DDOS: The Drone Depth and Obstacle Segmentation Dataset
This addresses a critical gap for drone developers and researchers by providing a specialized dataset, though it is incremental as it builds on existing synthetic data approaches.
The authors tackled the lack of training datasets for autonomous drones in challenging real-world conditions by creating the synthetic DDOS dataset for semantic segmentation and depth estimation, which includes thin structures and various weather conditions to improve drone navigation and safety.
The advancement of autonomous drones, essential for sectors such as remote sensing and emergency services, is hindered by the absence of training datasets that fully capture the environmental challenges present in real-world scenarios, particularly operations in non-optimal weather conditions and the detection of thin structures like wires. We present the Drone Depth and Obstacle Segmentation (DDOS) dataset to fill this critical gap with a collection of synthetic aerial images, created to provide comprehensive training samples for semantic segmentation and depth estimation. Specifically designed to enhance the identification of thin structures, DDOS allows drones to navigate a wide range of weather conditions, significantly elevating drone training and operational safety. Additionally, this work introduces innovative drone-specific metrics aimed at refining the evaluation of algorithms in depth estimation, with a focus on thin structure detection. These contributions not only pave the way for substantial improvements in autonomous drone technology but also set a new benchmark for future research, opening avenues for further advancements in drone navigation and safety.