Multi-Source Aggregation Transformer for Concealed Object Detection in Millimeter-Wave Images

The active millimeter wave scanner has been widely used for detecting objects concealed underneath a person’s clothing in the field of security inspection and anti-terrorism. However, the active millimeter wave (AMMW) images always suffer from low signal-noise ratio, motion blur, and small size objects, making it challenging to detect concealed objects efficiently and accurately. The scanner usually captures a sequence of images in different views around a human body at once, while the existing algorithms only utilize the single image without considering the relationships among images. In this paper, we design a multi-source aggregation transformer (MATR) with two different attention mechanisms to model spatial correlations within an image and contextual interactions across images. Specifically, a self-attention module is introduced to encode local relationships between the region proposals in each image, while a cross-attention mechanism is built to focus on modeling the cross-correlations between different images. Besides, to handle the problem of small objects in size and suppress the noise in AMMW images, we present a selective context module (SCM). It designs a dynamic selection mechanism to enhance the high-resolution feature with spatial details and make it more distinguishable from the noisy background. Experiments on two AMMW image datasets demonstrate that the proposed methods lead to a remarkable improvement compared to previous state-of-the-art and will benefit the concealed object detection in practice.