Anomaly Detection in Drone Videos for Preventive Maintenance of Power Lines

Power lines are composed of various components that can deteriorate over time due to use. To detect potential issues with these components and prevent costly network outages, aerial drones are increasingly being utilized. They allow for the rapid inspection of large distances and provide a clear view of the different components and their defects. However, the manual analysis of flight videos by experts is labor-intensive. Moreover, the wide variety of anomalies that can cause network interruptions makes it impractical to develop dedicated automated solutions for each type, particularly due to the limited number of examples available for many anomalies. We thus propose a method to automatically detect anomalies in scenarios where no prior information about the visual appearance of anomalies is available, using drone-acquired videos. The approach relies on the computation of an anomaly score based on a generic feature vector. Results demonstrate that this approach is effective in scenarios without any examples of anomalies and requires very limited computational resources for learning.