Abstract
Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger safety compared to X ray. Despite of recent advance in the last decades, the high cost of requisite large-scale antenna array hinders widespread adoption of MMW imaging in practice. To tackle this challenge, we report a large-scale single-shot MMW imaging framework using sparse antenna array, achieving low-cost but high-fidelity security inspection under an interpretable learning scheme. We first collected extensive full-sampled MMW echoes to study the statistical ranking of each element in the large-scale array. These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, which realizes robust and accurate image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at the same sample sampling ratio. The performance of the reported technique presents higher than 50% superiority over the existing MMW imaging schemes on various metrics including precision, recall, and mAP50. With such strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging, and could advocate its further practical applications.
Abstract (translated)
毫米波成像正在成为安全检查的有前途的技术。它在影像分辨率、穿透力和人类安全之间实现了微妙的平衡，相较于低频微波、相较于可见光，相较于X射线，它的分辨率更高。尽管过去几年有所进步，但所需的大规模天线阵列高昂的成本阻碍了它在实际应用中的广泛采用。为了解决这个问题，我们报告了一种稀疏天线阵列的大型单光子毫米波成像框架，实现了低成本但高保真的安全检查，通过可解释的学习算法实现。我们首先收集了广泛的全样本毫米波反射来研究大规模阵列中每个元素的统计分析排名。这些元素是根据排名进行采样，构建实验最优的稀疏采样策略，降低了天线阵列的成本高达一个数量级。此外，我们推导了一种未训练的可解释学习算法，从稀疏采样反射中实现稳健的且准确的图像重构。最后，我们开发了一种新的自动目标检测神经网络，并使用10%的稀疏数组实现了成功的目标检测，而其他 contemporary 方法在相同的样本采样比例上失败了。报告方法的性能在精度、召回率和mAP50等指标上表现出超过50%的优越性。凭借这种强大的检测能力和数量级的降低成本能力，我们预计，这种方法可以为大规模单光子毫米波成像提供实用的方法和建议其进一步实际应用。
URL
https://arxiv.org/abs/2305.15750
