An End-to-End Particle Gradation Detection Method for Earth-Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a fast, non-destructive method for analyzing rockfill particle size distribution using mobile photos and AI. The approach automates on-site screening, reducing costs and improving accuracy in dam construction.
Area Of Science
- Geotechnical Engineering
- Civil Engineering
- Computer Vision
Background
- Particle gradation is crucial for earth-rockfill dam mechanical performance.
- Current on-site screening methods are slow, labor-intensive, and invasive.
Purpose Of The Study
- To develop a rapid, non-destructive method for rockfill particle gradation analysis.
- To automate on-site screening using mobile photography and image segmentation.
Main Methods
- Utilized mobile-based photography and an end-to-end image segmentation approach.
- Employed an enhanced YOLOv8-seg model with a dual-attention mechanism, pre-trained on lab images and retrained using transfer learning on site images.
- Integrated a Minimum Area Rectangle algorithm for gradation computation.
Main Results
- Achieved high segmentation accuracy with mAP50 scores of 97.8% (base) and 96.1% (on-site).
- Successfully segmented densely stacked, adhered, and overlapped particles of various sizes.
- Gradation results closely matched manual screening outcomes.
Conclusions
- The proposed method offers a significant advancement in automating construction workflows for earth-rockfill dams.
- This approach reduces labor costs, minimizes structural disruption, and ensures reliable measurement quality.
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