Automated fatigue crack detection of orthotropic steel decks using an improved mask region-based convolutional neural network approach

Orthotropic steel decks (OSD) are widely used in the bridge industry, but they are susceptible to fatigue cracking due to various factors such as weld defects, overload traffic, and complicated welded joint configurations. Traditional crack detection methods rely mainly on visual inspection, which is time-consuming and prone to errors. Recently, many crack detection methods based on machine vision have been introduced. However, accurately and effectively detecting fatigue cracks of OSD remains challenging due to the indistinct nature of these cracks compared to the background and their susceptibility to various types of noise. To address these problems, this research proposes an improved Mask Region-based Convolutional Neural Network (Mask R-CNN). The proposed model enhances the feature extraction ability of the original Mask R-CNN by introducing a Convolutional Block Attention Module (CBAM) and Path Augmentation Feature Pyramid Network (PAFPN) into the backbone network. Additionally, a morphological closing operation is performed on the extracted fatigue cracks to address the problem of disjoint cracks. On the basis of crack mask closure, the pixel-wise quantization of cracks was achieved using relevant OpenCV functions. Evaluation on a crack dataset of OSD shows that the proposed model outperforms other methods with precision, recall, average precision (AP), and F1 score of 83.98%, 84.44%, 77.9%, and 84.21%, respectively. In conclusion, the experiment validates that the proposed model can effectively detect, segment, and quantify fatigue cracks of OSD.