Irregular Openings Identification at Construction Sites Based on Few-Shot Learning
Abstract
1. Introduction
2. Methodology
2.1. Framework for FSL in Construction
2.2. Dataset Development
2.3. FSL Model
2.3.1. Model Architecture
2.3.2. Meta Learning: MAML Algorithm
2.3.3. Attribute-Based Enhancement
2.4. Experimental Setup
2.4.1. Data Preparation and Splitting
2.4.2. Implementation and Hyperparameters
3. Results
3.1. Performance of the Proposed FSL Model
3.2. Comparison: FSL vs. Conventional Supervised Approach
3.3. Comparison Between the Base MAML and Advanced MAML Algorithms
4. Discussion
4.1. Potential Applications of FSL for Hazard Identification
4.2. Contributions
4.3. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
---|---|---|---|---|---|---|---|---|---|---|---|
0.0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1.0 | |
1.0 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 | 0.0 | |
Accuracy | 0.8600 | 0.8615 | 0.8624 | 0.8637 | 0.8658 | 0.8734 | 0.8830 | 0.8972 | 0.9032 | 0.8991 | 0.8828 |
K-Way N-Shot | Validation Accuracy | Recall | Precision | F1 Score | |
---|---|---|---|---|---|
K-Way | N-Shot | ||||
Two-way | 1 | 0.8680 | 0.7525 | 0.6624 | 0.6916 |
2 | 0.9086 | 0.7998 | 0.7327 | 0.7601 | |
3 | 0.9343 | 0.8747 | 0.8084 | 0.8370 | |
5 | 0.9495 | 0.9222 | 0.8276 | 0.8668 | |
10 | 0.9596 | 0.9278 | 0.8580 | 0.8888 | |
Three-way | 1 | 0.8255 | 0.6827 | 0.6494 | 0.6441 |
2 | 0.8440 | 0.7394 | 0.6956 | 0.7000 | |
3 | 0.8761 | 0.7999 | 0.7520 | 0.7570 | |
5 | 0.9054 | 0.8558 | 0.8018 | 0.8204 | |
10 | 0.9134 | 0.8832 | 0.8443 | 0.8598 |
Seed No. | Accuracy | Recall | Precision | F1 Score |
---|---|---|---|---|
Seed 1 (42) | 0.9054 | 0.8558 | 0.8018 | 0.8204 |
Seed 2 (106) | 0.8967 | 0.8413 | 0.7922 | 0.8131 |
Seed 3 (2024) | 0.9112 | 0.8625 | 0.8076 | 0.8322 |
Seed 4 (77) | 0.9031 | 0.8504 | 0.7940 | 0.8187 |
Seed 5 (9) | 0.9079 | 0.8612 | 0.7998 | 0.8284 |
Mean ± SD | 0.9049 ± 0.0050 | 0.8542 ± 0.0086 | 0.7991 ± 0.0055 | 0.8226 ± 0.0074 |
Model | Dataset | Accuracy | Recall | Precision | F1 Score |
---|---|---|---|---|---|
ResNet-50 | 15images | 0.4933 | 0.4867 | 0.5837 | 0.4606 |
EfficientNetB0 | 15images | 0.6905 | 0.6771 | 0.6902 | 0.6629 |
FSL model | 3way, 5shot | 0.9054 | 0.8558 | 0.8018 | 0.8204 |
Model | Dataset | Accuracy | Recall | Precision | F1 Score |
---|---|---|---|---|---|
Base MAML | 3way, 5shot | 0.9054 | 0.8558 | 0.8018 | 0.8204 |
CAVIA | 3way, 5shot | 0.9336 | 0.8774 | 0.8302 | 0.8516 |
Meta-SGD | 3way, 5shot | 0.9268 | 0.8699 | 0.8196 | 0.8403 |
Reptile | 3way, 5shot | 0.9221 | 0.8645 | 0.8133 | 0.8312 |
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Seo, M.; Kim, H. Irregular Openings Identification at Construction Sites Based on Few-Shot Learning. Buildings 2025, 15, 1834. https://doi.org/10.3390/buildings15111834
Seo M, Kim H. Irregular Openings Identification at Construction Sites Based on Few-Shot Learning. Buildings. 2025; 15(11):1834. https://doi.org/10.3390/buildings15111834
Chicago/Turabian StyleSeo, Minjo, and Hyunsoo Kim. 2025. "Irregular Openings Identification at Construction Sites Based on Few-Shot Learning" Buildings 15, no. 11: 1834. https://doi.org/10.3390/buildings15111834
APA StyleSeo, M., & Kim, H. (2025). Irregular Openings Identification at Construction Sites Based on Few-Shot Learning. Buildings, 15(11), 1834. https://doi.org/10.3390/buildings15111834