# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 4 Experiments
### Experimental Results  
#### 4.2.1 Quantitative Evaluation  
To compare semantic segmentation performance across different types of pavement cracks, we conducted an evaluation using three models with distinct feature extraction mechanisms: Mask2 Former, SegFormer, and DeepLabv3+. To compare semantic segmentation performance across different types of pavement cracks, we conducted an evaluation using three models with distinct feature extraction mechanisms: Mask2 Former, SegFormer, and DeepLabv3+. For Mask2 Former, we experimented with two backbone networks: ResNet-101 and Swin Transformer. SegFormer uses MiT-B2, and DeepLabv3+ is implemented with ResNet-50. Table 2 presents the evaluation results on the test set, reporting Pixel Accuracy (PA) and Intersection over Union (IoU) scores across six crack categories for all four model configurations. In this study, we employed two standard metrics--Pixel Accuracy (PA) and mean Intersection over Union (mIoU)--to evaluate the performance of semantic segmentation models. Pixel Accuracy measures the ratio of correctly classified pixels to the total number of pixels in the image, providing an overall sense of classification accuracy. In contrast, mIoU quantifies the average overlap between the predicted and ground truth segmentation masks. It is calculated by computing the Intersection over Union (IoU) for each class, defined as the intersection area divided by the union area ofpredicted and true regions, and then averaging across all classes. mIoU is particularly regarded as a more robust metric in scenarios with class imbalance, as it accounts for the quality of segmentation on a per-class basis. Among the models, SegFormer demonstrated the highest semantic segmentation performance in terms of both PA and mIoU, achieving particularly high accuracy (97.52%) on the background class. In per-class performance, the CSSC category showed the lowest prediction scores in three out of the four models, while the AC type exhibited relatively higher accuracy. Overall, the transformer-based models--SegFormer and Mask2 Former with a Swin Transformer backbone--achieved superior performance in semantic segmentation. These results highlight the effectiveness of transformer-based architectures in producing fine-grained masks for pavement crack and defect detection. To evaluate instance segmentation performance across different types of pavement cracks, we compared the Mask2 Former and YOLOv7 models. For Mask2 Former, experiments were conducted using two different backbone networks: ResNet-101 [30] and Swin Transformer [31]. Table 3 presents the AP\({}_{50}\) and overall AP scores for the three model configurations across the six crack categories in the test dataset. AP\({}_{50}\) (Average Precision at an IoU threshold 50%) measures the accuracy of predictions with an Intersection over Union greater than 0.5. In contrast, AP refers to the mean Average Precision calculated across IoU thresholds from 0.5 to 0.95 in increments of 0.05, following the COCO evaluation metric [32], providing a more comprehensive assessment of overall model performance. Among the models, YOLOv7 achieved the best overall performance, demonstrating particularly strong results on the AC category, with an AP\({}_{50}\) of 87.2% and an AP of 57.5%. On the other hand, the RC category yielded the lowest performance across all three models, suggesting that this type of crack is comparatively more challenging to segment and detect than others. Although the CSSC class accounts for only 3.3% of the dataset, its segmentation performance is not the lowest among the six crack types. For instance, the YOLOv7 model achieves an AP of 38.3% for CSSC, which is notably higher than that of the RC class (21.3%), despite the RC class occupying a larger portion of the dataset. This suggests that the challenge of segmentation may depend not only on class frequency but also on visual distinctiveness and contextual cues.