Lightweight defect detection algorithm of tunnel lining based on knowledge distillation

Due to the influence of construction quality, engineering geology and hydrological environment, defects such as dehollowing and insufficient compaction can occur in tunnels. Aiming at the problems of complex detection model, poor real-time performance and low accuracy of the current tunnel lining de...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 27178 - 16
Hauptverfasser: Zhu, Anfu, Xie, Jiaxiao, Wang, Bin, Guo, Heng, Guo, Zilong, Wang, Jie, Xu, Lei, Zhu, SiXin, Yang, Zhanping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 08.11.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
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639/166/987
Accuracy
Algorithms
Deep learning
Distillation
Engineering geology
Geology
Humanities and Social Sciences
Knowledge distillation
Model compression algorithms
multidisciplinary
Science
Science (multidisciplinary)
Tunnel detection
Tunnel linings
Deep learning
Tunnel detection
Knowledge distillation
Model compression algorithms
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Due to the influence of construction quality, engineering geology and hydrological environment, defects such as dehollowing and insufficient compaction can occur in tunnels. Aiming at the problems of complex detection model, poor real-time performance and low accuracy of the current tunnel lining defect detection methods, the study proposes a lightweight defect detection algorithm of tunnel lining based on knowledge distillation. Firstly, a high-precision teacher model based on yolov5s was constructed by constructing a C3CSFM module that combines residual structure and attention mechanism, a MDFPN network structure with multi-scale feature fusion and a reweighted RWNMS re-screening mechanism. Secondly, in the distillation process, the feature and output dimension results are fused to improve the detection accuracy, and the mask feature relationship is learned in the space and channel dimension to improve the real-time detection. Tests on the tunnel lining radar defect image dataset showed that the number of parameters of the improved model was reduced from 16.03 MB to 3.20 MB, a reduction of 80%, and the average accuracy was improved from 83.4 to 86.5%, an increase of 3.1%. On the basis of maintaining the structure and detection performance of the model, the lightweight degree of the model is greatly improved, and the high-precision and real-time detection of tunnel lining defects is realized.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-77404-8