Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block to decouple the classification and regression tasks. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.

翻译：被动毫米波（PMMW）是人体安检领域极具潜力的关键技术。现有多种主流目标检测网络已被应用于PMMW图像分析。然而，受限于PMMW图像的低分辨率与高噪声特性，基于深度学习的PMMW隐匿目标检测常面临检测精度低、分类置信度差等问题。针对上述挑战，本文提出一种任务对齐检测Transformer网络——PMMW-DETR。首先，设计多尺度去噪粗到细Transformer（DCFT）主干网络，用于提取不同尺度下的长程与短程特征；其次，提出查询选择模块，将学习的空间特征作为先验知识引入网络，增强模型的语义感知能力；再次，为提升分类性能，构建任务对齐双头（Task-Aligned Dual-Head）模块解耦分类与回归任务。基于自主开发的PMMW安检数据集，与现有最先进（SOTA）方法的对比实验及消融研究表明，PMMW-DETR在检测精度与分类置信度上均优于已有方法，且对低质量PMMW图像表现出良好的鲁棒性。