Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks

Pavement crack detection is critical for transportation infrastructure assessment using ground penetrating radar (GPR). This paper describes a YOLOv3 model with four-scale detection layers (FDL) to detect combined B-scan and C-scan GPR images subject to poor detection effects and a high missed detection rate of small crack feature sizes. Multiscale fusion structures, efficient intersection over union (EIoU) loss function, K-means++ clustering, and hyperparameter optimization were used in this proposed model to further improve detection performance. Results indicated that the F1 score and mAP of the YOLOv3-FDL model reached 88.1% and 87.8% and had an 8.8% and 7.5% improvement on the GPR dataset of concealed cracks, respectively, compared with the YOLOv3 model. This illustrated that this model solved the problem of missed crack detection to some extent. Future studies can take these results further, especially the three-dimensional feature analysis of pavement cracks.

Automated summary

This study proposes a YOLOv3 model with four-scale detection layers (FDL) to detect cracks in GPR images. Results show that the YOLOv3-FDL model achieves higher F1 score and mAP on the GPR dataset, indicating improved detection performance compared to the YOLOv3 model.