# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### Segmentation Result  
As mentioned in Section 2.4, SCCDNet can customize models with different parameters by selecting different depth values. We select the models constructed with D = 4, D = 8, D = 16, D = 32, and D = 64, respectively, for training, and compare their performances in segmentation accuracy and model complexity. In addition, in order to further verify the advantages of SCCDNet, we choose the advanced approach DeepCrack [31], U-Net [27], and SegNet [28] to compare with the model we designed. U-Net and SegNet use the Encoder-Decoder structure, and the comparison results are shown in Table 1.  
It can be seen from Table 1 that with the increase of D, the Parameters and FLOPs are increasing. However, except for SCCD-D64, the complexity of the other models is not much different. Among all the models of SCCDNet, the precision of the model is the highest when D = 4. The proportion of false positives of the model is the lowest at this time, so the ability to resist false detection is the strongest. When D = 8, the Recall of the model is the highest. At this time, the false negative of the model is the lowest, and the ability to resist missed detection is the strongest. When D = 32, the highest F-score, Dice, and IoU are obtained. Since F-score is the harmonic average of Precision and Recall, the model has the strongest ability to resist false detection and missed detection at the same time. Although SCCD-D64 has the largest amounts of parameters, its segmentation results are slightly worse than SCCD-D32, except Precision. The experimental results are consistent with the theoretical analysis. The change of D determines the number of channels and parameters of the Decoder module. Theoretically, the performance of the model will improve with the increase of the model parameters, but when the model's ability has a large redundancy relative to the scale of the dataset, there will be an over-fitting problem, and the performance of a model with more parameters will not improve or even decrease compared to the model with less parameter. The results also show that as D increases from 4 to 32, the amounts of parameters and segmentation accuracy of the model is steadily improving; however, when D increases to 64, the amounts of parameters greatly increase. At the same time, the accuracy of the model is slightly lower than that when D = 32, and the model is over-fitting at this time. Since continuing to increase D will introduce more parameters, we choose SCCD-32 as the best model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F-Score** & **Dice** & **IoU** & **FLOPs/G** & **Parameters/M** \\ \hline U-Net & 0.6953 & 0.8056 & 0.7464 & 0.7185 & 0.6002 & 111.632 & 44.021 \\ SegNet & 0.6483 & 0.7402 & 0.6912 & 0.6184 & 0.5015 & **30.703** & **29.444** \\ DeepCrack & 0.6761 & 0.4489 & 0.5396 & 0.3951 & 0.3166 & **61.280** & **58.858** \\ SCCD-D4 & **0.7336** & 0.7320 & 0.7328 & 0.6959 & 0.5773 & 61.590 & 30.161 \\ SCCD-D8 & 0.7114 & **0.8467** & 0.7732 & 0.7511 & 0.6362 & 61.817 & 30.302 \\ SCCD-D16 & 0.7234 & 0.8223 & 0.7697 & 0.7485 & 0.6324 & 62.533 & 30.663 \\
**SCD-D32** & 0.7294 & 0.8296 & **0.7763** & **0.7541** & **0.6402** & 65.004 & 31.705 \\ SCCD-D64 & 0.7302 & 0.8278 & 0.7760 & 0.7495 & 0.6372 & 74.108 & 35.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation results of different models. The best scores are in bold. Compared with U-Net, SCCD-D32 improves Precision, Recall, F-score, Dice, and IoU by 4.9%, 3.0%, 4.0%, 5.0%, and 6.7%, respectively, and reduces FLOPs and Parameters by 41.8% and 28.0%, respectively, which improves the accuracy of crack segmentation under the premise of greatly reducing the complexity of the model; compared with SegNet, although the complexity of the model is increased, the accuracy of crack segmentation is greatly improved. The precision, Recall, F-score, Dice, and IoU are increased by 9.5%, 20.5%, 14.6%, 27.4%, and 34.1%, respectively.