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LiteCrackSeg: A lightweight hybrid CNN-transformer for efficient crack segmentation
Kaleb Amsalu Gobena1, Md Youshuf Khan Rakib1, Fiseha Berhanu Tesema2
1School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
Plos One
|April 30, 2026
Summary
LiteCrackSeg, a new lightweight AI model, accurately detects infrastructure cracks using a hybrid CNN-transformer approach. This efficient system enables real-time structural health monitoring on edge devices.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Structural Engineering
Background:
- Infrastructure cracks are key indicators of structural deterioration.
- Automated crack segmentation is vital for structural health monitoring.
- Challenges include thin, low-contrast cracks and class imbalance.
Purpose of the Study:
- To develop an efficient and accurate crack segmentation model for resource-constrained devices.
- To address the challenges of crack morphology and class imbalance in segmentation.
Main Methods:
- Proposed LiteCrackSeg, a lightweight hybrid CNN-transformer architecture.
- Utilized a MobileViT encoder for local and global feature extraction.
- Introduced a Morphology-Aware MobileViT (MAM-ViT) bottleneck with Dynamic Snake Convolutions (DSConv).
- Employed a transformer-based decoder and attention-guided fusion.
- Trained using Tversky loss to handle class imbalance.
Main Results:
- Achieved state-of-the-art segmentation performance on DeepCrack, CrackMap, and TUT datasets.
- Demonstrated high computational efficiency with 2.72M parameters and 3.23 GFLOPs.
- Real-time inference at 56 FPS on 512x512 images.
Conclusions:
- LiteCrackSeg offers an efficient and accurate solution for infrastructure crack segmentation.
- The model's lightweight design is suitable for deployment on edge devices for practical inspection.
- Enables advanced structural health monitoring in real-world applications.
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