Accelerating Classification on Resource-Constrained Edge Nodes Towards Automated Structural Health Monitoring

Resource-constrained edge nodes are ubiquitous in industrial settings yet are challenged by limited computing resources. Leveraging computational advantage and perceptual awareness of Gabor filters, a hybrid classifier to mitigate computational requirements in the context of classification of key components of civil structures which are essential in the proactive structural assessment, specific repairs, and maintenance post-construction is given. Deployment of the hybrid classifier using the CoreML framework exhibits favorable classification accuracy and robustness as compared to contemporary state-of-the-art classifiers.