Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges

Abstract

Currently, damage in aging bridges is assessed visually, leading to significant personnel, time, and cost expenditures. Moreover, the results depend on the subjective judgment of the inspector. Machine-learning-based approaches, such as deep learning, can solve these problems. In particular, instance-segmentation models have been used to identify different types of bridge damage. However, the value of deep-learning-based damage identification may be reduced by insufficient training data, class imbalance, and model-reliability issues. To overcome these limitations, this study utilized photographic data from real bridge-management systems for the inspection and assessment of bridges as the training dataset. Six types of damage were considered. Moreover, the performances of three representative deep learning models—Mask R-CNN, BlendMask, and SWIN—were compared in terms of loss–function values. SWIN showed the best performance, achieving a loss value of 0.000005 after 269,939 training iterations. This shows that bridge-damage-identification performance can be maximized by setting an appropriate learning rate and using a deep learning model with a minimal loss value.