AI + ML for Forensic Scene Reconstruction: A Simulation-Based Study of Blood Spatter, Fingerprint, and Visual Evidence Mapping

Abstract The investigation of crime scenes plays a crucial role in the criminal justice system. Traditional forensic approaches rely heavily on manual inspection, documentation, and hypothesisbuilding based on physical evidence collected from the scene. These methods, although effective to a degree, are time-consuming, subjective, and susceptible to human error. As technological advancements continue to reshape various scientific domains, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into forensic science has the potential to revolutionize the way crime scenes are reconstructed, analyzed , and interpreted. This research proposes the development of a comprehensive AI and ML-based framework to facilitate forensic scene reconstruction using 3D modeling derived from 2D images. The system aims to automate the identification and tagging of forensic evidence such as blood spatter, fingerprints, and various visual clues using advanced computer vision and deep learning models. Through the application of 3D reconstruction techniques like photogrammetry and Structure-from-Motion (SfM), investigators can create accurate, virtual models of crime scenes that can be further enhanced by integrating ML algorithms capable of evidence detection and classification. The study will utilize publicly available forensic datasets along with synthetically generated data to train and validate the AI models. A userfriendly interface will also be developed to enable forensic professionals and law enforcement to interact with and analyze the reconstructed scenes. The results of this research are expected to improve the efficiency of forensic investigations, reduce the scope for human error, and enable immersive courtroom presentations. Key words: AI, ML, Reconstruction, scomputer vision, photogrammetry, Forensic, Crime, fingerprint, BSA