Weapon Detection in X-ray Image of Baggages

Abstract

Due to the daily commutes of people by MRT trains, following the shooting incident at Paragon, the MRT system has implemented bag searches before entering the stations to look for concealed or hidden weapons. These searches are conducted manually, which sometimes may not be thorough enough and can take a significant amount of time. Especially during peak hours when many people are using the MRT, it is possible for some individuals to pass through the station without being searched. Such actions can render the security measures ineffective. Therefore, this paper proposes a study to find ways to address these issues. From the study and comparison of object detection processes for risky items, such as sharp objects or guns, in X-ray images of luggage, it was found that models such as CNN, RCNN, Detectron, RetinaNet, and Yolo achieved excellent results in object detection and recognition. The organizers plan to apply object detection techniques and improve the existing methods for detecting objects in X-ray images to be more efficient and accurate, capable of identifying a variety of risky items.