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Being able to see into walls is crucial for diagnostics of building health;it enables inspections of wall structure without undermining the structuralintegrity. However, existing sensing devices do not seem to offer a fullcapability in mapping the in-wall structure while identifying their status(e.g., seepage and corrosion). In this paper, we design and implement SiWa as alow-cost and portable system for wall inspections. Built upon a customizedIR-UWB radar, SiWa scans a wall as a user swipes its probe along the wallsurface; it then analyzes the reflected signals to synthesize an image and alsoto identify the material status. Although conventional schemes exist to handlethese problems individually, they require troublesome calibrations that largelyprevent them from practical adoptions. To this end, we equip SiWa with a deeplearning pipeline to parse the rich sensory data. With an ingeniousconstruction and innovative training, the deep learning modules performstructural imaging and the subsequent analysis on material status, without theneed for parameter tuning and calibrations. We build SiWa as a prototype andevaluate its performance via extensive experiments and field studies; resultsconfirm that SiWa accurately maps in-wall structures, identifies theirmaterials, and detects possible failures, suggesting a promising solution fordiagnosing building health with lower effort and cost.

SiWa: See into Walls via Deep UWB Radar