Detecting Threats in X-ray Scans with YOLOv5 and YOLOv6

Improving public safety is essential for both citizens and governments. The monitoring and baggage inspections that are part of the current security protocols are crucial. By employing object detection for automated baggage security, this project enhances existing systems.Threats are successfully identified during X-ray scanning with the use of deep learning techniques, particularly Transfer Learning with YOLOv5 and YOLOv6. A strong dataset is produced for model training with the use of GANs and data augmentation.This system helps stop dangerous objects from entering public spaces by concentrating on detecting items like knives and guns. This lowers risks and promotes a safer atmosphere. This research emphasises how crucial cutting-edge technology is to preventative security measures.