3D vision-based bolt loosening assessment using photogrammetry, deep neural networks, and 3D point-cloud processing

Structural bolts are essential structural elements. Detection of structural bolt loosening is of great importance to provide earlier warnings of structural damages and prevent catastrophic system level collapse. Most existing studies about bolt loosening assessment were built in 2D computer vision, where the assessment may be restricted based on the camera views. In this paper, a novel 3D vision-based methodology is proposed for autonomous bolt loosening assessment. First, a 3D point cloud of bolted connection is created using readily available 2D images. Second, a new convolutional neural network (CNN)-based method is developed to localize structural bolts in the 3D point cloud. Further, a 3D point cloud processing algorithm is developed to recognize and quantify bolt loosening. Parameter studies were conducted to investigate the effectiveness of the proposed pipeline. Finally, a real-world implementation has been conducted to quantify bolt loosening on a steel column base connection with bolts. The results indicate that the proposed bolt loosening assessment methodology can effectively localize and quantify bolt loosening at high accuracy and low cost.

Automated summary

This paper proposes an autonomous bolt loosening assessment method based on 3D vision. By creating a 3D point cloud of bolted connection using readily available 2D images and using a convolutional neural network (CNN) to recognize and quantify bolt loosening, the accurate localization and measurement of bolt loosening are achieved. The experimental results demonstrate that the proposed method can effectively localize and quantify bolt loosening with high accuracy and low cost.