Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

Abstract

Shotcrete spray is an indispensable process in tunnel construction. At present, the construction of tunnels in China is mainly depend on labor or mobile concrete sprayer, which has lots problems like time-consuming, low precision, and labor intensive. An intelligent detection method for tunnel shotcrete spraying is proposed in this article. There are two main issues need to be solved, one is the modeling of tunnel in real-time to monitor the thickness of shotcrete and other is the detection of spraying area in the tunnel. The LiDAR can obtain a 3D model of tunnel after performing necessary preprocess on it in real-time. On the other hand, the spraying areas are usually divided by arches in the tunnel, so we can detect the position of arches to determine the spraying areas. Inspired by the YOLO algorithm, we proposed a novel neural network structure to detect the approximate bounding boxes of the arches and a line-detection algorithm is used to determine the final positions of the spraying area in the image. The size of the weight file of our neural network is only 2.57 MB after the use of some deep compression tricks, which means our model is device friendly. After that, the object detection results in the image will be projected to the point cloud data. The experimental results suggest that our method performed well in the detection for tunnel shotcrete spraying, and the mAP for spraying area detection was found to be 91.4%.

Keywords

• 3D point cloud
• deep learning
• object detection
• tunnel shotcrete