# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 3 Materials and Methods  
### Port Pavement Distress Dataset (PPDD)  
The data were collected from roadways near the port area of Busan, South Korea. This region is characterized by high traffic volumes, including frequent passage of heavy-freight vehicles, which increases the likelihood of various types of cracking and road surface damage [25]. In this study, we systematically observed and collected a wide range of pavement defects occurring in such asphalt road environments. The dataset used in this study was constructed from video data captured during driving using a GoPro10 camera (CMOS sensor, 23.0 MP, 5.3K resolution at 60 fps; GoPro, Inc., San Mateo, CA, USA) mounted on the upper front section of a vehicle. Images were recorded at a resolution of 3840 \(\times\) 2160 pixels, and only frames containing at least one visible road defect were selected for analysis. The recordings were conducted during three time periods (10:00-12:00, 13:00-15:00, and 15:00-17:00) across different days and undertwo weather conditions: clear and overcast. As illustrated in Figure 1, crack and defect data were extracted from the lower part of each frame--specifically, a region extending 1000 pixels upward from the bottom edge of the image. This area corresponds to the rear view of the vehicle and includes the left, center, and right lanes of the roadway. The red polygon highlighted in the figure indicates the region used for annotation (labeling); however, this area was not cropped for use during model training or evaluation. To ensure privacy protection, personally identifiable elements such as vehicle license plates and commercial signage were anonymized in accordance with data protection standards. The dataset constructed in this study was designed to comprehensively cover various types of cracks and defects commonly found on port roads. A total of six major crack categories were defined and labeled: reflective cracking (RC), longitudinal and edge cracking (LEC), corrugation, shoving, and bleeding cracking (CSSC), rutting and depression cracking (RDC), construction joint cracking (CJC), and alligator cracking (AC). In total, 204,839 images were collected and used for analysis. The distribution of labeled instances across the dataset is as follows: 124,780 instances of RC, 183,094 of LEC, 20,210 of CSSC, 68,434 of RDC, 109,625 of CJC, and 88,571 of AC, resulting in a total of 594,714 labeled crack instances. Representative visual examples of each crack category are provided in Figure 2.  
In this study, we aim to detect and analyze surface defects and cracks on roadways using semantic and instance segmentation techniques. To this end, the dataset was annotated using polygon-based labeling and crack region masks, following the annotation format of the MS COCO dataset [26]. For semantic segmentation, the polygon annotations for each crack instance were converted into binary masks distinguishing background and object regions. Every pixel within the annotated regions was assigned a class label corresponding to one of the six crack categories. The labeling process was assisted by the use of the Computer Vision Annotation Tool (CVAT) [27], an automated annotation platform. The automatically generated labels were manually verified on a frame-by-frame basis to ensure accuracy. While inter-annotator agreement was not formally measured during dataset construction, we plan to evaluate annotation consistency using a subset of samples in a follow-up study. Figure 3 presents sample images illustrating the mask annotations for semantic segmentation and polygon annotations for instance segmentation. The constructed dataset reflects the distribution of crack types found on port-area roads and provides a reliable and generalizable foundation for real-world crack detection and analysis tasks. Figure 1: Example image illustrating the inclusion criteria for the annotated region on paved road surfaces. The red polygon highlighted in the figure indicates the area used for annotation.