained on annotated underwater defect datasets. Preliminary results indicate that the system significantly improves defect detection accuracy, achieving an 87% increase over conventional approaches. Additionally, the automated system reduces inspection costs by 93% and enhances operational efficiency by minimizing downtime and improving data reliability. By integrating robotics and deep learning(DL), this study enables proactive maintenance strategies, ensuring the long-term sustainability, performance, and resilience of critical underwater infrastructure.