Communication tower inspections were traditionally conducted by technicians climbing structures — a process that was slow, expensive, and weather-dependent. The client wanted to use drones equipped with high-resolution cameras to capture inspection imagery, then use AI to analyze the results.
We developed a computer vision pipeline that processes drone-captured images to identify structural anomalies, surface corrosion, equipment damage, and snow or ice accumulation on tower components. The system classifies detected issues by severity and generates structured inspection reports.
The model was trained on labeled inspection data from the client’s existing maintenance records. We implemented multi-scale detection to handle varying distances and angles from drone flights, and designed the inference pipeline to run efficiently on edge hardware for field deployment.
The system now supplements manual inspections, helping the client prioritize maintenance schedules based on detected condition severity rather than fixed rotation intervals.