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A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification
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AI
DOI：10.1108/ECAM-06-2023-0613.png)
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Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 x 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.
Keyword:
Automatic crack detection
Crack feature quantification
Transformer
TransUnet
Deep learning
期刊
J
IF:
5.1
论文数:
5.1K
被引数:
1.4W
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Review on computer vision-based crack detection and quantification methodologies for civil structures基于计算机视觉的土木结构裂缝检测与量化方法综述
PREAI
分享收藏分享收藏A critical review and comparative study on image segmentation-based techniques for pavement crack detection基于图像分割的路面裂缝检测技术综述与比较研究
PREAI
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