[STAGE MASTER 2 ] - Enhancing tool detection through data augmentation and transfer learning Strategies in Industry 5.0

Introduction to the laboratory CESI LINEACT- Research Unit

CESI LINEACT (Digital Innovation Laboratory for Companies and Learnings at the service of the territories competitiveness) is the CESI group laboratory whose activities are implemented on CESI campuses.

Link to the laboratory website:

https://lineact.cesi.fr/en/

https://lineact.cesi.fr/en/research-unit/presentation-lineact/

CESI LINEACT (EA 7527), Digital Innovation Laboratory for Business and Learning at the service of the Competitiveness of Territories, anticipates and accompanies the technological mutations of the sectors and services related to industry and construction. CESI's historical proximity to companies is a determining factor for our research activities and has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as the territorial network and the links with training, have allowed us to build transversal research; it puts the human being, his needs and his uses, at the center of its problems and approaches the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific themes and two application areas.

• Theme 1 "Learning and Innovation" is mainly concerned with Cognitive Sciences, Social Sciences and Management Sciences, Training Sciences and Techniques and Innovation Sciences. The main scientific objectives of this theme are to understand the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems, etc.) on the learning, creativity and innovation processes.
• Theme 2 "Engineering and Digital Tools" is mainly concerned with Digital Sciences and Engineering. The main scientific objectives of this theme concern the modeling, simulation, optimization and data analysis of industrial or urban systems. The research work also focuses on the associated decision support tools and on the study of digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

• Industry 5.0,
• Construction 4.0 and Sustainable City,
• Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Links to the research axes of the research team involved

CESI Lineact Research Thematic: Modeling, Desing, and Architecture of CPS.

Keywords: Affordance; Computer vision; Object detection; Data augmentation; Transfer learning; Industry 5.0.

Description

In industry 5.0 marks a new stage in the evolution of the industrial world, built on three key pillars: human-centricity, sustainability, and resilience. Rather than focusing solely on productivity, it emphasizes creating systems that respect human capabilities, reduce environmental impact, and remain robust in the face of disruptions.

Re-centering the human in industrial systems therefore introduces several challenges (Nahavandi, 2019), particularly the need to design workspaces that are more ergonomic and compatible with human capabilities. The notion of tool affordance (Gibson, 1979), borrowed from the social sciences, provides a key framework for understanding how operators perceive the objects in their environment and how they interact with them. By analyzing these interactions, it becomes possible to design technologies that are more intuitive, adapted, and genuinely human-centered.

To achieve this, an important component is the accurate detection of objects, especially tools used during industrial tasks. Modern approaches rely on deep learning techniques (Trigka & Dritsas, 2025), which typically require large amounts of annotated data to reach high performance. However, collecting and manually labelling real-world industrial datasets is costly, time-consuming, and often impractical due to production schedules, safety and confidentiality constraints. This lack of real-world data represents a major limitation for training robust detection models in industrial environments.

In this context, synthetic data generated through simulation environments offers a promising direction to enrich training datasets. Our previous work on real/synthetic data ratios has shown that combining even a limited amount of real data with larger volumes of synthetic samples can effectively compensate for the scarcity of real-world observations. By producing large, diverse, and perfectly annotated images at low cost, synthetic data can therefore mitigate data limitations and significantly improve model generalization.

Domain randomization techniques applied to synthetic data, such as variations in lighting, object poses, camera viewpoints, and background conditions, can further increase the diversity and realism of synthetic images, helping models become more robust to real-world variability. Traditional data augmentation methods (flip, crop, noise, etc.) can also contribute to improved generalization. Additionally, different transfer-learning strategies can help bridge the gap between synthetic and real-world data by leveraging pretrained models and adapting them to industrial scenarios.

As a continuation of the existing work on mixed real/synthetic datasets (Ouarab et al., 2025a; Ouarab et al., 2025b), this internship will explore these advanced techniques using multiple dataset configurations (real, synthetic, and augmented). The objective is to systematically study how domain randomization, data augmentation, and transfer-learning strategies impact the performance and robustness of industrial tool-detection models, and to compare all these methods with each other in order to identify which approaches offer the best performance and generalization in real industrial settings.

Work program

● Step 1 : Literature Review (Weeks 2–3)

Review key concepts related to synthetic data generation in Unity, data augmentation, and transfer-learning strategies.

● Step 2 : Dataset Preparation and Randomization (Weeks 4–6)

Prepare and organize the different datasets: real, synthetic, augmented, and mixed.

● Step 3 : Training and Fine-Tuning Strategies (Weeks 7–14)

Train deep learning models for tool detection using different dataset configurations (real, augmented, etc.) and evaluate transfer-learning methods.

● Step 4 : Experimental Evaluation and Analysis (Weeks 15–17)

Produce quantitative metrics (mAP-50, F1-score, etc.) under different variations of datasets and transfer-learning techniques, and identify which combinations offer the best generalization to real industrial environments.

● Step 5 : Reporting and Final Presentation (Weeks 18–20)

Prepare documentation, dataset summaries, a technical report, and a final oral presentation consolidating the methodology, experiments, and key insights.

Bibliography

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6, 60.https://doi.org/10.1186/s40537-019-0197-0.

Man, M., Arabnia, H. R., & Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, 11, 40.https://doi.org/10.3390/technologies11020040.

Nahavandi, S. (2019). Industry 5.0—A Human-Centric Solution. Sustainability, 11, 4371https://doi.org/10.3390/su11164371.

Ouarab, S., Garcia, D., Ragot, N., & Dupuis, Y. (2025). Improving Image-Based Tool Detection in Industrial Workstations using Data Augmentation. In Proceedings of the 51st Annual Conference of the IEEE Industrial Electronics Society (IECON 2025).

Ouarab, S., Garcia, D., Ragot, N., & Dupuis, Y. (2025). Contribution à la caractérisation de l’affordance d’un environnement de travail industriel : une approche basée sur l’apprentissage profond combinant données réelles et synthétiques. In Conférence Nationale sur les Applications de l’Intelligence Artificielle (APIA 2025), Dijon, France. HAL Id : hal-0513249.

Ouarab, S., Boutteau, R., Romeo, K., Lecomte, C., Laignel, A., Ragot, N., & Duval, F. (2024). Industrial Object Detection: Leveraging Synthetic Data for Training Deep Learning Models. In Proceedings, Springer Nature Switzerland, pp. 200–212.

Trigka, M., & Dritsas, E. (2025). A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection. Sensors, 25, 214.https://doi.org/10.3390/s25010214.

Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin.

Required Profile

● Student in the final year of a Master’s program or engineering school, specializing in computer science, computer vision, artificial intelligence, industrial engineering, or a related field.

● Knowledge of Python programming, basic image processing, and fundamentals of machine learning.

● Experience with the Unity environment is a plus.

● Ability to work autonomously and rigorously, while also collaborating effectively within a multidisciplinary research team.

● Good written and oral communication skills, especially for scientific writing and presenting research results.