FDTNet: Enhancing frequency-aware representation for prohibited object detection from X-ray images via dual-stream transformers

Abstract

With the extensive application of object detection in intelligent security, the demand for detecting prohibited items in X-ray images has become increasingly stringent. Unlike natural images, X-ray images present unique challenges such as complex backgrounds and mutual occlusion between prohibited and normal items. Consequently, applying traditional detection methods to X-ray images remains a significant challenge. To tackle these challenges, we have developed a unique frequency-aware dual-stream transformers (FDTNet) that is specifically designed for analyzing X-ray images. The FDTNet consists of two streams: one handles the original image, while the other deals with an image that has been enhanced with frequency domain features. In order to achieve precise detection of prohibited items, we introduce a frequency-aware module (FAM) that enhances the representation of prohibited items by utilizing information from the frequency domain. This FAM can be easily integrated into other backbones or detectors as it is a plug-and-play module. Additionally, to enhance the fusion of feature maps from both streams, we utilize a global and channel attention module (GCA) that aggregates texture representations for spatial feature streams. Our evaluation of the proposed FDTNet on the OPIXray datasets and PIDray datasets demonstrates that our detection mAP achieves 88.02 and 68.2, respectively. Extensive experiments conducted on publicly available datasets provide substantial evidence that our proposed network significantly improves the detection of prohibited items compared to state-of-the-art methods.

Ziming Zhu 朱梓铭

PhD. Loyal listener of Jay Chou.

A doctoral student of this laboratory, research interests include Artificial Intelligence, Autonomous Driving Environment Perception, 3D Object Detection and Semantic Occupancy Prediction.

Yu Zhu 朱煜

Professor. Experts in artificial intelligence and computer vision. Lab leader.

Leader of this laboratory, research interests include Artificial Intelligence, Computer Vision, Industrial controls, Digital Image and Video Processing, Machine learning, Deep Learning and Applications.

Haoran Wang 王浩然

Master.

A Master student of this laboratory, research interests include Computer Vision, Few-shot Object Detection and X-ray Images Processing.