LARD 2.0: 自律着陸システムのための拡張データセットとベンチマーキング
LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems
自律着陸システムの物体検出モデル訓練用データセットLARDを拡張し、多様な画像ソースの追加や現実的なシナリオへの改良、複数滑走路環境でのベンチマークフレームワークを提供した論文。
著者: Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe, Augustin Fuchs, Jean-Brice Ginestet, Jacques Girard, Sofiane Kraiem, Franck Mamalet, Vincent Mussot, Claire Pagetti, Thierry Sammour
分類: cs.RO, cs.AI
原文アブストラクト
This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.
関連論文
- HiPHI: 高精度な人体動作と物体インタラクションのための大規模ベンチマークデータセット/ベンチマーク