eCHFD : extended Ceasefire Hierarchical Firearm Dataset
In recent years, the demand for large, diverse, and well-annotated firearm datasets has grown, particularly for advancing firearm recognition systems. However, existing datasets often lack sufficient variety in viewpoints, configurations, and model versions. To address these challenges, this paper introduces an extented version of the Ceasefire Hierarchical Firearm Dataset (eCHFD), a large image dataset of firearms consisting of over 93,000 images in 505 classes. It was constructed from more than 240 videos filmed at the Toulouse Forensics Laboratory (France) and further enriched with images from the existing CHFD dataset and additional downloaded images. Automated post-processing techniques were employed to efficiently build this large-scale dataset. In addition, a hierarchical annotation scheme validated by forensics experts was used to classify firearms at multiple levels, enabling its utility for fine-grained classification tasks. Experimental results was conducted using models such as HERBS and FGVC-PIM to demonstrate the effectiveness of the dataset.
Authors
- Sylvie Chambon (ORCID: https://orcid.org/0000-0001-8104-1637)
- Laurence Abadie
- Loubna Lechelek (ORCID: https://orcid.org/0000-0003-1751-7762)
- Thierry Hartmann (ORCID: https://orcid.org/0009-0000-1973-9866)
- Christian Brocard (ORCID: https://orcid.org/0009-0000-9763-9393)
- Saddam Abdulwahab (ORCID: https://orcid.org/0000-0003-0902-7245)
- Alain Crouzil (ORCID: https://orcid.org/0000-0001-7040-2978)
- Marjorie Le Bras
- Grégory Jalabert
- Bruno Sera
- Charles-Edouard Coquillard
Institutions
- Université Toulouse III - Paul Sabatier (FR)
- Université Toulouse-I-Capitole (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Institut National de Police Scientifique (FR)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
- Universitat Rovira i Virgili (ES)
Publication Details
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1145/3712676.3718333
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00