Reviewing the Impact of YOLO on Forensic Evidence Analysis in Crime Scene Investigations

As society grapples with an escalating crime rate, the urgency to resolve pending cases and streamline the evidence collection and analysis process has never been more critical.Amidst this backdrop, the advent of computer vision systems and the proliferation of data have catalysed a paradigm shift in object detection models/algorithms.This paper delves into the transformative potential of the You Look Only Once (YOLO) model, a state-of-the-art Convolutional Neural Network (CNN) architecture renowned for its swift detection capabilities, even on low-end devices.The paper commences with an introduction and background, subsequently unfolding the innovative and descriptive approach of the YOLO model towards object detection.The spotlight is on the model's application in Forensic Evidence Detection and Analysis, underscoring its instrumental role in revolutionizing crime scene investigations.Through this exploration, the paper aims to contribute to the ongoing discourse on leveraging advanced technologies to combat crime effectively and efficiently.