{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:18:00Z","timestamp":1785421080209,"version":"3.56.0"},"reference-count":66,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach\u2019s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.<\/jats:p>","DOI":"10.3390\/rs13132643","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T02:59:47Z","timestamp":1625540387000},"page":"2643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-8862","authenticated-orcid":false,"given":"D\u00e1rio","family":"Pedro","sequence":"first","affiliation":[{"name":"Projecto Desenvolvimento Manuten\u00e7\u00e3o Forma\u00e7\u00e3o e Consultadoria, 1300-609 Lisbon, Portugal"},{"name":"Center of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal"},{"name":"Electrical Engineering Department, FCT, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9409-7736","authenticated-orcid":false,"given":"Jo\u00e3o P.","family":"Matos-Carvalho","sequence":"additional","affiliation":[{"name":"Cognitive and People-Centric Computing Labs (COPELABS), Universidade Lus\u00f3fona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7173-7374","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Center of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal"},{"name":"Electrical Engineering Department, FCT, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-4739","authenticated-orcid":false,"given":"Andr\u00e9","family":"Mora","sequence":"additional","affiliation":[{"name":"Center of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal"},{"name":"Electrical Engineering Department, FCT, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Patias, P. 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