CrackDINO: A DINOv3-based Hybrid Framework for Fine-Grained Crack Segmentation

Abstract

Accurate pavement crack segmentation is essential for intelligent transportation systems and infrastructure maintenance. However, due to the low contrast, complex background interference, and elongated structural characteristics of cracks, existing segmentation methods often suffer from discontinuous predictions and missed detection of tiny crack regions. In particular, CNN-based methods are limited in capturing long-range dependencies, while Transformer-based methods tend to lose fine-grained spatial details during patch tokenization. To address these challenges, we propose a DINOv3-based crack segmentation framework termed CrackDINO. Specifically, we design an RGB-guided Multi-scale Feature Pyramid (RGMFP) module to enhance hierarchical semantic interaction across different feature resolutions. In addition, a Crack-aware Stable Attention (CAS) module is introduced to strengthen weak crack responses and improve discriminative representation for thin and low-contrast crack regions. Furthermore, a Cascaded Hierarchical Multi-scale Decoder (CHMD) is proposed to progressively recover spatial details and preserve crack continuity during feature reconstruction. Extensive experiments on the Crack500 and CrackForest Dataset indicate that the proposed method achieves competitive performance compared with several representative segmentation models, including U-Net, DeepLabV3+, SegFormer, TransUNet, and Swin-Unet. On the Crack500 Dataset, CrackDINO achieves an mIoU of 62.61\% and an F1-score of 74.16\%. On the CrackForest Dataset, the proposed method obtains an mIoU of 60.08\% and an F1-score of 74.79\%, demonstrating favorable robustness and generalization performance for fine-grained crack segmentation.