OUR METHODS

2D Bounding Box Annotation for Aviation & Drones

We use perfectly aligned 2D bounding boxes to label key components on aircraft and drones. This method is essential for creating datasets that train systems in object detection and part identification, such as engines, wings, and rotor assemblies.

Precise Contour Annotation

By drawing detailed contours around objects, we capture the exact shape of complex components. This technique is used to label irregular features on aircraft surfaces or drone structures, enabling precise model training for defect detection and maintenance inspections.

Pixel-Level Semantic Segmentation

This approach involves labeling images at the pixel level to create comprehensive segmentation maps. For aviation applications, it’s used to differentiate between various parts of an aircraft, while for drone imagery, it helps in accurately segmenting environmental features for autonomous navigation.

3D Cuboidal Annotation

For applications requiring 3D analysis, we annotate objects using cuboids. This technique is particularly valuable for processing both 2D images and 3D point cloud data, facilitating the training of models that assess structural integrity and spatial relationships in complex aerial scenes.

Keypoint Detection for Critical Features

Keypoint annotation involves marking specific points on an object to define its structure. In aviation, this method is used to label critical features such as control surfaces or instrumentation panels, while for drones, it can be used to track sensor positions and other vital components.

Polyline Annotation for Flight Paths

This technique involves drawing polylines to label trajectories, such as flight paths, runway lanes, or movement patterns captured in video sequences. It is crucial for training systems on path prediction and autonomous navigation in both aviation and drone operations.