Summary of Continual-learning-based Framework For Structural Damage Recognition, by Jiangpeng Shu et al.
Continual-learning-based framework for structural damage recognition
by Jiangpeng Shu, Jiawei Zhang, Reachsak Ly, Fangzheng Lin, Yuanfeng Duan
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed continual-learning-based damage recognition model (CLDRM) integrates the learning without forgetting method into a ResNet-34 architecture for recognizing damages in reinforced concrete structures. The CLDRM addresses issues like catastrophic forgetting, training inefficiency, and large memory requirements by reducing prediction time and data storage by 75%. The framework outperforms other methods through gradual feature fusion, achieving high accuracy in damage recognition and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to recognize damages in buildings made of concrete. It’s like teaching an AI to identify different types of damage without it forgetting what it learned before. They used a special type of neural network and showed that their method works better than others, remembering previous tasks well even when learning new ones. This is important for construction because it can help save time and money by quickly identifying problems. |
Keywords
» Artificial intelligence » Classification » Continual learning » Neural network » Resnet
