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.

中文翻译：

---

用于毫米波图像中隐藏物体检测的多源聚合变压器


主动式毫米波扫描仪已广泛应用于安检、反恐领域检测隐藏在衣服下的物体。然而，主动毫米波（AMMW）图像始终存在信噪比低、运动模糊和小尺寸物体等问题，使得高效、准确地检测隐藏物体具有挑战性。扫描仪通常会同时捕获人体周围不同视角的一系列图像，而现有算法仅利用单个图像，而没有考虑图像之间的关系。在本文中，我们设计了一个具有两种不同注意机制的多源聚合变压器（MATR），用于对图像内的空间相关性和图像之间的上下文交互进行建模。具体来说，引入自注意力模块来编码每个图像中区域建议之间的局部关系，同时建立交叉注意力机制来专注于对不同图像之间的互相关性进行建模。此外，为了处理小目标的问题并抑制 AMMW 图像中的噪声，我们提出了选择性上下文模块（SCM）。它设计了一种动态选择机制，以增强具有空间细节的高分辨率特征，并使其更能与噪声背景区分开来。在两个 AMMW 图像数据集上的实验表明，与之前的最先进技术相比，所提出的方法取得了显着的改进，并将有利于实践中的隐藏物体检测。