Spectral clustering makes use of the spectrum of an input affinity matrix to segment data into disjoint clusters. The performance of spectral clustering depends heavily on the quality of the affinity matrix. Commonly used affinity matrices are constructed by either the Gaussian kernel or the self-expressive model with sparse or low-rank constraints. A technique called diffusion which acts as a post-process has recently shown to improve the quality of the affinity matrix significantly, by taking advantage of the contextual information. In this paper, we propose a variant of the diffusion process, named Self-Supervised Diffusion, which incorporates clustering result as feedback to provide supervisory signals for the diffusion process. The proposed method contains two stages, namely affinity learning with diffusion and spectral clustering. It works in an iterative fashion, where in each iteration the clustering result is utilized to calculate a pseudolabel similarity so that it can aid the affinity learning stage in the next iteration. Extensive experiments on both synthetic and real-world data have demonstrated that the proposed method can learn accurate and robust affinity, and thus achieves superior clustering performance.
Conventional structural health monitoring (SHM) evaluates the condition of civil structures by analyzing the data acquired by advanced sensors. The requirement of overinvestment in specialized equipment and labor for implementation prevents the traditional SHM from large-scale usage. On the other hand, computer vision techniques offer cost-effective solutions for SHM thanks to its inherent advantage in data acquirement and processing. More importantly, it has been demonstrated that these emerging solutions can produce reliable condition diagnoses for civil structures using pure image data. In this article, a novel transformer-based neural network is proposed for vision-based structural condition assessment which is formulated to a semantic segmentation problem. The network employs Swin Transformer as the backbone and MaskFormer as the overall architecture to recognize components (sleepers, slabs, columns, etc.) and damage (concrete damage, exposed rebar) of structures. Unlike the commonly used fully convolutional networks, the proposed model tackles semantic segmentation as a mask classification rather than a pixel classification problem. To deal with the lack of training data, an image data augmentation method called Copy-Paste is extended and applied for training data generation, resulting in an increase of around 40% data for component segmentation and 71% data for damage segmentation. Experimental validations on the Tokaido railway viaduct dataset show that the proposed approach is very accurate, achieving 97% and 90% mean Intersection Over Union for component and damage segmentation, outperforming the existing methods by a significant margin. The accurate segmentation results can provide meaningful information for downstream SHM tasks.
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