An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds

Discovering seepage is widely thought to be critical for maintaining the healthy conditions of the tunnel. Unfortunately, most of the seepage surveys are still manual with tedious, time-consuming, and inefficient as well as work-related physical injuries. To address this problem, this research proposes an encoder-decoder deep learning method combined with point cloud techniques for multi-class object segmentation, including seepage, from 3D tunnel point clouds. This method develops data processing and feature extraction techniques to perform normalization of 3D point clouds with full consideration of point features, followed by constructing voxels as input to the proposed encoder-decoder architecture for learning. In the training process, an optimal model is selected with a learning rate of 0.0001, a batch size of 256, and a voxel boundary of 8. Subsequently, the optimal well-trained model is applied to the testing set, achieving excellent performance. Comparisons with other state-of-the-art methods and four data processing strategies are conducted, demonstrating that the proposed method outperforms in segmenting large-scale 3D point clouds. Overall, the proposed method performs excellently, beneficially contributing to the multi-class object segmentation from 3D tunnel point clouds with great practical potential.