Event-Triggered Neuromorphic Vision Sensing for Energy-Autonomous Structural Health Monitoring in Smart Cities
Keywords:
Edge computing, Parent-child chunking, Threat level prediction, Spiking neural networks, Structural health monitoringAbstract
This paper proposes a novel event-triggered neuromorphic vision framework for energy-autonomous structural health monitoring (SHM) in smart cities, addressing the fundamental limitations of conventional frame-based vision systems: data redundancy, high power consumption, and latency bottlenecks that render them impractical for battery-operated infrastructure deployments. The proposed system fuses Dynamic Vision Sensors (DVS) with MEMS accelerometers and piezoelectric energy harvesters, exploiting the intrinsic alignment between event generation and structural vibration physics: event rate is proportional to vibration velocity, while the same vibration provides harvestable energy. Four-layer Spiking Neural Network (SNN) architecture employs Resonate-and-Fire neurons with resonance frequencies log-spaced from 0.5 to 10 Hz, enabling frequency-selective feature extraction and ultra-low-power damage classification at the edge. Event streams are processed directly without frame reconstruction using contrast maximization and time-surface representations, achieving millimeter-level displacement precision (RMSE < 0.3 mm, R² > 0.98) and millisecond-level response latency. Experimental validation demonstrates 94% weighted-average accuracy for multiclass damage classification across five structural states (healthy, crack, loosening, corrosion, and delamination), with misclassifications occurring exclusively at low excitation amplitudes where event density is insufficient. For binary damage detection (healthy vs. damaged), the system achieves 97% overall accuracy and 94% damaged-class recall. Energy autonomy is achieved under typical bridge vibration conditions (≥ 0.1 g at 2 Hz), with harvested power exceeding consumption and an energy neutrality threshold of approximately 0.08 g. The system achieves approximately a 6× reduction in processing time (95 ms vs. 550 ms per 10-second window) and a 9× reduction in data volume (8 MB vs. 72 MB per 10-second window) compared to conventional frame-based approaches, establishing a pathway toward truly autonomous, continuously vigilant infrastructure monitoring that transforms aging assets into self-aware systems.
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