A deep segmentation network for crack detection with progressive and hierarchical context fusion

Accurate detection of crack defects in infrastructure is crucial to ensure their safety and extend their service life. However, the presence of complex backgrounds, various shapes and sizes of crack defects, incomplete discontinuous crack defects, and class imbalance makes this task challenging. Traditional image processing techniques are sensitive to image noise and may miss cracks due to weak textures, complex lighting conditions, and other similar objects on the pavement. In recent years, deep learning-based segmentation networks have been proposed for crack defect detection, but they still face challenges in high-precision crack segmentation due to insufficient local feature processing, information loss caused by pooling operations, and limited receptive fields. To address these issues, we propose an end-to-end deep crack segmentation network, called PHCF-Net, which incorporates progressive and hierarchical context fusion. Firstly, the proposed network consists of progressive context fusion (PCF) and hierarchical context fusion (HCF) blocks for effective aggregation of global and local context information. Secondly, a multi-scale context fusion (MCF) block is proposed for multi-scale context extraction and aggregation. Finally, in order to solve the information loss problem caused by pooling operations, a hierarchical context fusion (HCF) block is proposed for effective aggregation of the deep and shallow features. In addition, a multi-scale input unit is also applied to the proposed segmentation network to obtain more context information. To evaluate the performance of PHCF-Net, we have conducted experiments on two publicly available crack segmentation datasets and compared its performance with mainstream segmentation models. The results demonstrate that proposed PHCF-Net achieves better pixel-level crack detection results and outperforms other advanced segmentation models.