# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 5 Discussion
### Future Directions  
Our new benchmark dataset introduces several promising directions for future research in road crack detection and analysis. We organize the future directions into two broad categories: (1) domain-specific challenges within the expanded taxonomy of crack types, and (2) integration with emerging techniques in deep learning and computer vision. The high-quality segmentation annotations provided in PPDD offer detailed information on the extent of damage, enabling the development of more practical and real-world-applicable crack segmentation models. The dataset includes a diverse set of crack categories, contributing to the analysis of visual characteristics associated with different causes of pavement damage. First, it would be worthwhile to explore methods for enhancing model sensitivity to visually subtle cracks with specific directional characteristics, such as reflection cracks. These cracks frequently appear perpendicular to the driving lane and often exhibit low visual contrast with nearby road markings, making them prone to misclassification. To address this challenge, architectural improvements that explicitly encode directional information and effectively learn spatial context may serve as effective solutions. Proposed strategies include adapting transformer-based architectures to better capture global contextual features [39; 40], as well as applying connected component-based post-processing techniques for prediction refinement [41]. These approaches aim to improve model robustness for complex crack types by leveraging both structural alignment and contextual dependencies inherent in road defect imagery. While prior work [42] has considered an mIoU of 0.5 to represent reasonably sufficient performance, our model achieves an mIoU of 48.31 for the rarest class in the dataset. Despite the extremely limited training opportunities for this class, the model is still able to capture meaningful patterns, indicating that it has learned to recognize the structural characteristics of even the most infrequent crack types. Future work applying a broader range of model architectures beyond the proposed baselines may further improve segmentation performance. Additionally, the use of ensemble techniques or hybrid architectures could enhance robustness across diverse crack types. Incorporating advanced data augmentation strategies and class balancing techniques [43] may also help mitigate issues arising from class imbalance and limited training samples, particularly for rare crack categories. Together, these approaches could lead to more accurate and generalizable models for real-world crack segmentation tasks. Moreover, although the model maintained reasonable performance under overcast conditions, the number of such samples was limited, and thus, a detailed condition-specific performance analysis was not included in this study. In future work, expanding annotations related to environmental factors such as weather and lighting, and introducing condition-aware evaluation protocols will be crucial to further improve model robustness under varying real-world scenarios. To support real-world applications, we also plan to expand the dataset with more comprehensive weather variation annotations. Secondly, recent advances in deep learning-based perception have increasingly focused on training predictive models under challenging constraints. This includes scenarios where only a limited number of accurate annotations are available, where annotations are weak [44; 45] or incomplete [46; 47] or where domain gaps [48] arise between training and deployment environments. The proposed crack segmentation dataset is well positioned to support emerging research directions driven by the expansion of deep learning technologies. One particularly promising avenue is the integration of vision-language models (VLMs), such as CLIP [49], which have been trained on large-scale image-text datasets.