Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we design and implement SiWa as a low-cost and portable system for wall inspections. Built upon a customized IR-UWB radar, SiWa scans a wall as a user swipes its probe along the wall surface; it then analyzes the reflected signals to synthesize an image and also to identify the material status. Although conventional schemes exist to handle these problems individually, they require troublesome calibrations that largely prevent them from practical adoptions. To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data. With an ingenious construction and innovative training, the deep learning modules perform structural imaging and the subsequent analysis on material status, without the need for parameter tuning and calibrations. We build SiWa as a prototype and evaluate its performance via extensive experiments and field studies; results confirm that SiWa accurately maps in-wall structures, identifies their materials, and detects possible failures, suggesting a promising solution for diagnosing building health with lower effort and cost.
翻译:在本文中,我们设计并实施了SiWa系统,作为低成本和便携式的墙体检查系统。在定制的IR-UWB雷达上,SiWa将墙壁扫描成一个用户在墙表面的探测器;然后,它分析反射信号,合成图像,并查明物质状况。虽然现有遥感设备似乎无法提供充分的能力,既绘制墙内结构图,又查明其状况(例如渗出和腐蚀)。在本文中,我们设计和实施SiWa系统,作为低成本和便携式的墙体检查系统。在定制的IR-UWB雷达上,SiWa系统将墙壁扫描成一个用户,在墙面上对其探测器进行扫描;然后,它分析反射信号,以合成一个图像,同时确定物质状况。尽管存在单独处理这些问题的常规计划,但它们需要困难的校准能力。为此,我们为SiWaa系统配备了一条深层次的学习管道,以分析丰富的传感器数据。通过巧妙的建筑和创新的培训,深学习模块进行结构成像,随后对材料状况进行了分析,而不需要参数调整和校准。我们通过广泛的实验和实地研究来将SiWaWa-lag 检测出可能测量出一个低的地图。