Jiayao Chen

TL;DR: The quantitative evaluation of the proposed FraSegNet model illustrates that it can extract trace occurrences effectively and accurately and shows advanced performance in pixel-level fracture trace map extraction and noise reduction compared to other deep learning approaches and traditional image edge detection algorithms.

TL;DR: A framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks (CNN), namely Inception-ResNet-V2 (IRV2) is presented, which exhibits the best performance in terms of various indicators, such as precision, recall, F-score, and testing time per image.

TL;DR: The proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and non-trace classifications and provides a new alternative approach for the identification of 3D rock trace.

TL;DR: A deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) incorporated with a morphological closing operation, and a relative optimal model is found.

TL;DR: An integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning that achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakage and the leakage information.