Snehalata U. Shenoy, Varad Nagar and Akhith
Crime Scene Reconstruction (CSR) is a crucial component of criminal investigations and calls for a careful examination of the available data to pinpoint the chain of events that led to the crime. There has been an increase in the demand for using AI-based techniques for crime scene reconstruction since the advent of Artificial Intelligence (AI). We compared the developments, drawbacks, and potential applications of` AI-based crime scene reconstruction systems. We discovered that machine learning models, computer vision models, natural language processing models, deep learning models, and graph analytics models have all demonstrated considerable gains in crime scene reconstruction. However, there are also limitations to the use of AI-based techniques, including the need for large amounts of high-quality data, potential bias in the data or algorithms, and the interpretability of the results. To overcome these limitations, future research should focus on developing more robust and transparent AIbased models that integrate multiple techniques and provide clear explanations of the results. Over the past few decades, 3D modeling has been the subject of extensive research. Overall, AI-based techniques have the potential to revolutionize crime scene reconstruction, but further research is needed to optimize their use in criminal investigations. This comparative review addresses how AI is being used in forensic science now and in the future.
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