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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 images while having limited human resources. To address these challenges, we designed damage- and context-assisted approaches to lower the requirements on human resources. We then evaluated the accuracy of CNN-based segmentation methods using the datasets prepared with these two approaches. LAY DESCRIPTION: This work is about automated segmentation of Scanning Electron Microscopy (SEM) images taken from core and prism samples of concrete. The segmentation must detect several damage classes in each image in order to understand properties of concrete-made structures over time. The segmentation problem is approached with an artificial network (AI) based model. The training data for the AI model are created using damage- and context-assisted approaches to lower the requirements on human resources. The access to all training data and to a web-based validation system for scoring segmented images is available at https://isg.nist.gov/deepzoomweb/data/concreteScoring.

SUBMITTER: Bajcsy P

PROVIDER: S-EPMC7849179 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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

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