A Machine Learning Based Forensic System for Road Accident Detection and Reporting

The escalating frequency of road traffic accidents necessitates advanced investigative tools to ensure judicial clarity and rapid insurance processing. Traditional forensic methods often rely on manual video surveillance review and subjective eyewitness accounts, leading to significant delays and potential investigative bias. This research introduces Oracle Forensic v2.0, a machine learning-based forensic system designed to automate the detection and reporting of road accident scenes through an intelligent, end-to-end digital portal. Unlike existing radio-frequency (RF) or sensor-based detection systems that are often sensitive to hardware malfunctions or environmental interference, this vision-based approach leverages high-definition visual data to achieve precise reconstruction. The proposed system utilizes a Flask-based backend coupled with OpenCV for heuristic temporal keyframe extraction, which effectively reduces raw video data into a concise sequence of critical events while maintaining investigative integrity. At the core of the architecture, these extracted frames are processed by the Gemini 3 Flash multimodal AI engine to perform high-level cognitive reasoning, fault allocation, and timeline reconstruction. The model is guided by specialized Forensic Prompt Engineering to identify traffic violations, such as lane departures or signal noncompliance, which are essential for determining legal liability. For data persistence, all structured JSON findings are securely committed to a MongoDB collection, ensuring a permanent and searchable record of every case. The final output is a professionally compiled PDF dossier generated via the ReportLab toolkit, providing law enforcement and insurance agencies with standardized, objective, and courtroom-ready evidence. Experimental results demonstrate that the system drastically reduces investigation time from hours to seconds while maintaining high analytical accuracy across diverse lighting and weather conditions. This work establishes a scalable, zero-hardware solution that strengthens digital forensics in modern smart-city environments.