Detection of Prohibited Items in X-ray Images using Deep Learning

Automated baggage screening is crucial for safety and surveillance as it ensures precise and effective scrutiny of luggage at places like airports, and railway stations. This paper presents a deep learning-based approach to identify prohibited items such as HandGun, Knife, RazorBlade, and Shuriken within luggage by utilizing YOLOv8 model from Xray images. YOLOv8 one of the most advanced object detection algorithms. By integrating cutting-edge image processing techniques with machine learning techniques, the suggested system automates the scanning process and instantly identifies potential threats of these contraband items. The proposed model utilizes YOLOv8 object detection algorithm and works on X-ray images taken from GDXray database. The F1 score of 0.98 was achieved by the proposed model on test dataset. This automated approach is an essential component of contemporary security infrastructure because it strengthens security measures and increases operational efficiency.