Crack orientation recognition method based on prototype learning

Ankang Liu; Lan Cheng; Yingchun Lv; Yan Chen; Weiyan Shi

doi:10.1117/12.2681013

8 June 2023 Crack orientation recognition method based on prototype learning

Ankang Liu, Lan Cheng, Yingchun Lv, Yan Chen, Weiyan Shi

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073Q (2023) https://doi.org/10.1117/12.2681013
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

To address the problem that the direction of pipe cracks is difficult to detect, a crack direction recognition method based on prototype learning is proposed with a prototype network as a framework. First, through the convolutional layer of the prototype network, shallow features of crack directions are extracted to improve the generalization ability of the model on the data set of this paper. then, by improving the High-Resolution Network and introducing a location self-attention mechanism, and combined with a migration training method for the data set of this paper, a category that can accurately reflect the crack directions is constructed prototype learning mechanism. Finally, pattern recognition is performed by the metric classification methods, the effective classification of crack direction under small sample condition is achieved. The experimental results show that the recognition accuracy of the crack direction recognition method based on prototype learning can reach 99.2% with the sample parameters unchanged.

Citation Download Citation

Ankang Liu, Lan Cheng, Yingchun Lv, Yan Chen, and Weiyan Shi "Crack orientation recognition method based on prototype learning", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073Q (8 June 2023); https://doi.org/10.1117/12.2681013

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KEYWORDS

Data modeling

Prototyping

Education and training

Statistical modeling

Feature fusion

Performance modeling

Convolution

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