Feature-Aware Prohibited Items Detection for X-Ray Images

Abstract

Prohibited items detection is a challenging problem in security inspection. Since the commonly used manual security screening is a subjective, inefficient and costly process, some researchers have used existing models designed for natural images and their modifications to replace it. However, these methods do not take full advantage of the unique properties of X-ray images to solve its clutter and occlusion problem in detection. In this paper, we propose a feature-aware prohibited items detection (FAPID) method for X-ray images to detect normal and overlapped heavily prohibited items. The overall framework consists of shape-guided feature enhancement module (SGFE) and prohibited item aware module (PIA). The SGFE module enhances the items’ structural integrity in the learned features by utilizing the strong shapes in X-ray images without losing texture features. And the PIA module learns discriminative fine contour and texture of prohibited items without the influence of overlapping noise by using frequency information and the feedback-like mechanism. Extensive experiments conducted on HiXray and OPIXray datasets verify the effectiveness of the proposed method compared to other SOTA methods.