Automatic UAV inspection of tunnel infrastructure in GPS-denied underground environment

In the Architecture, Engineering and Construction (AEC) industry, unmanned aerial vehicles (UAV) has been widely acknowledged as a promising tool to perform adaptive structural health monitoring automatically. However, there still remains some challenges for drones to collect image data of underground structures, primarily due to low light and no GPS conditions. In order to facilitate data acquisition, this article developed a mobile software development kit (MSDK) for drone using visual positioning and predefined controlling code, which enabled the drone to automatically fly along a designated sinusoidal route, whilst continuously taking videos and images of the tunnel surface. The developed MSDK was able to adjust the drone parameters (e.g., overlapping rate, inspection range, heading, flight direction between frames of the video) for different underground infrastructure conditions. Furthermore, a field test is conducted in an abandoned windless tunnel near Cork (Goggins Hill Tunnel) to test its feasibility. Results show that the 40-m difference between the designated routine and actual routine was 1.9%, and the collected data processed by Pix4Dmapper could reconstruct the complete tunnel scene and surface details. The navigation method proposed in this paper allows UAVs to perform automatic inspection without GPS, and the collected image data is used to build a tunnel panorama view

Automated pixel-level crack monitoring system for large-scale underground infrastructure – A case study at CERN

In concrete tunnel linings, cracks usually appear and develop as an early sign of structural degradation prior to severe intolerable serviceability damage. The monitoring and assessment of crack spatial distribution can highlight long-term tunnel structural behavior and facilitate tunnel maintenance. This study describes a remote and automated system for conducting crack monitoring at a pixel-level scale using robot-mounted imaging technology. This system collects crack images remotely and stitches them together to create a panorama image of the tunnel surface. Employing transfer learning, this study fine-tunes and improves the state-of-the-art semantic segmentation model with a lightweight backbone, DeepLab V3plus, to detect cracks automatically. A novel smooth blending prediction method is implemented on the panorama to present long-distance tunnel crack distribution, alleviating misclassification problems encountered in high-resolution image inference. In addition, transfer learning, tailored loss functions, and regularization techniques have been developed based on the CERN tunnel crack database characteristics to maintain high performance and generalization of the proposed method. Field trials conducted in tunnels at CERN demonstrate the feasibility of the proposed crack monitoring system. Results show that the proposed system allows the identification of severe crack-damaged tunnel sections and specific crack patterns, which can be related to the structural behavior of the tunnel lining