Algorithms, Vol. 18, Pages 707: Comprehensive Forensic Tool for Crime Scene and Traffic Accident 3D Reconstruction

Algorithms doi: 10.3390/a18110707

Authors:
Alejandra Ospina-Bohórquez
Esteban Ruiz de Oña
Roy Yali
Emmanouil Patsiouras
Katerina Margariti
Diego González-Aguilera

This article presents a comprehensive forensic tool for crime scene and traffic accident investigations, integrating advanced 3D reconstruction and semantic and dynamic analyses; the tool facilitates the accurate documentation and preservation of crime scenes through photogrammetric techniques, producing detailed 3D models based on images or video captured under specified protocols. The system includes modules for semantic analysis, enabling object detection and classification in 3D point clouds and 2D images. By employing machine learning methods such as the Random Forest model for point cloud classification and the YOLOv8 architecture for object detection, the tool enhances the accuracy and reliability of forensic analysis. Furthermore, a dynamic analysis module supports ballistic trajectory calculations for crime scene investigations and the vehicle impact speed estimation using the Equivalent Barrier Speed (EBS) model for traffic accidents. These capabilities are integrated into a single, user-friendly platform offering significant improvements over existing forensic tools, which often focus on singular tasks and require expertise. This tool provides a robust, accessible solution for law enforcement agencies, enabling more efficient and precise forensic investigations across different scenarios.

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Alejandra Ospina-Bohórquez www.mdpi.com