Recent Research Study on AI-based Crime Scene Evidence Detection

• Main Authors: Murugan, Thangavel, Aldahmani, Fotoon Khaleifah Abdulla Mosabbas, Almehrzi, Ghalya Salem Mohamed Alshuraiqi, Alsereidi, Eiman Mohamed Salem Mohamed, Aldahmani, Aaesha Abdulla Khalfan Ali, Alahbabi, Eiman Mubarak Masoud
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Artificial intelligence; bloodstains; crime scene; evidence; footprint; Forensics; Machine learning; Machine learning algorithms; Privacy; Reliability; Research and development; Reviews; weapon; Weapons
• ISSN: 2771-7402
• DOI: 10.1109/CommNet63022.2024.10793266

Traditional crime scene evidence detection methods are time-consuming and prone to human error. In recent years, artificial intelligence (\mathbf{A I}) has revolutionized crime scene investigation by improving the detection and analysis of evidence. This review paper examines recent studies on AIbased evidence detection from key crime scenes, such as weapons, footprints, and bloodstains. Researchers have used advanced AI algorithms to develop innovative techniques for accurately identifying and documenting these crucial pieces of evidence, which helps law enforcement agencies solve criminal cases more efficiently. The paper discusses various AI models and technologies for detecting weapons, footprints, and bloodstains at crime scenes, highlighting their strengths, limitations, and potential for future advancements. Overall, the paper emphasizes the importance of AI in enhancing crime scene investigation processes and advocates for further research and development in this rapidly evolving field to maximize its potential impact on criminal justice systems worldwide.