A Comparative Analysis of Weapons Detection Using Various Deep Learning Techniques

In crime scene analysis, object detection can be used to identify and track objects and people, which can help investigators to recreate the events and understand the sequence of actions that took place during a crime. The images are manually annotated, which is a process where a human expert goes through each image and marks the location and class of objects within the image. This process is important for training object detection algorithms as it provides the necessary ground truth data for the algorithm to learn from. In the case of crime scene analysis, high accuracy is crucial, as it can help ensure that no evidence is missed, but speed is also important, as time is often a critical factor in investigations. The proposed system in this study attempts to balance this trade-off by using algorithms like YOLOv5, SSD, and RCNN, which are known for their real-time performance while maintaining a high accuracy level.