Structural defect segmentation using a semi-supervised algorithm integrating YOLO and the segment anything model

doi:10.1016/j.autcon.2025.106709

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Structural defect segmentation using a semi-supervised algorithm integrating YOLO and the segment anything model

2025-12-11

PRE

Gauthami Vijayakumar Kuttuva

J. Prawin *

DOI：10.1016/j.autcon.2025.106709

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Abstract

En 中文

• Two-stage semi-supervised YOLOv11–SAM framework enables fine-grained instance segmentation of civil defects. • Region-level labelling cuts annotation effort by up to 30× while preserving high segmentation accuracy. • The proposed model generalises well across datasets, outperforming supervised methods under varied textures and lighting. • Oriented boxes capture irregular cracks accurately, improving quantification and reducing augmentation needs.

Journal

Automation in Construction

IF:

11.5

Papers:

6.3K

Citations:

4.2W

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