{"id":1161,"date":"2022-08-17T11:15:11","date_gmt":"2022-08-17T09:15:11","guid":{"rendered":"https:\/\/web.umons.ac.be\/isia\/?p=1161"},"modified":"2023-06-26T22:05:25","modified_gmt":"2023-06-26T20:05:25","slug":"publication221001","status":"publish","type":"post","link":"https:\/\/web.umons.ac.be\/isia\/publication221001\/","title":{"rendered":"Un nouvel article d’ISIA Lab sur la d\u00e9tection d’ombre de drone \u00e0 la conf\u00e9rence ESANN 2022 \u00e0 Bruges"},"content":{"rendered":"

Titre<\/strong><\/p>\n

\n

Semi-synthetic Data for Automatic Drone Shadow Detection <\/span><\/h1>\n<\/blockquote>\n

Auteures<\/span><\/strong><\/p>\n

Mohammed El Amine Mokhtari, Virginie Vandenbulcke, Sohaib Laraba, Matei Mancas, Elias Ennadifi, Mohamed Lamine Tazir, and Bernard Gosselin<\/span><\/p>\n

R\u00e9sum\u00e9<\/span><\/span><\/span><\/strong><\/p>\n

In this paper, we deal with the problem of shadow detection of UAVs, which impacts their navigation. We propose to generate synthetic images containing shadows in random locations, backgrounds, sizes, and opacities in order to augment our dataset. The generated data is used to train and compare several models to effectively detect, in real-time, UAVs shadows which will help to stabilize their localization and navigation. Deep learning models such as SSD, YOLOv3, and YOLOv5 are tested for the detection part. With our approach, we achieved 99% of the mean average precision when using the YOLOv5.<\/p>\n

 <\/p>\n

Link:<\/strong> https:\/\/www.esann.org\/sites\/default\/files\/proceedings\/2022\/ES2022-82.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Mohammed El Amine Mokhtari du laboratoire ISIA pr\u00e9sente un article sur la d\u00e9tection des sourires et des rires lors de la conf\u00e9rence ESANN. 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