Engineer position F/H – automatic detection of military vehicles in images and videos

il y a 1 mois

Technopole de Sophia Antipolis, France INRIA Temps plein

Contexte et atouts du poste

This open position is part of a collaboration between the STARS team of Inria Sophia-Antipolis, the Direction Générale de l’Armement (DGA) of the French Defense ministry, and the Defense and Security department at Inria.

 The aim of the project is to study state of the art algorithms for detection and identification, close to real-time, of military vehicles in images and video; to propose an implementation of these algorithms, possibly combining several techniques and/or models; and to evaluate this system. The evaluation of the system will not only consist of a global quantification of its performance, but also include a finer grained measure of its errors (false positives, silences) and deficits, as well as a measure of the cost of such errors for end users (time lost, danger risk, …).

 The goal of the project consists in applying recent advances in deeplearning (in the field of computer vision for the automatic detection, classification and identification of objects in images and videos) to a specific domain while taking into account all its inherent constraints and difficulties (confidentiality and scarcity of training data, noise and interferences on the data, ...)

Mission confiée

The engineer will rely on the expertise of the STARS team (François Bremond) in the field of computer vision to try out different algorithms and combinations of algorithms for the automatic detection, classification and tracking of objects in images and videos. He/She will focus on boosting the performances of such models while taking into account specific difficulties such as:

Low resolution Low contrast or lighting Occultation Reduced number of training images Unusual point of view Camouflage

The engineer will have to define procedures and tools for the evaluation of these algorithms. The evaluation procedures will have to account for the number of classes and the type of classes, as well as the number of images required to efficiently train a model and obtain good performance on a specific class.

Recent technics and models for deeplearning and data augmentation will be studied and proposed (such as using computer generated images or images generated from text prompts with diffusion models to enhance training datasets, …) to answer the specific constraints of the domain and provide sufficient data for training the models.

Principales activités

The main activities of the position include:

Keeping up-to-date with the state of the art in computer vision and specifically automatic detection of objects, or any other domain relevant for this position. Develop a system for automatic detection of military vehicles with state-of-the-art performance. Propose a methodology for evaluating the performances of such a system that factors in domain specific constraints, and use recent advances in deeplearning and computer vision to boost the performances of object detection models and for data augmentation. Interact with partners of the project and provide expertise on subjects related to computer vision.

Compétences

Qualities sought for this position include

A Masters’ degree or equivalent (engineering school) or a doctorate in the field of computer science, artificial intelligence, machine learning, deeplearning or computer vision. A strong level in programming (python, git, ...) A previous experience in the field of deeplearning (tensorflow, pytorch, cuda, ...) and an interest for the field of computer vision (OpenCV, YoloV8, …) A willingness to diversify and learn new skills while applying state of the art algorithms to new domains. An interest for collaborative and multidisciplinary work. Ability to show initiative & creativity.

This position can be a good opportunity for candidates interested in the field of computer vision and/or deeplearning, and can lead to other opportunities in academia (PhD, postdoc) for the successful candidate.

Avantages

Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) Possibility of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage

Poste rattaché au siège dans les Yvelines mais localisé au centre Inria de L'université Côte d'Azur.

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