DDOS: The Drone Depth and Obstacle Segmentation Dataset

Autor:	Kolbeinsson, Benedikt, Mikolajczyk, Krystian
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at CVPR Workshops. Best Paper Honorable Mention Award. Dataset is available at https://huggingface.co/datasets/benediktkol/DDOS
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.12494 Zobrazit plný text záznamu View this record from Arxiv