Works

Details of the tasks

This M2 internship is part of the CoHoMa III Challenge proposed by the Battle Lab Terre and supported by the Defense Innovation Agency, with CESI participating alongside NAE, WE Access, Conscience Robotics, and DAE.

It is evident that numerous solutions exist in the literature for human and vehicle detection in various applications such as autonomous vehicles and video surveillance. However, these approaches struggle with generalization and are highly sensitive to slight domain variations. Another critical issue is the lack of datasets contextualized to military challenges in outdoor environments, which makes model training and transfer learning challenging.

Our approach is to propose a solution based on mixed datasets, combining real scenes with synthetic objects reprojected into these scenes. The goal of this M2 internship is to assess the relevance of these datasets in deep learning for human and vehicle detection. The intern will be responsible for:

• Creating mixed datasets (synthetic and real data).
• Training the model using these mixed datasets.
• Testing and evaluating the proposal’s performance on synthetic datasets.

Project Context

This recruitment is part of the CoHoMa project (Human-Machine Collaboration), co-financed by the Normandy region and the European Union. The project aims to test, improve, and mature the technological components of a "Human-Machine Collaboration" system centered around drones and rovers. The CoHoMa III challenge, proposed by the Battle Lab Terre and supported by the Defense Innovation Agency (https://www.defense.gouv.fr/aid/actualites/battle-lab-terre-soutenu-laid-organise-troisieme-edition-du-challenge-collaboration-homme-machine), serves as one of the project’s testing grounds.

Organization

• Funding: CESI Nord-Ouest (CoHoMa project)
• Workplace : Rouen Campus (in Saint Etienne du Rouvray)
• Start date: February 2025
• Duration : 6-month internship

Your Recruitment

Profile Sought: Master's in Computer Science with a focus on computer vision, image processing, and machine learning.

Skills:

Scientific and technical skills:

Interpersonal Skills:

• Autonomy, initiative, curiosity
• Teamwork ability and good interpersonal skills
• Rigorousness

Application Process: by dossier and interview.

Send your application to Nicolas Ragot (nragot@cesi.fr), Vincent Vauchey (vvauchey@cesi.fr), with the subject line: "[Application] Title on page 1".

Your application should include:

• A detailed CV. If there is a gap in your academic career, please provide an explanation.
• A motivation letter explaining your interest in pursuing a doctoral thesis.
• A link to your previous works on GitHub would be an asset.
• Any other documents you consider useful.

Please send all documents in a single zip file named LASTNAME_firstname.zip