Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision

Abstract

Contractors should conduct strict quality inspection of the steel bars used in concrete structures and need to automate the process of quality inspection. The objective of this study is to develop an Artificial Intelligence Quality Inspection Model (AI-QIM) that can execute quality inspection on steel bars at the construction site. The proposed AI-QIM is built on the Mask Region-based Convolutional Neural Network (Mask R-CNN) technique, which can perform instance segmentation of steel bars. This object detection technique is integrated with a stereo vision camera to generate information on steel bar installation. A contractor can use the proposed AI-QIM to estimate the quantity, spacing, diameter, and length of steel bars during quality inspection. A sample case study indicated that the AI-QIM yielded a maximum relative error of 3% when measuring steel bar spacing and a maximum relative error of 8% when measuring steel bar lengths within a range of 1–2 m from a stereo camera.