# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
###### Abstract  
Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects--such as slippage, rutting, and construction-related cracks--and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an \(AP_{50}\) of 87.2% and \(AP\) of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository. s 2  
as improved traffic safety, reduced vehicle maintenance costs, and enhanced pedestrian convenience [3]. Traditional pavement inspection methods have mainly relied on visual assessments by human inspectors or semi-automated techniques, where road surface measurements are collected using automated equipment and later reviewed manually. These methods are inherently limited by their reliance on subjective judgment [4] and often involve considerable time and financial costs. In addition, inspectors must occupy and control traffic lanes during inspections, increasing the risk of accidents and traffic disruptions. To overcome these limitations, deep learning-based detection models have gained traction in recent years, significantly advancing automated crack detection technologies [1]. However, training such models typically requires densely annotated crack labels, which poses a major barrier to large-scale adoption. Consequently, existing studies have mostly relied on bounding box annotations for entire images [5] or segmentation masks applied at the patch level (e.g., cropped images of cracks) [1,6]. In this study, we propose a novel benchmark dataset for deep learning-based crack detection. The dataset includes previously under-represented crack types such as slippage, rutting, and construction but also provides polygon-based segmentation masks for enhanced annotation precision. Unlike existing crack segmentation datasets, which often rely on orthographic imaging--capturing cracks from a perpendicular viewpoint on surfaces such as bridges or building exteriors--our dataset adopts an egocentric acquisition method, capturing road cracks from the viewpoint of a moving vehicle. This acquisition setup allows the dataset to include oblique views and real-world perspective distortions, thereby more accurately reflecting the visual conditions encountered in practical, on-road applications. In particular, we constructed a new benchmark segmentation dataset for road cracks and expanded the crack taxonomy to encompass a wider variety of crack types. Furthermore, by conducting a comprehensive evaluation of various segmentation architectures using the proposed dataset, we demonstrated both its practical utility and its potential as a standardized benchmark for future research.