YOLO, Mask R-CNN and reinforcement learning

Revolutionary technologies in use at TOPseven

In the rapidly developing world of drone technology, software developers face the challenge of developing innovative methods to improve drone performance. Advanced AI technologies form a crucial element of this development.

Three key technologies that are particularly promising in this respect are YOLO (You Only Look Once), Mask R-CNN (Mask Region-based Convolutional Neural Network) and reinforcement learning. In the following we will provide a small insight into these technologies and describe how TOPseven is successfully employing these techniques in its business.

YOLO: precise real time object detection

YOLO, or “You Only Look Once”, is a groundbreaking method for real time object detection. Unlike conventional approaches that analyse images several times to identify objects, YOLO can do this in one pass.
YOLO, a real time object detection model, has proven to be extremely powerful here. It enables drones to identify and inspect various objects in real time. At TOPseven we utilise YOLO to expand the ability of our drones to detect objects in complex environments. Whether the issue is the detection of structural damage on wind turbine generators, PV installations, bridges, etc. or the monitoring of these installations, YOLO is now an essential tool.

Object detection is a critical aspect of drone technology, particularly for applications such as the inspection and monitoring of complex infrastructure.

Mask R-CNN: detailed segmentation for advanced applications

Mask R-CNN is an expansion of R-CNN (Region Convolutional Neural Network) that not only detects objects, but also makes it possible to segment these objects precisely. This feature is extremely useful if a drone must not only know where an object is, but also what it looks like and how it moves.Mask R-CNN is a further powerful model that takes image segmentation and image classification to a new level. During the inspection of turbine generators or the monitoring of environments, it is often necessary not only to detect objects, but also to determine their exact position in images or videos. Mask R-CNN makes this task possible by not only detecting objects, but also creating masks that depict the precise position and shape of these objects. TOPseven uses Mask R-CNN to further optimise the accuracy of its inspection and monitoring services.

Reinforcement learning: automated drones for the future

Reinforcement learning is a key technology for the development of automated drones. This method makes it possible for drones to learn from experience and to improve their decision making. With the aid of RL, drones can avoid obstacles, adapt to different environments and plan optimal flight paths.

At TOPseven we use reinforcement learning to make our drones more autonomous. We train them to operate in complex, changing environments and to undertake challenging tasks, for example the inspection of wind turbine generators or the monitoring of large areas.


The integration of YOLO, Mask R-CNN and reinforcement learning in drone technology at TOPseven has opened the door to a new era of efficiency and precision. Using these technologies, our drones can master complex tasks that required human involvement in the past.

Even though the challenges during the development and implementation of such models should not be underestimated, the advantages in relation to speed, accuracy and versatility are indisputable. The possibilities for the future of drone technology at TOPseven are boundless. We are looking forward to expanding our expertise further in this area and taking our drone solutions to the next level; always true to our vision: MAKING DRONES SMARTER.