A refinement network embedded with attention mechanism for computer vision based post-earthquake inspections of railway viaduct

Abstract

Post-earthquake inspection of structures based on computer vision is developing rapidly due to the advantages of high efficiency and without manual feature extraction. However, it is still necessary to investigate how to accurately recognize structural components and damage from the perspective of pixels. Fortunately, refinement network which named RefineNet has been developed for semantic segmentation of images, which helps to combine low-level features and high-level semantics to generate high resolution segmented images for efficient end-to-end learning. Therefore, RefineNet is used in this study as a network architecture for semantic segmentation tasks of recognizing railway viaducts components and damages. Moreover, it is proposed to embed the convolutional block attention mechanism in the down-sampling process of the RefineNet to extract image features, which helps the network to assign different weights to image regions of different importance and effectively improve the extraction effect of intermediate features. With the provided large-scale synthetic railway viaduct image dataset, which named Tokaido Dataset, the proposed RefineNet with Attention Mechanism (RefineNet-AM) is used for structural condition assessment of railway viaduct, including semantic segmentation tasks of components and damages of railway viaduct. Based on the test dataset, it is shown that proposed RefineNet-AM can inspect the structural components and damage of railway viaduct with satisfactory accuracy.