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Millimeter-wave (MMW) imaging is emerging as a promising technique for safesecurity inspection. It achieves a delicate balance between imaging resolution,penetrability and human safety, resulting in higher resolution compared tolow-frequency microwave, stronger penetrability compared to visible light, andstronger safety compared to X ray. Despite of recent advance in the lastdecades, the high cost of requisite large-scale antenna array hinderswidespread adoption of MMW imaging in practice. To tackle this challenge, wereport a large-scale single-shot MMW imaging framework using sparse antennaarray, achieving low-cost but high-fidelity security inspection under aninterpretable learning scheme. We first collected extensive full-sampled MMWechoes to study the statistical ranking of each element in the large-scalearray. These elements are then sampled based on the ranking, building theexperimentally optimal sparse sampling strategy that reduces the cost ofantenna array by up to one order of magnitude. Additionally, we derived anuntrained interpretable learning scheme, which realizes robust and accurateimage reconstruction from sparsely sampled echoes. Last, we developed a neuralnetwork for automatic object detection, and experimentally demonstratedsuccessful detection of concealed centimeter-sized targets using 10% sparsearray, whereas all the other contemporary approaches failed at the same samplesampling ratio. The performance of the reported technique presents higher than50% superiority over the existing MMW imaging schemes on various metricsincluding precision, recall, and mAP50. With such strong detection ability andorder-of-magnitude cost reduction, we anticipate that this technique provides apractical way for large-scale single-shot MMW imaging, and could advocate itsfurther practical applications.

Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection