Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation

Laser scanning is used as a modern means to capture data from tunnels to assess their condition, but automated processing requires robust component detection and deterioration characterization. In order to segment 3D tunnel point clouds aiming at more accurate results with high time efficiency, this paper describes a point cloud technique that collects actual tunnel scenes and develops an attention-enhanced sampling point cloud network named ASPCNet. In the developed model, the feature embedding module is responsible to process the point cloud data for local features followed by the attention module for enhancing the feature extraction and learning. Additionally, the point downsampling-upsampling structure fully assists the model to strengthen the capability to process point clouds for time efficiency. In the training process, a weighted focal loss is designed to enhance the model learning by eliminating the effect of data imbalance. The developed ASPCNet is trained and then tested on a dataset collected from a cross-river metro tunnel section in China, demonstrating its efficiency and effectiveness. In comparison with different sampling ratios, state-of-the-art methods, and sampling methods, the ASPCNet with a uniform sampling rate of 2 exhibits the best performance, achieving an overall accuracy of 0.9758, a mean Intersection over Union (MIoU) of 0.8988, and an inference time of 4.1 s, demonstrating that the sampling structure involved in this research boosts the time efficiency, the developed model has superior performance, and the sampling method adopted is beneficial to strengthen the model performance.

Automated summary

This paper presents a method called ASPCNet, which uses point cloud technique to segment 3D tunnel point clouds for accurate and efficient processing. The developed model demonstrates superior performance and efficiency and the adopted sampling method strengthens the model performance.