Attention-Based Underwater Oil Leakage Detection

This study addresses the pressing issue of oil and water and leakage detection in underwater pipes, which has become a major concern due to the increasing demand for pristine water and natural oil and a growing global demand. While extensive datasets exist for image and voice recognition, few datasets are available for the engineering detection of oil and water pipe leakage using acoustic signals. Consequently, many existing leak detection systems are ineffective at identifying breaches, resulting in major spills that cost pipeline companies millions of dollars. To address this problem, we propose a novel approach that employs an attention-based neural network methodology to predict underwater pipe leakage and evaluate the effectiveness of deep learning models. Our study employs sensor signal datasets from an actual industrial scenario, and our results indicate that the attention model outperforms other models in this domain. This study presents a promising avenue for addressing the issue of water leakage detection and management, which has significant implications for the water industry and the global population.