Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network
Abstract
:1. Introduction
2. Related Work
3. Methodology
3.1. Proposed Model Architecture
3.2. Hybrid Pooling
3.3. Attention Network
4. Implementation
4.1. Establishing Concrete Surface Damage Dataset
4.2. Experimental Settings
5. Results and Discussion
5.1. Performance Evaluation Metrics
5.2. Experimental Results
6. Conclusions and Scope for Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Models | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
---|---|---|---|---|
AlexNet_avg * | 0.97449 | 0.97449 | 0.974490 | 0.97449 |
VGG16_avg * | 0.97959 | 0.97943 | 0.974489 | 0.97692 |
ResNet50 | 0.94879 | 0.95408 | 0.948979 | 0.95149 |
InceptionV3 | 0.94879 | 0.94898 | 0.948979 | 0.94898 |
MobileNetV2 | 0.92347 | 0.92824 | 0.923469 | 0.92582 |
Proposed model—CMDnet | 0.98980 | 0.98980 | 0.989780 | 0.98978 |
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Shin, H.K.; Ahn, Y.H.; Lee, S.H.; Kim, H.Y. Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network. Materials 2020, 13, 5549. https://doi.org/10.3390/ma13235549
Shin HK, Ahn YH, Lee SH, Kim HY. Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network. Materials. 2020; 13(23):5549. https://doi.org/10.3390/ma13235549
Chicago/Turabian StyleShin, Hyun Kyu, Yong Han Ahn, Sang Hyo Lee, and Ha Young Kim. 2020. "Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network" Materials 13, no. 23: 5549. https://doi.org/10.3390/ma13235549