# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 4 Experiments
### Experimental Results
#### 4.2.2 Qualitative Evaluation  
Figure 4 presents a visual comparison of how accurately the semantic segmentation models distinguish different crack types in the PPDD dataset. Panel (a) shows the ground truth labels, depicting four types of pavement damage--RC, LEC, CJC, and AC--in their annotated forms. For RC, a horizontally oriented crack, SegFormer was the most effective in capturing the number and type of cracks, although it struggled with fine boundary delineation, as shown in (c). Mask2 Former (d) also detected some portions of RC, but there were instances of omission and misclassification, particularly where RC was confused with AC. In the case of CJC, DeepLabv3+ (b) failed to deliver precise predictions. However, SegFormer (c), Mask2 Former-r101 (d), and Mask2 Former-swin-l (e) showed relatively detailed boundary representation and yielded outputs closely resembling the ground truth. Figure 4: An example of ground truth and model prediction on semantic segmentation. (**a**) Ground truth—prediction from DeepLabv3+ (**b**), SegFormer (**c**), Mask2 Former with ResNet-101 (**d**), and largest setting of Swin Transformer (**e**). \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**RC**} & \multicolumn{1}{c}{**LEC**} & \multicolumn{1}{c}{**CSSC**} & \multicolumn{1}{c}{**RD**} & \multicolumn{1}{c}{**CJC**} & \multicolumn{1}{c}{**AC**} & \multicolumn{1}{c}{**Total**} \\ \hline Mask2 Former-r101 & & & & & & & & & \\ AP50 & 45.9 & 50.7 & 49.1 & 62.6 & 64.9 & 69.7 & 57.15 \\ AP & 15.4 & 17.1 & 18.7 & 36.6 & 29.6 & 43.3 & 26.78 \\ \hline Mask2 Former-swin-s & & & & & & & & \\ AP50 & 51.9 & 56.5 & 57.6 & 66.7 & 70.10 & 76.3 & 63.18 \\ AP & 18.5 & 20 & 22.7 & 41.4 & 34.6 & 49.4 & 31.10 \\ \hline YOLOv7 & & & & & & & & \\ AP & 57.4 & 65.1 & 80.2 & 84 & 84.3 & 87.2 & 76.37 \\ AP & 21.3 & 25.2 & 38.3 & 55.9 & 45.2 & 57.5 & 40.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation results for each class and overall performance in instance segmentation. Figure 5 provides a visual comparison of instance-level segmentation performance, showing the predictions of YOLOv7 (b), Mask2 Former with a ResNet-101 backbone (c), and Mask2 Former with a Swin-L backbone (d), alongside the ground truth annotations (a). YOLOv7 demonstrated stable detection of AC, which spanned a wide area, and yielded relatively high confidence scores for the predicted instances (AC: 87.0, 89.8). However, it tended to miss finer details in CJC, indicating a limitation in capturing subtle damage patterns. In the case of Mask2 Former, regardless of the backbone architecture, the model tended to overpredict the number of crack instances compared to the ground truth annotations. This suggests the presence of false-positive errors in the Mask2 Former predictions. Figure 6 visualizes the results of both semantic segmentation and instance segmentation on the same road image. It can be observed that cracks belonging to the RDC class are frequently misclassified as AC when they exhibit similar visual patterns. In both semantic and instance segmentation results ((a)-(c) and (d)-(f)), regions annotated as RDC are often predicted as AC, suggesting that the models tend to confuse the two classes when structural similarities are present. The predictions from Mask2 Former (middle columns (b) and (e)) show a tendency to over-detect AC, RC, and LEC classes across both tasks. Numerous additional crack segments--colored as AC, RC, or LEC--appear even in areas without corresponding ground truth annotations. This over-detection may be attributed to the model's high sensitivity and the class imbalance in the training dataset. In contrast, YOLOv7 (Subfigure (f)) produces fewer predictions in regions without actual cracks, whereas Mask2 Former frequently generates false positives in such areas.