Approaches to training multiclass semantic image segmentation of damage in concrete.

ABSTRACT: This paper addresses the problem of creating a large quantity of high-quality training segmentation masks from scanning electron microscopy (SEM) images. The images are acquired from concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica reaction (ASR), a leading cause of deterioration, cracking and loss of capacity in much of the nation's infrastructure. The target damage classes in concrete SEM images are defined as paste damage, aggregate damage, air voids and no damage. We approached the SEM segmentation problem by applying convolutional neural network (CNN)-based methods to predict the damage classes due to ASR for each image pixel. The challenges in using the CNN-based methods lie in preparing large numbers of high-quality training labelled imag

SUBMITTER: Bajcsy P 

PROVIDER: S-EPMC7849179 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature