论文标题
基于任务一致检测变压器的被动毫米波安全成像的隐藏对象检测
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer
论文作者
论文摘要
被动毫米波（PMMW）是人类安全筛查的重要潜在技术。几个流行的对象检测网络已用于PMMW图像。但是，受PMMW图像的低分辨率和高噪声的限制，PMMW基于深度学习的隐藏对象检测通常会遭受低精度和低分类置信度的影响。为了解决上述问题，本文提出了一个任务一致的检测变压器网络，名为PMMW-Detr。在第一阶段，脱氧的粗到精细变压器（DCFT）骨干旨在提取不同尺度的长距离特征。在第二阶段，我们提出了查询选择模块，以作为先验知识将学习的空间特征引入网络，从而增强了网络的语义感知能力。在第三阶段，旨在提高分类性能，我们执行一个任务一致的双头块，以使分类和回归任务分离。基于我们自我开发的PMMW安全筛查数据集，实验结果包括与最先进的方法（SOTA）方法进行比较和消融研究表明，PMMW-DETR比以前的工作获得了更高的准确性和分类置信度，并且表现出对低质量PMMW图像的鲁棒性。
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.
