AI-Powered Drone Solutions for Base Station Inspections

Short Description 

Tietoevry partnered with a Tier 1 Network Equipment Producer to develop an AI-driven drone inspection system for base station maintenance in the telecom industry. The solution automates the inspection of antenna verticality, azimuth, and down tilt, ensuring compliance with strict deviation thresholds (azimuth <3°, down tilt <1°). Leveraging machine learning, OpenCV, and real-time data analysis, the system delivers actionable insights while enhancing safety and operational efficiency. 

Executive Summary 

Goal: Develop a remote, automated inspection system via AI drones to replace manual base station checks, focusing on antenna alignment and structural integrity. The solution aims to minimize human errors, reduce downtimes, and ensure compliance with network performance standards. 

Solution: A comprehensive, AI-driven system that seamlessly integrates computer vision, drone technology, and machine learning to enhance antenna maintenance and inspection. The solution leverages OpenCV and advanced machine learning algorithms for real-time edge detection and image processing, enabling precise analysis of antenna alignment and structural integrity. By incorporating gyroscopic sensors, the system calculates horizontal reference lines and measures tilt accuracy with high precision, ensuring reliable data collection during aerial inspections.  

Benefits: Transforms telecom infrastructure maintenance by delivering critical operational advantages. Eliminates safety risks associated with high-altitude climbing through fully automated, drone-based inspections. AI-powered analysis reliably detects structural anomalies, dramatically accelerates workflows, and reduces typical maintenance cycles.  

Services delivered: AI Development, Drone Integration, Real-Time Data Analytics, Software Engineering. 

About the Client 

• Name: Tier 1 Network Equipment Producer 
• Location: Global (HQ in the EU) 
• Industry: Telecommunications 
• Number of employees: 100,000 

Business Challenge 

Manual base station inspections have historically presented critical operational risks and inefficiencies. Traditional methods exposed technicians to dangerous high-altitude climbing and suffered from measurement inconsistencies that compromised network performance. These human-dependent processes struggled to scale across remote or hazardous locations while failing to maintain the required alignment precision.  

Our client wanted to automate inspections without compromising stringent technical standards, namely antenna azimuth range of less than 3 degrees and down tilt angle deviation of less than 1 degree. The ultimate goal was to enhance network coverage reliability, reduce maintenance costs, and eliminate safety risks associated with manual operations. 

Solution & Business Value 

The solution designed by Tietoevry Create for the Tier 1 Network Equipment Producer combines machine learning, OpenCV, image processing, and automated analysis to revolutionize antenna inspections.  

The system employs OpenCV-powered edge detection algorithms to assess antenna verticality and alignment through precise visual measurements, while simultaneously utilizing a trained AI model to identify structural defects.  

By integrating gyroscopic sensors with computer vision, the solution establishes accurate horizontal reference planes, enabling down-tilt measurements with <1° deviation precision. Drones systematically capture multi-perspective imagery and achieve antenna azimuth alignment while maintaining the client's stringent <3° accuracy deviation.  

Customer Value 

• Achieving a 10x efficiency improvement as automation levels increase. 
• Safety insurance for network operations workers. 
• Network performance improvement of up to 10% (KPI improvement in some circumstances). 
• Supports simultaneous inspections of multiple sites. 
• Automated report generation highlights raising issues such as rusted bolts or damaged weatherproofing. 

Technical Details 

To meet the strict requirements, the solution employs AI-powered OpenCV edge detection to get the edge of the antenna, followed by the real-time calculation of the horizontal reference line derived from the drone's gyroscope data. Antenna azimuth is measured from four orthogonal perspectives: top, down, side, and backside.  

A dedicated team of four engineers implemented an agile, iterative approach, ensuring continuous improvement based on field validation and evolving business needs. 

Development Approach: 

• Agile methodology featuring iterative delivery model, adjusting technologies and the solution to new challenges when they appear. 

• Close collaboration with the client – a team consisting of AI engineers and telecom experts to ensure full alignment with the business requirements and expectations. 

Frontend & visualization 

• Developed a highly responsive website: AI Drone Site Inspection. 

Technologies & Tools Used 

• Machine learning 
• OpenCV  
• Image processing 
• Agile