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Deep learning in forensic shotgun pattern interpretation - A proof-of-concept study.

Petteri Oura1, Alina Junno2, Juho-Antti Junno2

  • 1Department of Forensic Medicine, University of Helsinki, Helsinki, Finland; Forensic Medicine Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.

Legal Medicine (Tokyo, Japan)
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Summary

Artificial intelligence shows promise in forensic shotgun pattern analysis. Deep learning models accurately classify shooting distances from patterns, aiding forensic investigations.

Keywords:
Deep learningForensic medicineGunshot interpretationShotgun pattern

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Area of Science:

  • Forensic Science
  • Computer Science
  • Ballistics

Background:

  • Shotgun pattern interpretation is crucial in forensic investigations.
  • Shooting distance significantly influences shotgun patterns.
  • The application of artificial intelligence (AI) in this field is largely unexplored.

Purpose of the Study:

  • To investigate the potential of neural network architectures for classifying shotgun pattern images based on shooting distance.
  • To assess the feasibility of AI-driven tools for estimating shooting distances in forensic contexts.

Main Methods:

  • A dataset of 106 shotgun pattern images from two distinct distances (10m and 17.5m) was utilized.
  • Deep learning algorithms were trained, validated, and tested using the AIDeveloper software.
  • A TinyResNet-based algorithm was implemented for image classification.

Main Results:

  • The TinyResNet algorithm achieved a high testing accuracy of 94% in classifying shooting distances.
  • The algorithm correctly identified all patterns from 10m and misclassified only one pattern from 17.5m.
  • Preliminary results indicate strong potential for AI in forensic shotgun pattern analysis.

Conclusions:

  • AI, specifically deep learning, demonstrates significant potential as a tool for forensic investigators.
  • Algorithms can be developed to assist in accurately estimating shooting distances from shotgun patterns.
  • Further research with larger, more complex datasets is necessary to develop robust and applicable forensic tools.