Automating Tower Inventory and Anomaly Detection for Enhanced Efficiency
The Challenge
Managing a vast network of cell towers proved to be a significant challenge. The manual process of counting antennas and identifying anomalies through drone imagery was time-consuming, inefficient, and prone to errors. Additionally, workers had to climb towers for inspections, which posed safety risks. There was a need for a more efficient, accurate solution to streamline the process, enhance worker safety, and uncover potential revenue opportunities by detecting unauthorized antenna installations.
The Solution
A comprehensive AI-powered solution was developed to address these challenges. The solution involved creating a machine learning pipeline capable of automatically detecting, classifying, and quantifying antennas from drone imagery. A custom application was built to enable the team to upload images, manage batches, and review the AI’s analysis. The application provided detailed reports on antenna counts, variances from expected inventory, and potential anomalies such as bird nests or structural damage. Additionally, the solution incorporated a robust data management system, including a document-based database and a data warehouse, to store and analyze historical data for future insights.
The Result
Time Savings & Efficiency: Automation saved thousands of hours annually, allowing personnel to focus on higher-value tasks.
Improved Accuracy & Revenue Recovery: AI’s precision led to better billing accuracy and uncovered potential revenue from unauthorized installations.
Enhanced Worker Safety: Reduced the need for tower climbs, improving worker safety and minimizing risks.
Optimized Operations with AI: AI streamlined operations, improving data accuracy and setting a new standard for operational efficiency.
Automating Tower Inventory and Anomaly Detection for Enhanced Efficiency
The Challenge
Managing a vast network of cell towers proved to be a significant challenge. The manual process of counting antennas and identifying anomalies through drone imagery was time-consuming, inefficient, and prone to errors. Additionally, workers had to climb towers for inspections, which posed safety risks. There was a need for a more efficient, accurate solution to streamline the process, enhance worker safety, and uncover potential revenue opportunities by detecting unauthorized antenna installations.
The Solution
A comprehensive AI-powered solution was developed to address these challenges. The solution involved creating a machine learning pipeline capable of automatically detecting, classifying, and quantifying antennas from drone imagery. A custom application was built to enable the team to upload images, manage batches, and review the AI’s analysis. The application provided detailed reports on antenna counts, variances from expected inventory, and potential anomalies such as bird nests or structural damage. Additionally, the solution incorporated a robust data management system, including a document-based database and a data warehouse, to store and analyze historical data for future insights.
The Result
Time Savings & Efficiency: Automation saved thousands of hours annually, allowing personnel to focus on higher-value tasks.
Improved Accuracy & Revenue Recovery: AI’s precision led to better billing accuracy and uncovered potential revenue from unauthorized installations.
Enhanced Worker Safety: Reduced the need for tower climbs, improving worker safety and minimizing risks.
Optimized Operations with AI: AI streamlined operations, improving data accuracy and setting a new standard for operational efficiency.
