Automatic concrete crack segmentation model based on transformer

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.

Automated summary

In this study, a novel SegCrack model for pixel-level crack segmentation using deep learning methods is proposed. The model utilizes a hierarchically structured Transformer encoder to output multiscale features and incorporates a top-down pathway and lateral connections for progressive feature upsampling and fusion. An online hard example mining strategy is also adopted to improve model performance. Experimental results demonstrate SegCrack achieves high precision, recall, F1 score, and mean intersection over union on the test set.