Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning

Aiming to solve the challenges of low detection accuracy, poor anti‐interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning‐based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4‐ED), is proposed. In the YOLOv4‐ED, EfficientNet is used as the backbone to improve the identification accuracy of indistinguishable defect targets in complex tunnel background and light conditions. Furthermore, DSC block is introduced to reduce the storage space of the model and thereby enhance the detection efficiency. The experimental results indicate that the mean average precision, F1 score, Model Size, and FPS of YOLOv4‐ED are 81.84%, 81.99%, 49.3 MB, and 43.5 f/s, respectively, which is superior to the comparison models in both detection accuracy and efficiency. Based on robust and cost‐effective YOLOv4‐ED, a tunnel lining defect detection platform (TLDDP) with the capacity of automated inspection of various lining defects (i.e., water leakage, crack, rebar‐exposed) is built. The established TLDDP can realize the high‐precision and automatic detection of multiple tunnel lining defects under different lighting and complex background conditions of the practical in‐service tunnel.

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method 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 leakages and the leakage information (area, location, lining segments, etc.).

Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data.

A deep learning-based approach for refined crack evaluation from shield tunnel lining images

Quantification of water inflow in rock tunnel faces via convolutional neural network approach

Deep learning-based automatic recognition of water leakage area in shield tunnel lining

An optimization strategy to improve the deep learning-based recognition model of leakage in shield tunnels

With the rapid development of maintenance of the urban metro tunnel, the structural defects of the metro shield tunnel, especially the water leakage, need to be recognized quickly and accurately. Mask R-CNN is one of the state-of-the-art instance segmentation methods, which has achieved the automatic segmentation of shield tunnel leakage. Although the error rate of the Mask R-CNN algorithm is very low due to a series of complex network structures such as feature pyramid network (FPN) and region proposal network (RPN), the inference cost is 3.24 s per image. Because the structural inspection usually takes only 2–3 h, quick processing of defect images seems necessary. Inspired by a real-time detection method called Single Shot MultiBox Detector (SSD) and the generation of Mask R-CNN, this study constructed a novel convolutional network for fast detection and segmentation of the water leakage. Taking into account the unique appearance and features of water leakage area, it was divided into five groups of different backgrounds to evaluate the interference caused by the complex background and its various shapes. Finally, 278 images were used to test the network, and the average IOU was found as 77.25%, which was close to that of Mask R-CNN. Additionally, the average segmentation time was calculated as 0.09 s per image, far less than Mask R-CNN, which meets the actual requirement of engineering.

Xinyuan Cai

Papers

Deep learning-based automatic recognition of water leakage area in shield tunnel lining

An optimization strategy to improve the deep learning-based recognition model of leakage in shield tunnels

Semantic Segmentation of Shield Tunnel Leakage with Combining SSD and FCN