Computer Vision for Anomaly Detection in Optical Networks with State of Polarization Image Data: Opportunities and Challenges

Anomaly detection in optical networks is vital for network reliability. We investigate leveraging state of polarization (SOP) data and computer vision for this task, despite challenges like scarce anomaly data. Our study explores using deep learning to identify anomalies in optical networks, focusing on SOP time series derived images data. Challenges such as anomaly rarity are addressed through innovative approaches. Our research sheds light on the potential of combining SOP data and computer vision for robust anomaly detection in optical networks, while also highlighting the ongoing efforts to overcome associated challenges and propel the field forward.