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Conference Papers Year : 2020

ColANet: A UAV Collision Avoidance Dataset

Dário Pedro
  • Function : Author
  • PersonId : 1153129
André Mora
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  • PersonId : 988912
João Carvalho
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  • PersonId : 1153130
Fábio Azevedo
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  • PersonId : 1153131
José Fonseca
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  • PersonId : 985928

Abstract

Artificial Intelligence is evolving at an accelerating pace alongside the increasing number of large datasets due to vast number of image data on the Internet. Unnamed Aircraft Vehicles (UAVs) are also a new trend that will have a huge impact over the next years. The use of UAVs arises some safety issues, such as collisions with dynamic obstacles like birds, other planes, or random thrown objects. Those are complex and sometimes impossible to avoid with state-of-the-art algorithms, representing a threat to the applications. In this article, a new video dataset of collisions, entitled ColANet, aims to provide a base for training new Machine Learning algorithms for handling the problem of avoiding collisions with high efficiency and robustness. It is also shown that using this dataset is easy to build new neural network models and test them.
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hal-03741569 , version 1 (01-08-2022)

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Dário Pedro, André Mora, João Carvalho, Fábio Azevedo, José Fonseca. ColANet: A UAV Collision Avoidance Dataset. 11th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jul 2020, Costa de Caparica, Portugal. pp.53-62, ⟨10.1007/978-3-030-45124-0_5⟩. ⟨hal-03741569⟩
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