eSmart Systems halves power line inspection time with Ultralytics YOLO

eSmart Systems is a Norway-based company that helps utility providers inspect and manage large-scale assets, such as power grids and substations, using computer vision and analytics. In particular, their flagship platform, Grid Vision®, leverages computer vision, geospatial analytics, and time-series data to analyze aerial imagery, detect components and defects, and provide predictive insights across transmission lines.

To further enhance inspection efficiency, eSmart Systems integrated Ultralytics YOLO models into Grid Vision®. This resulted in boosted defect detection speed and made it possible for utilities to shift from reactive repairs to more efficient, condition-based maintenance.

Link to this sectionTransforming power line inspections with AI and computer vision#

With headquarters in Halden, Norway, eSmart Systems focuses on bringing innovative solutions to the utilities sector to monitor and maintain critical infrastructure. For example, their flagship platform, Grid Vision®, provides a comprehensive solution for inspecting and managing large-scale assets like power grids and substations.

Trusted by over 70 utilities worldwide, eSmart Systems has inspected more than 100,000 kilometers of power lines, enabling utilities to make better, data-driven decisions. Grid Vision® makes maintenance more efficient, reduces risks, and supports the transition to more resilient and sustainable energy infrastructure.

eSmart Systems also ensures that its AI solutions meet high standards for data privacy and regulatory compliance. They are ISO 27001 certified for information security management and comply with Netcode Article 7.8, which governs secure data exchange in European electricity grid operations.

Link to this sectionThe complexities of grid inspections#

Power grids stretch across vast areas, often running through remote or hard-to-reach locations. Many of these systems are aging and require regular inspections to ensure safety and reliability. Inspecting components like transmission towers and power lines is time-consuming, costly, and can be risky for workers.

eSmart Systems aimed to capture aerial imagery using drones and helicopters, applying computer vision to detect components and identify defects. However, because utilities have different components and capture images in various conditions, it was challenging to maintain a consistent inspection workflow.

Fig 1. Power grids can be difficult to maintain.

Manually reviewing these images was also slow and resource-intensive, making it hard to scale fault detection. To automate inspections and support proactive maintenance, eSmart Systems needed a fast and adaptable Vision AI model that could perform reliably across asset types, regions, and weather conditions.

Link to this sectionThe role of object detection and YOLO in grid inspections#

To bring automation and intelligence to grid inspections, eSmart Systems integrated Ultralytics YOLO, a computer vision model, into its Grid Vision® platform. Ultralytics YOLO models support various computer vision tasks, including object detection, which allows the platform to identify key components like towers, crossarms, insulators, and conductors in aerial imagery.

The models are also being used to detect defects such as vegetation encroachment, damage, and wear, which can affect the grid’s performance. Once the components and defects are detected, this information is processed through Grid Vision®, which uses cloud-based processing to automate and scale the inspection process quickly and accurately.

Fig 2. Grid Vision® detects electrical components using YOLO.

The platform flags potential defects, evaluates the associated risk levels, and helps utilities plan maintenance based on the condition of the assets. This combination of real-time detection and analysis allows utilities to move from reactive maintenance to a more proactive approach, assisting them in staying ahead of potential issues before they lead to costly failures.

By integrating these insights with metadata and time-series data, Grid Vision® enables utilities to optimize their maintenance strategies, improving efficiency and reducing the risk of unexpected outages.

Link to this sectionWhy choose Ultralytics YOLO models?#

eSmart Systems adopted Ultralytics YOLO models for their speed, accuracy, and seamless integration into their AI pipeline. Ultralytics YOLO models deliver consistent results when analyzing large, high-resolution aerial images, making them ideal for grid inspections.

Also, the Ultralytics Python package offers a variety of integration options, including 15 export formats. This flexibility enables eSmart Systems to deploy the models across different environments. They use formats like PyTorch for training and ONNX for optimized CPU inference in production, particularly when GPU resources are limited in their cloud infrastructure.

With over 30 Ultralytics YOLO models already in production, eSmart Systems can efficiently scale inspections. This allows them to focus on improving data quality and addressing utility-specific challenges.

Link to this sectionReducing inspection time by 50% with Ultralytics YOLO#

The impact of Grid Vision®, powered by Ultralytics YOLO models, has been significant in enhancing utility inspections. By automating asset inspections and improving defect detection, Grid Vision® has reduced manual workloads, increased safety, and facilitated more proactive maintenance strategies.

For instance, in Switzerland, a major energy company managing thousands of pylons (tall structures supporting power lines) in mountainous terrain reduced inspection times by 50%. Transitioning from manual climbing to drone-based inspections sped up fault detection, improved worker safety, and saved time.

Similarly, in the United States, a large utility provider used Grid Vision® to digitize 1,400 transmission structures in just three months. This AI-driven image analysis replaced manual photo reviews, allowing for remote validation and enabling better, data-driven capital planning decisions.

Similarly, in Finland, a transmission system operator reduced field visits and minimized outages by switching from ground-based inspections to drone-assisted assessments. With Grid Vision® and YOLO-powered defect detection, inspection accuracy improved, and skilled workers were able to focus on more important tasks.

Fig 3. A look at power grid lines in Finland monitored using Grid Vision® and YOLO.

Link to this sectionPowering the next generation of utility inspections#

Looking ahead, as eSmart Systems expands globally, they are addressing challenges such as varying infrastructure, different image capture methods, and data drift across regions. To overcome these concerns, the company is focusing on making Grid Vision® more scalable and adaptable.

Their progress with MLOps pipelines has been key, simplifying model retraining and automating dataset expansion. These improvements continuously enhance the accuracy and performance of their AI solutions. eSmart Systems is paving the way for more efficient and reliable grid management, ensuring a future-ready approach to the global energy transition.

Interested in computer vision? Explore our GitHub repository to see how Ultralytics YOLO models are driving innovations in areas such as AI in self-driving cars and computer vision in agriculture. Learn more about our YOLO models and licensing options today!