Surface Damage Identification of Wind Turbine Blade Based on Improved Lightweight Asymmetric Convolutional Neural Network

Wind turbine blades are readily damaged by the workplace environment and frequently experience flaws such as surface peeling and cracking. To address the problems of cumbersome operation, high cost, and harsh application conditions with traditional damage identification methods, and to cater to the wide application of mobile terminal devices such as unmanned aerial vehicles, a novel lightweight asymmetric convolution neural network is proposed. The network introduces a lightweight asymmetric convolution module based on the improved asymmetric convolution, which applies depthwise separable convolution and channel shuffle to ensure efficient feature extraction capability while achieving a lightweight design. An enhanced Convolutional Block Attention Module (CBAM) embedded with a spatial attention module with a selective kernel, enhances the acquisition of spatial locations of damage features by combining multi-scale feature information. Experiments are carried out to verify the efficacy and the generalizability of the network proposed for the recognition task. A comparison experiment of common lightweight networks based on transfer learning is also conducted. The experimental results show that the lightweight network proposed in this article has better experimental metrics, including 99.94% accuracy, 99.88% recall, and 99.92% precision.