用可编程无线环境+机器学习实现无损检测，视觉还原度达99.5%。
A novel RF-enabled Non-Destructive Inspection Method through Machine Learning and Programmable Wireless Environments
- 通过可编程无线环境调控射频波前，构建波形与工件图像的对应关系。
- 基于生成对抗网络还原图像，视觉相似度（SSIM）达99.5%。
- 适用于密闭、危险等难触达场景，适合智能工厂质量检测应用。
当前工业无损检测（NDI）需在遮挡、危险或难以接近的环境中工作，但依赖光学相机的视觉检测手段性能有限。为此，本文提出一种新型无损检测方法，利用可编程无线环境（PWE）重构射频（RF）波传播，将其作为可控的检测实体。通过射频波前编码技术，从指定数据库中提取工件图像信息，建立波前与目标工业资产间的关联数据集。随后，采用生成对抗网络（GAN）训练模型，生成与数据库条目高度匹配的视觉输出。实验结果表明，该方法在视觉输出上实现了99.5%的结构相似性（SSIM）匹配度，为下一代工业质量控制流程提供了可行路径。
原文摘要 · Abstract (English)
Contemporary industrial Non-Destructive Inspection (NDI) methods require sensing capabilities that operate in occluded, hazardous, or access restricted environments. Yet, the current visual inspection based on optical cameras offers limited quality of service to that respect. In that sense, novel methods for workpiece inspection, suitable, for smart manufacturing are needed. Programmable Wireless Environments (PWE) could help towards that direction, by redefining the wireless Radio Frequency (RF) wave propagation as a controllable inspector entity. In this work, we propose a novel approach to Non-Destructive Inspection, leveraging an RF sensing pipeline based on RF wavefront encoding for retrieving workpiece-image entries from a designated database. This approach combines PWE-enabled RF wave manipulation with machine learning (ML) tools trained to produce visual outputs for quality inspection. Specifically, we establish correlation relationships between RF wavefronts and target industrial assets, hence yielding a dataset which links wavefronts to their corresponding images in a structured manner. Subsequently, a Generative Adversarial Network (GAN) derives visual representations closely matching the database entries. Our results indicate that the proposed method achieves an SSIM 99.5% matching score in visual outputs, paving the way for next-generation quality control workflows in industry.
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