DDOS: The Drone Depth and Obstacle Segmentation Dataset
Benedikt Kolbeinsson Krystian Mikolajczyk
Accurate depth and semantic segmentation are crucial for various computer
vision tasks. However, the scarcity of annotated real-world aerial datasets
poses a significant challenge for training and evaluating robust models.
Additionally, the detection and segmentation of thin objects, such as wires,
cables, and fences, present a critical concern for ensuring the safe operation
of drones. To address these limitations, we present a novel synthetic dataset
specifically designed for depth and semantic segmentation tasks in aerial
views. Leveraging photo-realistic rendering techniques, our dataset provides a
valuable resource for training models using a synthetic-supervision training
scheme while introducing new drone-specific metrics for depth accuracy.