Semantic segmentation of bridge point clouds with a synthetic data augmentation strategy and graph-structured deep metric learning

Deep learning techniques are capable of providing versatile solutions to automate classification of bridge point clouds into corresponding constituent components, but training sample scarcity and erroneous boundary segmentation have hindered the extent of their application. To respond to these challenges, this study presents two synthetic data augmentation strategies for alleviating the data scarcity problem and improves the weighted superpoint graph (WSPG) model by using a graph-structured deep metric learning method for generating high quality superpoints to address the erroneous boundary segmentation problem. Evaluation experiments were conducted to validate their effectiveness. Experimental results showed that the synthetic data augmentation strategies can significantly alleviate the training sample scarcity problem. The synthetic superpoint augmentation strategy outperformed the synthetic point cloud augmentation strategy with an increase of around 6% for overall accuracy (OA), 3% for mean class accuracy (mAcc) and 18% for mean Intersection over Union (mIoU). Compared with the original WSPG model, the improved WSPG model with graph-structured deep metric learning method increased the mIoU of the overall dataset and the pier caps by 4% and 21% respectively, while also reducing the boundary segmentation errors.