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Multilabel Concrete Bridge Damage Classification Using Graph Convolution
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PRE
AI
DOI：10.1109/TIM.2024.3400345.png)
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摘要
En 中文
In recent years, the evolution of convolutional neural networks (CNNs) has paved the way for the development of deep-learning-based visual inspection techniques to identify damages in concrete bridges. However, the coexistence of diverse damage attributes within a single image complicates the comprehensive interpretation of these images during practical bridge inspections, posing a persistent challenge for existing single classification tasks. To address this issue, we introduce an innovative algorithm that synergizes graph convolutional network (GCN) and multiaxis vision transformer (MaxViT). Our approach leverages MaxViT to extract image features and subsequently models the co-occurrence correlation of labels. In addition, we use a graph CNN to extract features that capture the correlation between labels. Ultimately, we integrate these image features to enable multilabel classification of damage images. To evaluate the efficacy of our method, we curated a multilabel damage classification dataset for concrete bridges, consisting of 2098 samples encompassing the four prevalent types of spall, rebar, corrosion, and crack observed during bridge inspections. Experimental results showcase that our proposed method achieves a mean average precision (mAP) of 99.37% when applied to real bridge inspection images. In terms of general evaluation metrics, our method attains an accuracy of 98.29%, precision of 96.28%, recall of 97.08%, and F1-score of 96.68%. These findings underscore the superior performance of our method compared with current advanced multilabel classification networks, demonstrating higher accuracy. An interpretable analysis of the classification feature maps generated by our model, using the Grad-CAM algorithm, reveals that the model effectively directs attention to specific regions where damage is located. Consequently, our method holds promising engineering applications and has the potential to significantly enhance the accuracy of support provided for bridge inspection and maintenance endeavors.
Keyword:
Bridges
Inspection
Feature extraction
Classification algorithms
Accuracy
Image classification
Concrete
Concrete bridge damage classification
deep learning
feature fusion
graph convolutional network (GCN)
multilabel classification
transformer
期刊
IF:
5.9
论文数:
2.0W
被引数:
6.1W
机构
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