Robust multitask compressive sampling via deep generative models for crack detection in structural health monitoring

Abstract

In structural health monitoring (SHM), there is an increasing demand for real-time image-based damage detection. Such a technology is essential for minimizing hazard loss caused by delayed emergency response after earthquakes or other natural disasters, or service interruption during structural inspection. Compressive sampling (CS) is a promising solution to achieve such a goal by greatly reducing the power consumption on high-resolution image transmission when using wireless devices. However, conventional CS failed to achieve high enough compression ratios, while existing generative-model-based CS requires laboriously training a high-quality generator with many large-scale images. To overcome such a bottleneck that hinders the practical use of CS in SHM, we propose a multitask CS algorithm that only relies on existing generators trained by low-pixel crack images. By exploiting the new discovery that similar crack images share a similar sparsity pattern in their latent vectors mapped by the generator, our algorithm achieves higher crack detection accuracy and robustness within a much shorter time when using a high data compression ratio. We verify the effectiveness of the proposed CS algorithm using synthetic and real image data. The results demonstrate that this work has moved a step closer toward successful implementation of operational CS-based crack detection systems in real-time SHM.