A Spatial Contexts-Informed Self-Supervised Learning Approach for Pavement Distress Segmentation

Detection and repair of pavement distress in time are crucial to maximize functional performance and service life, while minimizing maintenance costs on extensive roadway networks. Manual distress detection is labor intensive and error prone. While deep learning techniques offer unparalleled capabilities for automated and accurate pixel-level pavement distress segmentation, their reliance on extensive manual annotations remains a bottleneck. To address this challenge, we propose an open-ended self-supervised framework enabling flexible integration of various pretext tasks for pavement distress segmentation without manual annotations. We introduce a spatial contexts-informed pretext task that automatically generates pseudo labels by leveraging the highly consistent semantic information inherent across continuous pavement images within localized areas. Amulti-line parallel network architecture is then employed, where each line extracts a distinct deep representation aligned with the pseudo-label generation process. These representations are jointly optimized through a shared weight update scheme augmented by momentum encoders to capture long-range dependencies. Avision transformer processes the input images during inference, utilizing self-attention to highlight distressed regions based on the learned representations for precise segmentation. Extensive evaluations validate the performance of our framework, outperforming state-of-the-art self-supervised methods by 0.075 mIoU on average, while remarkably surpassing weakly supervised techniques requiring manual image-level annotations. These results are far more promising given that our self-supervised approach avoids human labeling costs, striking a trade-off between model effectiveness and annotation efficiency for large-scale deployments. It helps transportation agencies to realize timely, proactive infrastructure maintenance through scalable, accurate distress monitoring over extensive road networks.