Objectives To solve problems that the existing crack identification algorithms are not accurate and the detection and segmentation tasks cannot be performed simultaneously, we propose a pavement crack identification method via the improved Mask R-CNN model.
Methods First, the crack dataset is collected and labeled. And the crack dataset is trained and tested by Mask R-CNN model, and the aspect ratios of the anchor points in the model are adjusted to segment the crack pixels in the generated detection box while the crack is located. Second, to solve the problem that the crack detection boxes generated by Mask R-CNN model are inaccurate, C-Mask R-CNN is designed to improve the quality of crack region proposal by cascading multi-threshold detectors and achieve accurate positioning under high threshold. Finally, a series of optimization parameters and experimental comparison are carried out for the improved model, and the effectiveness of the proposed model is verified.
Results The experimental results show that the mean average precision (mAP) of C-Mask R-CNN model in the detection part is 0.954, which is 9.7% higher than that of the conventional model, and its mAP in the segmentation part is 0.935, which is 13.0% higher than that of the conventional model. It confirms that the C-Mask R-CNN model performs well in identifying cracks.
Conclusions In summary, the proposed C-Mask R-CNN model can locate and extract cracks with high identification accuracy.
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