Anomaly Detection in Electric Poles

Business Goals

• Ensure Public and Infrastructure Safety: Identify defects in electric poles early to prevent wildfires and outages.
• Support Utility Field Operations: Equip field personnel with visual intelligence to take timely corrective actions.
• Automate Defect Detection at Scale: Process thousands of images daily to detect issues with minimum human intervention.
• Enable Proactive Maintenance: Predict the likelihood of pole deterioration and optimize maintenance workflows.
• Integrate Into Global Ecosystem: Deploy a scalable and secure solution for use by international stakeholders.

Understanding the Challenges:

• High Risk of Wildfires: Defective poles are known to trigger wildfires, especially in high-temperature regions.
• Manual Inspections Are Inefficient: Traditional methods are time-consuming, costly, and error-prone.
• Massive Image Volumes: Drone programs capture large amounts of imagery, creating a need for scalable processing.
• Subtle Anomalies: Damage in subcomponents (e.g., cross arms, insulators) may not be easily visible to human reviewers.
• Global User Base: The system had to support users across geographies, from partners to field sales personnel.

Prerequisites and Preconditions:

To deliver this advanced anomaly detection solution, the following groundwork was laid:

• Drone-Captured Imagery Pipeline: Integrated drone footage upload directly into Azure Blob Storage.
• Image Labeling & Training Data: Curated a dataset of pole components and known defect patterns.
• Computer Vision Model Design: Trained object detection and classification models for poles and sub-parts.
• Azure-Based Processing Pipeline: Built an inference pipeline on the Azure tech stack for scalable detection.
• User Access Management: Set up secure access for internal teams, partners, and global field operators.

ThirdEye Data engineered an AI-driven anomaly detection system using Azure’s cloud capabilities and advanced computer vision techniques.

The deployed solution had a measurable impact on operational safety, maintenance efficiency, and environmental risk reduction:

• 92% Accuracy in Anomaly Detection: Significantly reduced false positives and manual verification efforts.
• Reduced Inspection Time by 70%: Automated processing allowed field teams to prioritize actual problem areas.
• Early Failure Prediction: Helped prevent potential wildfires and power disruptions with proactive interventions.
• Cost Savings in Field Operations: Lowered the costs of routine inspections and emergency maintenance.
• Environmental Impact Mitigation: Supported safer energy infrastructure and protected at-risk communities.