An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model
Abstract
1. Introduction
2. Methodology
2.1. Data Acquisition and Pre-Processing
2.1.1. Image Acquisition
2.1.2. Image Pre-Processing
2.1.3. Data Labeling
2.2. Particle Segmentation Based on YOLOv8-Seg-CBAM-SE
2.2.1. Overview of YOLOv8-Seg
2.2.2. YOLOV8-Seg-CBAM-SE
2.2.3. Model Pre-Training and Fine-Tuning
2.2.4. Model Evaluation Criteria
2.3. Particle Gradation Detection
3. Model Training and Testing
3.1. Dataset Establishment
3.2. Training Environment
3.3. Testing and Assessment
4. Experiments and Results
4.1. Testing Material
4.2. Particle Segmentation
4.3. Gradation Calculation
5. Discussion
5.1. Shooting Parameter Constraints
5.2. Impact of Transfer Learning
5.3. On-Site Implementation Procedure
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
PGD | Particle gradation detection |
CV | Computer vision |
DIP | Digital image processing |
DL | Deep learning |
IIS | Image instance segmentation |
YOLO | You Only Look Once |
SE | Squeeze and excitation |
MAR | Minimum area rectangle |
GIS | Geographic information system |
SNR | Signal-to-noise ratio |
MAE | Mean absolute error |
ISAT-SAM | Interactive Segment Anything Tool–Segment Anything Model |
CBAM | Convolutional block attention module |
P | Precision |
R | Recall |
mAP | Mean average precision |
TP | True positives |
FP | False positives |
FN | False negatives |
IoU | Intersection over union |
PR | Precision–recall |
AABB | Axis-aligned bounding box |
RMSE | Root mean square error |
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Data Set | Numbers of Images | ||
---|---|---|---|
Group I (Figure 14a) | Group II (Figure 14b) | Group III (Figure 14c) | |
Training set | 355 | 559 | 1780 |
Validation set | 104 | 201 | 446 |
Testing set | - | - | 560 |
Model | Precision | Recall | IoU | mAP50 | mAP50-95 | Time (s) |
---|---|---|---|---|---|---|
Mask R-CNN | - | - | 0.915 | 0.870 | - | 1.57 |
Mask R-CNN-SE | - | - | 0.916 | 0.899 | - | 1.81 |
YOLACT | - | - | 0.720 | 0.591 | 0.178 | 0.26 |
SOLOv2 | - | - | 0.930 | 0.955 | 0.851 | 0.39 |
YOLOv8-seg | 0.915 | 0.934 | 0.924 | 0.975 | 0.913 | 0.35 |
YOLOv8-seg-CBAM-SE | 0.923 | 0.935 | 0.928 | 0.978 | 0.917 | 0.36 |
Model | Precision | Recall | IoU | mAP50 | mAP50-95 | Time (s) |
---|---|---|---|---|---|---|
Mask R-CNN | - | - | 0.883 | 0.773 | - | 2.50 |
Mask R-CNN-SE | - | - | 0.897 | 0.805 | - | 2.75 |
YOLACT | - | - | 0.678 | 0.417 | 0.109 | 0.36 |
SOLOv2 | - | - | 0.920 | 0.935 | 0.809 | 0.50 |
YOLOv8-seg | 0.885 | 0.896 | 0.902 | 0.955 | 0.803 | 0.45 |
YOLOv8-seg-CBAM-SE | 0.905 | 0.899 | 0.910 | 0.961 | 0.857 | 0.44 |
Model | Parameters | Gradients | Training Time (s) | Batch Size |
---|---|---|---|---|
Mask R-CNN | 44M | 44M | 507 | 4 |
Mask R-CNN-SE | 44M | 44M | 508 | 4 |
YOLACT | 30.74M | 30.74M | 40 | 4 |
SOLOv2 | 65.22M | 65.22M | 47 | 4 |
YOLOv8-seg | 3.26M | 3.26M | 4.05 | 16 |
YOLOv8-seg-CBAM-SE | 3.28M | 3.28M | 4.07 | 16 |
Shooting Distance | Shooting Angle | Imaging Device Pixel Requirement |
---|---|---|
40 cm–80 cm | ≤20° | >3000 × 3000 pixels |
Weight | IoU | mAP50 | mAP50-95 |
---|---|---|---|
Pre-trained weight | 0.903 | 0.939 | 0.805 |
Fine-tuned weight | 0.910 | 0.961 | 0.857 |
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Tang, Y.; Zhao, S.; Qin, H.; Ming, P.; Fang, T.; Zeng, J. An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model. Sensors 2025, 25, 4797. https://doi.org/10.3390/s25154797
Tang Y, Zhao S, Qin H, Ming P, Fang T, Zeng J. An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model. Sensors. 2025; 25(15):4797. https://doi.org/10.3390/s25154797
Chicago/Turabian StyleTang, Yu, Shixiang Zhao, Hui Qin, Pan Ming, Tianxing Fang, and Jinyuan Zeng. 2025. "An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model" Sensors 25, no. 15: 4797. https://doi.org/10.3390/s25154797
APA StyleTang, Y., Zhao, S., Qin, H., Ming, P., Fang, T., & Zeng, J. (2025). An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model. Sensors, 25(15), 4797. https://doi.org/10.3390/s25154797