2023 Volume 4 Issue 1 Pages 1-8
Detection of subway tunnel distress is a crucial task for ensuring public safety. It is typically performed manually by technical workers, which has become increasingly expensive due to the decline in working-age population and increase in aging subway tunnels. We have previously proposed deep learning-based approaches for segmenting distress regions in subway tunnel images to reduce the maintenance cost; however, these approaches still have some difficulties in practical application and cannot address some worksite requirements. Thus, in this study, we developed technologies for enabling deep learning-based distress segmentation approaches to adapt better to practical scenarios. We propose a soft label approach for model training to specifically address the issue of slight deviation in the manual annotations of cracks. Additionally, for model evaluation, we propose using region-based metrics as a supplement to the commonly employed pixel-based metrics for a better evaluation of the crack segmentation performance. In the experiments, our distress segmentation approach, when enhanced by the above technologies, demonstrates the potential for practical use in subway tunnel inspection.
