A Deep Learning-Based Fine Crack Segmentation Network on Full-Scale Steel Bridge Images With Complicated Backgrounds

Abstract

Automatic defect detection of steel infrastructures in structural health monitoring (SHM) is still challenging because of complicated background, non-uniform illumination, irregular shapes and interference in images. Conventional defects detection mainly relies on manual inspection which is time-consuming and error-prone. In this study, a deep learning-based fine crack segmentation network, termed as FCS-Net was proposed in light of ResNet-50 and fully convolutional network (FCN). Structural modifications including Batch Normalization (BN) and Atrous Spatial Pyramid Pooling (ASPP) were made. In full-scale steel girder images with complicated background and fine foreground, the proposed FCS-Net achieves a MIoU of 0.7408, outperforming benchmark algorithms such as LinkNet, DeepLab V3, and CrackSegNet. Moreover, the ablation experiments were performed that justified the contribution and necessity of each modification.