# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 5 Discussion  
### Road Crack Detection and Segmentation Dataset  
In selecting representative models for evaluation, we referred to established practices in prior studies on pavement crack analysis to ensure methodological consistency. Accordingly, we incorporated both CNN-based segmentation architectures (e.g., YOLO, DeepLab) and ViT-based segmentation architectures (e.g., Mask2 Former, SegFormer), which are widely recognized and frequently adopted as core architectures in the recent literature. This selection enables meaningful benchmarking while maintaining relevance to previous research. Recent advancements in deep learning-based crack detection and segmentation technologies have been actively reported through competitive challenges [1], newly released datasets [6], and academic publications [33]. The fine-grained visual appearance of cracks--whether on natural surfaces or man-made structures--can vary significantly depending on the surrounding environment and the underlying causes of formation. OmniCrack30K [6] was developed to facilitate a comprehensive understanding of cracks by aggregating 30,000 diverse crack samples extracted from over 20 different datasets. The authors evaluated crack segmentation performance using both domain-specific models trained to represent crack-specific features and a group of general-purpose segmentation models, including DeepLabV3 [34] and Mask2 Former [7]. Their findings revealed that the general-purpose models trained on the proposed dataset significantly outperformed the crack-specific models. This outcome highlights the critical importance of having access to a sufficiently large and well-curated dataset in advancing the performance of deep learning-based models. In the context of paved roads, cracks are not only defined by fissures but also by the overall visual degradation of the road surface, which constitutes  
Figure 6: Qualitative comparison of road crack segmentation results. Top row (**a**–**c**): semantic segmentation results—(**a**) ground truth, (**b**) Mask2 Former (Swin-Large) prediction, (**c**) SegFormer prediction. Bottom row (**d**–**f**): instance segmentation results—(**d**) ground truth, (**e**) Mask2 Former (Swin-Large) prediction, (**f**) YOLOv7 prediction. an important visual characteristic for detection. This aspect is highly domain-specific and serves the independent purpose of supporting road maintenance through dedicated crack detection. To address this, the road damage dataset (RDD) [14] was introduced, providing annotated bounding boxes for classes such as longitudinal cracks, transverse cracks, alligator cracks, and potholes, collected from paved roads across multiple countries. This dataset has since supported the advancement of crack detection research through a series of ongoing competitions [5; 18; 35; 36; 37]. In the recent ORDDC2024 challenge [37], which utilized the RDD dataset, all of the top five teams adopted models from the YOLO series [20] as their backbone architectures, aiming to maximize detection performance relative to inference time. The highest-performing model achieved an overall F1-score of 79.27 across all participating countries. The SHREC2022 challenge [1] focused on evaluating semantic segmentation performance for various crack and potholes classes. The competition used Weighted Pixel Accuracy (WPA) and mean Intersection over Union (mIoU) as evaluation metrics. To facilitate performance comparison with submitted models, the organizers employed DeepLabv3+ [9] as a baseline. DeepLabv3+ achieved a WPA of 0.598 and an mIoU of 0.676, establishing a representative baseline and reference level of performance for benchmark studies in crack segmentation. In this study, we adopted widely used baseline models for crack segmentation--such as YOLOv7 [10], Mask2 Former [7], and Deeplabv3+ [9]--which have been frequently employed in prior research for detection and semantic segmentation tasks.