Improved threat item detection in baggage X-ray imagery through image projection

Abstract

Detecting prohibited items using X-ray machines is essential for maintaining security in various transportation hubs. By integrating intelligent algorithms with X-ray imaging systems, we can significantly improve the efficacy of security screening procedures. It explores a method to form synthetically composited imagery and formulates a technique that handles each pixel of an image individually, depending on material properties and surrounding pixel intensities, to blend pixels from source to target realistically. A target is chosen based on the threat size to be superimposed, and the threat is superimposed with greater probability on more densely cluttered regions of the target. Evaluating object detection methods (Faster-RCNN, YOLO-F) on real and synthesized datasets, showed improvements in mean average precision from 70.6% to 71.4% for GDXray dataset, 67.9% to 70.6% for PIDray dataset, and from 62% to 65.3% in case of In-house dataset. Additionally, the model performs better on those areas where it previously struggled.