Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automatic concrete crack segmentation model based on transformer
Abstract Routine visual inspection of concrete structures is essential to maintain safe conditions. Therefore, studies of concrete crack segmentation using deep learning methods have been extensively conducted in recent years. However, insufficient performance remains a major challenge in diverse field-inspection scenarios. In this study, a novel SegCrack model for pixel-level crack segmentation is therefore proposed using a hierarchically structured Transformer encoder to output multiscale features and a top-down pathway with lateral connections to progressively up-sample and fuse features from the deepest layer of the encoder. Furthermore, an online hard example mining strategy was adopted to strengthen the detection of hard samples and improve the model performance. The effect of dataset size on the segmentation performance was then investigated. The results indicated that SegCrack achieved a precision, recall, F1 score, and mean intersection over union of 96.66%, 95.46%, 96.05%, and 92.63%, respectively, using the test set.
Highlights A novel Transformer architecture for concrete crack segmentation is proposed. Online Hard Example Mining strategy is utilized to improve model performance. The effect of data set size on model performance is studied. The proposed segmentation framework outperforms state-of-the-art models.
Automatic concrete crack segmentation model based on transformer
Abstract Routine visual inspection of concrete structures is essential to maintain safe conditions. Therefore, studies of concrete crack segmentation using deep learning methods have been extensively conducted in recent years. However, insufficient performance remains a major challenge in diverse field-inspection scenarios. In this study, a novel SegCrack model for pixel-level crack segmentation is therefore proposed using a hierarchically structured Transformer encoder to output multiscale features and a top-down pathway with lateral connections to progressively up-sample and fuse features from the deepest layer of the encoder. Furthermore, an online hard example mining strategy was adopted to strengthen the detection of hard samples and improve the model performance. The effect of dataset size on the segmentation performance was then investigated. The results indicated that SegCrack achieved a precision, recall, F1 score, and mean intersection over union of 96.66%, 95.46%, 96.05%, and 92.63%, respectively, using the test set.
Highlights A novel Transformer architecture for concrete crack segmentation is proposed. Online Hard Example Mining strategy is utilized to improve model performance. The effect of data set size on model performance is studied. The proposed segmentation framework outperforms state-of-the-art models.
Automatic concrete crack segmentation model based on transformer
Wang, Wenjun (Autor:in) / Su, Chao (Autor:in)
16.04.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Automatic crack segmentation model based on multi-branch aggregation transformer
| SAGE Publications | 2024