Infrastructure cracks are critical indicators of structural deterioration in pavements, bridges, and buildings. Automated crack segmentation has therefore become an important component of structural health monitoring systems. However, accurate pixel-level crack segmentation on resource-constrained devices remains challenging due to the thin, low-contrast, and curvilinear morphology of cracks, as well as severe foreground-background class imbalance. To address these challenges, we propose LiteCrackSeg, a lightweight hybrid CNN-transformer architecture designed for efficient and accurate crack segmentation. The proposed framework adopts a hybrid MobileViT encoder that captures both local spatial details and long-range contextual dependencies while maintaining a compact model size. To enhance morphological sensitivity to elongated crack structures, we introduce a Morphology-Aware MobileViT (MAM-ViT) bottleneck, which integrates dual-branch Dynamic Snake Convolutions (DSConv) to align receptive fields with crack trajectories. Furthermore, a transformer-based decoder with local self-attention progressively reconstructs spatial details, while an attention-guided multi-scale fusion strategy improves boundary precision and structural continuity. To mitigate severe class imbalance, the model is trained using the Tversky loss, which explicitly balances false positives and false negatives. Extensive experiments on three public crack segmentation datasets (DeepCrack, CrackMap, and TUT) demonstrate that LiteCrackSeg achieves state-of-the-art segmentation performance while maintaining high computational efficiency. The proposed model requires only 2.72M parameters and 3.23 GFLOPs, achieving real-time inference at 56 FPS on 512 × 512 images, making it suitable for deployment on resource-constrained edge devices for practical infrastructure inspection.
Gobena et al. (Thu,) studied this question.