How Türk Telekom Achieved a 98% Reduction in Site Inspection Time Using AI and Computer Vision

About Türk Telekom:

Türk Telekom is Turkey’s first and largest integrated telecommunications operator, with a rich history of over 180 years dedicated to driving the nation’s connectivity. The company provides a complete range of mobile, broadband, TV, and voice services, underpinned by a world-class network infrastructure. At the core of its modern strategy is a deep commitment to digital transformation and technological leadership, with a strong emphasis on developing and deploying next-generation solutions.

Türk Telekom actively leverages Artificial Intelligence and network automation (AIOps) to build a more intelligent, efficient, and proactive network for the future. Through its dedicated technology and innovation teams, as well as its R&D subsidiary Argela, the company is a leader in creating high-impact AI applications. These initiatives focus on enhancing network resilience, optimizing energy consumption, and delivering superior, uninterrupted customer experiences, positioning Türk Telekom at the forefront of AI adoption in the telecommunications industry.

Summary

Türk Telekom, Turkey’s largest integrated telecom operator, has pioneered an innovative AI-driven solution that automates the visual inspection and inventory management of its mobile field system rooms. By leveraging 360° cameras and advanced computer vision models, this system provides real-time monitoring and predictive maintenance insights for critical Radio Access Network (RAN) infrastructure, dramatically reducing manual effort and enhancing network reliability.

The Challenge: Scaling Manual Inspections for Uninterrupted Service

The consistent and reliable operation of mobile network services hinges on the health of thousands of system rooms at cell sites. These rooms house critical equipment, including batteries, cooling units, and power systems.

Traditionally, Türk Telekom relied on manual field inspections to monitor this equipment. While effective, this process was time-intensive and difficult to scale across a vast and geographically dispersed network. The challenges included:

• Operational Inefficiencies: Manual checks required significant field engineer hours, diverting skilled personnel from more complex tasks.
• Time Sensitivity: Delays in identifying equipment issues, such as a failing battery or a faulty cooling unit, could impact service quality.
• Scaling Difficulties: Ensuring consistent and frequent inspections across hundreds or thousands of sites was a logistical and costly challenge.

To maintain its commitment to service excellence, Türk Telekom needed a smarter, more scalable solution to streamline inspections, improve maintenance planning, and guarantee network uptime.

The Solution: An AI-Powered Eye on the Network

The solution works in three key stages:

1. Continuous Visual Capture: At each mobile site, 360° cameras are installed to continuously capture complete, panoramic views of the system room.
2. AI-Powered Object Detection: The captured images are processed by a YOLO v8 Convolutional Neural Network (CNN) model. This powerful computer vision algorithm automatically scans the images to locate, identify, and label key equipment, such as battery belts, caps, and air conditioning units.
3. Automated Inventory & Anomaly Detection: The system compares the detected equipment against a pre-defined digital inventory standard. Any discrepancy—such as a missing component, an incorrect installation, or a visual anomaly—instantly triggers an alert. A detailed maintenance report is automatically generated and dispatched to the relevant field teams.
4. Predictive Maintenance: Data on visual inconsistencies is merged with operational metrics from the equipment. This combined dataset feeds a machine learning model that predicts component degradation and potential failures. This enables the operations team to move from reactive repairs to a proactive, data-driven maintenance schedule.

The entire architecture is designed to scale horizontally, allowing for the seamless addition of hundreds of dispersed sites without a linear increase in the need for field engineers.

Results & Benefits: A 98% Drop in Inspection Time and Enhanced Network Agility

The consistent and reliable operation of mobile network services hinges on the health of thousands of system rooms at cell sites. These rooms house critical equipment, including batteries, cooling units, and power systems.

Across an initial deployment of 200 sites, the solution achieved:

• Massive Efficiency Gains: Daily manual inspection time was reduced from 14 hours to just 15 minutes—a remarkable 98% reduction.
• Freed-Up Expertise: Field engineers are no longer tied up with routine visual checks, allowing them to focus on higher-value maintenance and optimisation tasks.
• Improved Compliance and Accuracy: The AI-verified inventory has slashed discrepancies, ensuring that compliance checks are faster and virtually error-free.
• Enhanced Service Reliability: By enabling proactive repairs and instantly flagging anomalies, the system strengthens the 24/7 service continuity guarantee for customers.

Ultimately, this innovative use of AI has not only streamlined a critical operational process but has also built a powerful, data-driven monitoring framework, significantly elevating Türk Telekom’s overall network agility and resilience.