Automated detection of through-cracks in pavement using three-instantaneous attributes fusion and Swin Transformer network

Abstract

To improve the performance of existing through-crack detection networks by solving the problem in which through-cracks are misidentified as simple surface cracks due to limited feature extraction, this study proposes an automated detection method based on the fusion of three instantaneous attributes and the Swin Transformer network. First, a 900MHZ ground-coupled radar system was used to collect data and construct the original dataset (Origin). Then, the Hilbert-Huang transform was used to extract three instantaneous attributes (instantaneous amplitude (IA), instantaneous phase (IP) and instantaneous frequency (IF)). Second, four fusion-feature datasets, i.e., IA + IP, IA + IF, IP + IF and IA + IP + IF, were constructed using the spectral weighting of individual features. Finally, the Swin Transformer network was proposed to detect through-cracks. The results show that the IA + IF dataset exhibited the best performance. The improved network achieved a 5.4% increase in the mean average precision compared with the initial network, reaching 87.78%.