Data · dataset · 2026
Bullet impact images dataset on different materials for forensic ballistics applications
Listed in e-cienciaDatos
Description of the project.
Description
This project aims to advance the field of forensic ballistics by creating a large, controlled dataset of bullet impact images to train deep learning models for automatic trajectory reconstruction. The dataset was generated in collaboration with the Criminalistics Service of the Spanish Civil Guard to ensure real-world relevance and accuracy.
Without this collaboration, this dataset would not have been possible. Description of the dataset. The dataset consists of 1,800 high-resolution images (in PNG format) of bullet impact holes, captured under controlled laboratory conditions.
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It covers six drift angles (15°, 30°, 45°, 60°, 75°, 90°) on two common building materials, frequently encountered in forensic investigations: particleboard and drywall. Each angle category contains 300 images, equally split between the two materials (150 images for particleboard and 150 images for drywall).
Links
Where it is published
- Dataverse dataset page edatos.consorciomadrono.es/dataset.xhtml?persistentId=doi%3A10.21950%2FPVWAHT ↗
landing page · from edatos consorciomadrono es
- DOI doi.org/10.21950/pvwaht ↗
DOI / persistent id · from edatos consorciomadrono es
Catalogue records · 1
- Dataverse API edatos.consorciomadrono.es/api/datasets/:persistentId/?persistentId=doi%3A10.21950%2FPVWA… ↗
metadata API · from edatos consorciomadrono es
Topics
- Stated by source
- Física
- From keywords
- Computer Science & AI · Deep learning
- Inferred from text
- Image 65%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| e-cienciaDatos | doi:10.21950/PVWAHT | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:461103 | mapping · edatos consorciomadrono es | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].dataverse_subject:f-sica | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · edatos consorciomadrono es | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| description | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /description |
| publication_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| title | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /name |
| updated_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| version_label | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 |