Answers are grounded in this paper and cite their sources.
This system demonstrates real-time monitoring of infrastructure cracks and anomalies in civil engineering, suggesting enhanced safety.
Structural crack detection and environmental hazard monitoring are essential in non-destructive testing (NDT) for civil infrastructure. This paper presents a real-time, AI-powered multi-sensor dashboard that receives data from a mobile climbing robot. The system integrates optical, thermal infrared (IR), and gas sensing, each supported by dedicated deep learning models. AI inference is performed server-side to classify surface cracks, segment IR-based crack regions, and detect gas anomalies. Models include MobileNetV2 for crack classification, U-Net for IR segmentation, and a gas classifier using MQ-135 data. The dashboard fuses predictions with visual overlays and quantitative measurements, enabling faster, safer, and more robust infrastructure inspection.
No takes yet. Share an insight, caveat, or question.
Zia et al. (2025) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: