Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

Abstract

Most of the studies on oil tanks have focused on the analysis of deformation and settlement, and more research needs to be done on crack extraction from external LNG tanks.

Oil tanks are more sensitive to temperature due to the lower temperature inside the tank. Using infrared images as a dataset for crack recognition can identify cracks that the naked eye cannot see, and a convolutional neural network that introduces a channel attention mechanism is used for crack recognition with a recognition accuracy of 85.9%.

The automatic extraction of three-dimensional (3D) crack point clouds using depth images is novel and accurate, with an accuracy of about 97.6%.