Fig. 1. Schematic of Crack Identification Net (CIN)

Fig. 2. Flowchart of identification

Fig. 3. Example of crack and non-crack images. (a) Crack images; (b) non-crack images

Fig. 4. Training and validation results for group 4th model. (a) Training results; (b) validation results

Fig. 5. Accuracy rate of training and validation for 5 different groups

Fig. 6. Example of identification results. (a) Original image; (b) crack classification and identification result

Fig. 7. Segmentation results obtained by the proposed algorithm and traditional methods. (a) Original image; (b) improved Otsu algorithm; (c) improved Canny algorithm; (d) improved median filter algorithm; (e) our algorithm

Fig. 8. Comparison of evaluation indicators of each algorithm

Fig. 9. Segmentation results obtained by the proposed algorithm and clustering methods. (a) Original image; (b) K-means algorithm; (c) mean shift algorithm; (d) fuzzy C-means algorithm; (e) our algorithm

Fig. 10. Comparison of evaluation indicators of each algorithm

Fig. 11. Identification of cracks with different thicknesses. (a) Original image; (b) identification of neural network; (c) segmentation; (d) mark

Fig. 12. Example of crack marking. (a) Crack 1; (b) crack 2

Fig. 13. Original crack images for quantitative calculation