A review of deep learning methods for pixel-level crack detection

Cracks are a major sign of aging transportation infrastructure. The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure. In recent years, due to the remarkable success of deep learning (DL) in the field of crack detection, many researches have been devoted to developing pixel-level crack image segmentation (CIS) models based on DL to improve crack detection accuracy, but as far as we know there is no review of DL-based CIS methods yet. To address this gap, we present a comprehensive thematic survey of DL-based CIS techniques. Our review offers several contributions to the CIS area. First, more than 40 papers of journal or top conference most published in the last three years are identified and collected based on the systematic literature review method. Second, according to the backbone network architecture of the models proposed in them, they are grouped into 10 topics: FCN, U-Net, encoder-decoder model, multi-scale, attention mechanism, transformer, two-stage detection, multi-modal fusion, unsupervised learning and weakly supervised learning, to be reviewed. Meanwhile, our survey focuses on discussing strengths and limitations of the models in each topic so as to reveal the latest research progress in the CIS field. Third, publicly accessible data sets, evaluation metrics, and loss functions that can be used for pixel-level crack detection are systematically introduced and summarized to facilitate researchers to select suitable components according to their own research tasks. Finally, we discuss six common problems and existing solutions to them in the field of DL-based CIS, and then suggest eight possible future research directions in this field.