An Automated Inspection Method for the Steel Box Girder Bottom of Long-Span Bridges Based on Deep Learning

Abstract

Among the existing methods for the maintenance and monitoring of bridges, human eye evaluation, which is inevitably subjective and time-consuming, is still the most widely applied. In this paper, a new automatic inspection method for the deterioration of the bottom of a steel box girder based on computer vision is proposed. First, a computer vision system installed on a bridge inspection vehicle is used to capture photos of the bottom of the steel box girder, which are synthesized into panoramas by image stitching technology. Then, a U-net based semantic segmentation network is used to identify the diseases in the panoramas. Finally, the statistics of the disease are executed to evaluate the health condition of the box girder bottom. Comparisons between various sets of deep neural network models are also carried out. Our experimental results show that this method is effective and feasible as a replacement for manual inspection and can achieve a more standardized and accurate evaluation. This method has great engineering potential value in the progress of intelligent structural health management, and should be extended to solve other similar problems.

Keywords

• Bridge engineering
• deep learning
• image stitching
• disease detection