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Crack instance segmentation using splittable transformer and position coordinates
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AI
DOI：10.1016/j.autcon.2024.105838.png)
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Abstract
En 中文
Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model's computational load. To handle varying segmentation challenges, a range of models with different computational demands has been designed to suit diverse needs. The most lightweight model can be deployed for real-time use on edge devices. A module in the neck of the pipeline encodes crack coordinate information, and end-to-end training has resulted in state-of-the-art performance across multiple datasets.
Keywords:
Intelligence city construction
Crack instance segmentation
Splittable transformer
Re-parameterization
Coordinate module
Crack location segmentation transformer
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11.5
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6.2K
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4.2W
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