Offre en lien avec l’Action/le Réseau : – — –/Doctorants
Laboratoire/Entreprise : IRISA/ATERMES
Durée : 3 years
Contact : minh-tan.pham@irisa.fr
Date limite de publication : 2023-05-30
Contexte :
ATERMES is an international mid-sized company, based in Montigny-le-Bretonneux with a strong expertise in high technology and system integration from the upstream design to the long-life maintenance cycle. It specializes in offering system solution for border surveillance. Its flagship product BARIER™ (“Beacon Autonomous Reconnaissance Identification and Evaluation Response”) provides ready application for temporary strategic site protection or ill-defined border regions in mountainous or remote terrain where fixed surveillance modes are impracticable or overly expensive to deploy. As another exemple, SURICATE is the first of its class optronic ground “RADAR” that covers very efficiently wide field with automatic classification of intruders thanks to multi-spectral deep learning detection.
The collaboration between ATERMES and IRISA was initiated through a first PhD thesis (Heng Zhang, defended December 2021, https://www.theses.fr/2021REN1S099/document). This successful collaboration led to multiple contributions on object detection in both mono-modal (RGB) and multi-modal (RGB+THERMAL) scenarios. Besides, this study allowed to identify remaining challenges that need to be solved to ensure multispectral object detection in the wild.
Sujet :
The project aims at providing deep learning-based methods to detect objects in outdoor environments using multispectral data in a low supervision context, e.g., learning from few examples to detect scarcely-observed objects. The data consist of RGB and IR (Infra-red) images which are frames from calibrated and aligned multispectral videos.
Few-shot learning [1][2], active learning [3] and incremental/continual learning [4][5] are among the frameworks to be investigated since they allow to limit the number of labeled examples needed for learning. Most developed methods [6][7][8][9] based on these approaches have been proposed to perform object detection from RGB images within different weakly-supervised scenarios. They should be adapted and improved to deal with scarce object detection from multispectral images.In case of lacking objects of interest during the training, anomaly detection approaches [10][11] can be also considered to detect new object classes which will be further characterized by prior semantic concepts.
In addition to the (private) data from ATERMES, the PhD candidate will be able to work with public benchmarks such as KAIST, FLIR, VEDAI or MIL to benchmark the developed frameworks in the vision and machine learning communities.
Profil du candidat :
MSc or Engineering degree with excellent academic track and proven research experience in the following fields: computer science, applied maths, signal processing and computer vision;
Formation et compétences requises :
Experience with machine learning, in particular deep learning;
Skills and proved experience in programming (Python and frameworks such as Pytorch/Tensorflow will be appreciated);
Good communication skills (spoken/written English) is required ;
Adresse d’emploi :
The PhD candidate will work part time (80%) at IRISA (with 1 day per week in Rennes and the rest of the time in the Vannes IRISA facility) and part time (20%) in ATERMES in Paris (which corresponds to 2 days every 2 weeks). The exact schedule will be flexible: it might be preferable to spend more time in the company at the beginning of the thesis to learn about the system and understand the data and be full time in the lab while writing the PhD dissertation.
Document attaché : 202302091035_PHD_IRISA_Atermes_2023.pdf