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SiamYOLOv8: a rapid conditional detection framework for one-shot object detection

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Abstract

Deep learning networks typically require vast amounts of labeled data for effective training. However, recent research has introduced a challenging task called One-Shot Object Detection, which addresses scenarios where certain classes are novel and unseen during training and represented by only a single labeled example. In this paper, we propose a novel One-Shot Object Detection model applicable to Conditional Detection without over-training on novel classes. Our approach leverages the strengths of YOLOv8 (You Only Look Once v8), a popular real-time object detector. Specifically, we incorporate a Siamese network and a matching module to enhance One-Shot Object Detection capabilities. Our proposed model, SiamYOLOv8, enables exploration of new applications without being limited by its training data. To evaluate the performance, we introduce a novel methodology for using the Retail Product Checkout (RPC) dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/RPC)”, and extend our evaluation using the Grozi-3.2k dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/GROZI-3.2k)”. In such contexts, new products often lack sufficient data for continuous Deep Learning methods, making individual case identification difficult. Our model outperforms SOTA models, achieving a significant performance improvement of 20.33% increase in Average Precision (+12.41 AP) on the Grozi-3.2k dataset and 25.68% increase (+17.37 AP) on the RPC dataset.

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The data supporting this study will be available at the same time as the paper.

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Acknowledgements

The authors would like to thank the Generix Group Company for financial support and the L@bISEN laboratory of the ISEN Yncrea Ouest for scientific support.

Funding

This work is funded by our partner Generix (world leader in the SaaS industry), as part of their project to add AI solutions to their existing warehouse management product.

Author information

Authors and Affiliations

  1. L@bISEN, LSL Team, 20 Rue Cuirassé Bretagne, Brest, 29200, France

    Matthieu Desmarescaux, Wissam Kaddah & Ayman Alfalou

  2. Generix Group, 8 Rue Simone IFF, Paris, 75012, France

    Matthieu Desmarescaux & Isabelle Badoc

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  1. Matthieu Desmarescaux
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  2. Wissam Kaddah
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  3. Ayman Alfalou
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  4. Isabelle Badoc
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All authors contributed significantly to the work, in terms of content, analysis, writing, and revision of the manuscript.

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Correspondence to Matthieu Desmarescaux.

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Desmarescaux, M., Kaddah, W., Alfalou, A. et al. SiamYOLOv8: a rapid conditional detection framework for one-shot object detection. Appl Intell 55, 609 (2025). https://doi.org/10.1007/s10489-025-06513-2

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