Video Analytics using Deep Learning in Cloud Services to Detect Corrosion - A Comprehensive Survey

The research study analyzes the combination of deep learning and video analytics for corrosion detection, tackling the growing problems that corrosion poses to vital infrastructure. This study emphasizes the shortcomings of conventional inspection techniques while highlighting the benefits of automated, real-time monitoring made possible by cutting-edge technology. This review study focuses on the use of deep learning architectures like CNNs and RNNs, classifying and assessing the body of research. This study provides insights on different approaches by examining crucial factors like model performance indicators, preprocessing techniques, and dataset properties. This research study also addresses the environmental constraints and model interpretability as practical obstacles to implement these solutions in real-time. Also, this study suggests directions for future research, including the investigation of multi-modal techniques, connections to IoT devices, and developments in transfer learning for better generalization. For researchers, practitioners, and decision-makers looking to improve corrosion detection techniques in critical infrastructure, this research study offers insightful information.