The classification method of small sample tower surface defects based on improved relational network

Abstract

Abstract: To implement the intelligent detection of surface defects on wind turbine tower, a small-sample tower surface defect classification method based on an improved relational network is proposed. The method integrates a residual network with a relation network, where the increased network depth enhances the model’s feature representation capability. An infinite-order continuous, non-monotonic activation function is used in the nonlinear transformation phase, and a general adaptive robust loss function is used in the training phase. The model is tested and validated on a real image dataset of wind turbine tower surface, achieving a classification accuracy of 90.26%, which is 4.57% better than the original model. The accuracy and reliability of the proposed network model meet the current requirements of wind turbine tower condition monitoring and operation, and maintenance.

Key words: wind turbine tower, relation network, few-shot learning, surface defects

CLC Number:

TK83

ZHENG Yu-qiao, JIANG Dong-yu, DONG Fu-gang, ZHANG He-yu. The classification method of small sample tower surface defects based on improved relational network[J]. Journal of Lanzhou University of Technology, 2025, 51(6): 49-54.

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