Crime Scene Object Detection for Forensic Investigations Using Faster R-CNN and YOLOv5 Models

More and more complex and numerous are the forensic findings required while investigating the crime scene, it is important to address the need for technological enhancements for the crime scene analysis. This objective detection models are important in helping alleviate the numerous errors that are involved when a human is tasked with the responsibility of identifying and categorizing objects within a crime scene, as it faster the process. In this research, Faster R-CNN and YOLOv5 deep learning models are used to detect the objects in crime scenes. Faster R-CNN which offers accuracy in object detection is used while YOLOv5 a real-time object detection framework improves the speed of detection. The models were trained and tested on a dataset which contains images of crime scene and the related objects include weapon, evidence mark and personal effect. The efficiency of the developed models was assessed by comparing the results on the mAP, detection speed, and false positive ratios. As such, the experimental results show that YOLOv5 is faster than Faster R-CNN for real-time applications, whereas Faster R-CNN is more accurate for higher detection rate-based applications. These models are complementary in their operation, the study suggests an integration of these models to improve efficiency for the forensic process. The study shows that by incorporating data object detection into contemporary forensic investigation processes powered by AI, the forensic science will significantly improve its ability to analyze evidence and solve crimes.