AI Crime Scene Evidence Analyzer

Modern crime scene investigation involves substantial evidence collection and analysis, including CCTV footage and images requiring manual and automated inspection. The proposed AI Crime Scene Evidence Analyzer is a full-stack forensic intelligence web platform with an integrated suite of six AI modules, including YOLOv8 weapon and evidence detection trained on a domain-specific weapons dataset, frame-level surveillance video analysis with UCF-Crime anomaly timestamps, Scene Change Detection using image alignment (ORB) and RANSAC homography estimation, followed by localization of altered regions using SSIM dissimilarity, and a novel multi-factor weighted risk score generation with 0-100 case-level risk indices, MongoDB-based audit trail generation of evidence custody steps, and a case-aware forensic LLM chatbot named ARIA with Claude API context injection. Additionally, the platform automatically generates a PDF forensic report for the submitted case within five seconds. Evaluation demonstrates that the weapon detection module achieves mAP@0.5 > 75% on the Kaggle Weapons Detection dataset and the Scene Change Detection module reliably localizes altered regions in aligned before-after crime scene image pairs with dissimilarity < 0.75 global SSIM. The contribution of this work is a forensic intelligence platform leveraging six individually published forensic computer vision techniques alongside a novel risk score engine, evidence comparison pipeline, and chain-of-custody logging, implemented in React.js, Python Flask, Node.js, and MongoDB, and evaluated on UCF-Crime and Kaggle Weapons Detection benchmarks.