Mr. Luca Ballan Profile

Luca Ballan

Computer Vision Scientist at TNO

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Author

Area of Expertise:

Deep Learning , Computer Vision , Python , Data Processing , Decision Support , Artificial Intelligence

Publications (6)

Proceedings Article | 7 June 2024 Presentation + Paper

Improving object detector training on synthetic data by starting with a strong baseline methodology

Frank Ruis, Alma Liezenga, Friso Heslinga, Luca Ballan, Thijs Eker, Richard J. den Hollander, Martin van Leeuwen, Judith Dijk, Wyke Huizinga

Proceedings Volume 13035, 1303511 (2024) https://doi.org/10.1117/12.3013441

KEYWORDS: Data modeling, Object detection, Transformers, Education and training, Performance modeling, 3D modeling, Sensors, Visual process modeling, Linear filtering, Computer vision technology

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Proceedings Article | 7 June 2024 Presentation + Paper

EO and radar fusion for fine-grained target classification with a strong few-shot learning baseline

Luca Ballan, Jorge G. Melo, Sebastiaan van den Broek, Jan Baan, Friso Heslinga, Wyke Huizinga, Judith Dijk, Arta Dilo

Proceedings Volume 13057, 130570K (2024) https://doi.org/10.1117/12.3013756

KEYWORDS: Data modeling, Machine learning, Image classification, Visualization, Radar, Sensors, Deep learning

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Combining data from multiple sensors to improve the overall robustness and reliability of a classification system has become crucial in many applications, from military surveillance and decision support, to autonomous driving, robotics, and medical imaging. This so-called sensor fusion is especially interesting for fine-grained target classification, in which very specific sub-categories (e.g. ship types) need to be distinguished, a task that can be challenging with data from a single modality. Typical modalities are electro-optical (EO) image sensors, that can provide rich visual details of an object of interest, and radar, that can yield additional spatial information. Defined by the approach used to combine data from these sensors, several fusion techniques exist. For example, late fusion can merge class probabilities outputted by separate processing pipelines dedicated to each of the individual sensor data. In particular, deep learning (DL) has been widely leveraged for EO image analysis, but typically requires a lot of data to adapt to the nuances of a fine-grained classification task. Recent advances in DL on foundation models have shown a high potential when dealing with in-domain data scarcity, especially in combination with few-shot learning. This paper presents a framework to effectively combine EO and radar sensor data, and shows how this method outperforms stand-alone single sensor methods for fine-grained target classification. We adopt a strong few-shot image classification baseline based on foundation models, which robustly handles the lack of in-domain data and exploits rich visual features. In addition, we investigate a weighted and a Bayesian fusion approach to combine target class probabilities outputted by the image classification model and radar kinematic features. Experiments with data acquired in a measurement campaign at the port of Rotterdam show that our fusion method improves on the classification performance of individual modalities.

Proceedings Article | 17 October 2023 Presentation + Paper

Leveraging temporal context in deep learning methodology for small object detection

Friso Heslinga, Frank Ruis, Luca Ballan, Martin van Leeuwen, Beatrice Masini, Jan Erik van Woerden, Richard J. den Hollander, Martin Berndsen, Jan Baan, Judith Dijk, Wyke Huizinga

Proceedings Volume 12742, 127420H (2023) https://doi.org/10.1117/12.2675589

KEYWORDS: Object detection, Video, Deep learning, Infrared cameras, Image segmentation

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Proceedings Article | 17 October 2023 Presentation + Paper

Ship detection in thermal infrared using paired visible light images and domain adaptation via knowledge distillation

Jorge G. O. Melo, Luca Ballan, Bas S. P. van den Broek, Jan Baan, Judith Dijk, Wyke Huizinga, Arta Dilo

Proceedings Volume 12742, 127420K (2023) https://doi.org/10.1117/12.2680071

KEYWORDS: Object detection, Education and training, Performance modeling, Data modeling, Sensors, Thermography, Visible radiation, Video surveillance, Surveillance, Infrared radiation

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Proceedings Article | 17 October 2023 Presentation + Paper

The effect of simulation variety on a deep learning-based military vehicle detector

Thijs Eker, Friso Heslinga, Luca Ballan, Richard J. den Hollander, Klamer Schutte

Proceedings Volume 12742, 127420O (2023) https://doi.org/10.1117/12.2679808

KEYWORDS: 3D modeling, High dynamic range imaging, Object detection, Deep learning

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Proceedings Article | 28 October 2022 Presentation + Paper

Combatting fraud on travel, identity, and breeder documents

Henri Bouma, Jelle Van Mil, Johan-Martijn ten Hove, Raimon Pruim, Arthur van Rooijen, Alexander Kern, Luca Ballan, Ben Kromhout, Dominic Wolbers, Hans de Moel, Niels Dolstra, Gerton Bomhof, Demi de Rooij

Proceedings Volume 12275, 122750B (2022) https://doi.org/10.1117/12.2637416

KEYWORDS: Artificial intelligence, Optical character recognition, Printing, Sensors, Data modeling, Binary data, Inkjet technology, New and emerging technologies, Performance modeling, Laser engraving, Visualization

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Showing 5 of 6 publications

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