# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 2 Related Work  
### Public Dataset for Crack Identification and Detection  
Public datasets for analyzing the visual characteristics of cracks using deep learning-based visual perception models can largely be categorized into two research directions. The first approach focuses on segmentation for detailed representation of cracks. This involves identifying fine-grained visual features of cracks that appear in diverse contexts--such as roads, bridges, and concrete building facades--under varying causes of deterioration. Toachieve this, crack segmentation datasets typically involve orthographic imaging, in which the surface affected by cracking is captured from a perpendicular viewpoint. From these images, crack regions are cropped into patches, and segmentation masks are annotated to delineate the fine fissures present in the crack areas [11; 12]. While this approach enables detailed analysis of the visual characteristics and precise delineation of various types of cracks, it has limitations when applied to the development of automated road crack detection systems. The second research direction focuses on crack detection specifically tailored for road surfaces. This approach utilizes annotations of road cracks captured from an ego-vehicle view, providing data that more closely resemble real-world driving scenarios. Recently, the SHREC 2022 Challenge [1] addressed this topic by organizing a task focused on detecting potholes and comprehensive cracks in road pavement imagery. The participants of the competition analyzed the challenges of road surface defect detection and proposed solutions, thereby contributing to the advancement research on deep learning-based detection systems using road-specific datasets. However, existing public datasets for road crack detection are limited in terms of both the diversity of crack categories and the availability of pixel-level dense annotations. Most of these datasets only provide bounding box annotations indicating the location of cracks within ego-vehicle images [13; 14]. EdmCrack600 [15; 16] attempted to address the limitations of front-mounted ego-vehicle views--such as obstructions caused by the vehicle hood--by mounting a GoPro camera on the rear of the vehicle. While the dataset includes segmentation annotations and real-world road surface data, its scale is limited to only 600 images. In Table 1, we compare our dataset with existing road damage datasets in terms of data types, number of classes, types of deep learning tasks applied, and dataset size. This comparison reveals that, unlike previous datasets, which often focus on specific damage types or limited tasks, the dataset constructed in this study encompasses a wider range of data types and a greater number of classes. It also demonstrates scalability and flexibility for application to various deep learning tasks, including classification and detection. To enable more practical and challenging road crack analysis, we propose a new benchmark dataset consisting of ego-vehicle images annotated with segmentation masks for six distinct crack categories.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Image Type** & **Task** & **Number of Class** & **Dataset Size** \\ \hline [PERSON], et al. [17] & Patch & SEG & 1 & 10,000 \\  & & & & (5000/2500/2500) \\ EdmCrack600 [16] & Ego-vehicle & SEG & 1 & 600 \\  & & & & (420/60/120) \\ CPRID & Ego-vehicle & SEG & 1 & 2235 \\  & & & & (2000/200/35) \\ RCDC [18] & Ego-vehicle & OD & 4 & 23,705 \\  & & & & (18,930/2111/2664) \\ RDD [19] & Ego-vehicle & OD & 8 & 9053 \\  & & & & (-) \\ Omicrack30k [6] & Patch & SEG & 1 & 30,017 \\  & & & & (22,158/3277/4582) \\ PPDD (Ours) & Ego-vehicle & SEG & 6 & 204,839 \\  & & & & (163,871/20,484/20,484) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of road crack datasets.