Resumen
Wind turbine blades are easily affected by the working environment and often show damage features such as cracks and surface shedding. An improved convolution neural network, ED Net, is proposed to identify their damage features. An EAC block based on the improved asymmetric convolution is introduced which strengthens the feature extraction during convolution. A DPCI_SC block, which is improved based on the attention module, is embedded to enhance the ability to obtain spatial location information of the damage. GELU is used as the activation function. The loss function is smoothed and labeled during training. Finally, three sets of experiments were conducted. Experiment 1 confirmed the efficacy of the ED Net for identifying damaged wind turbine blades. Experiment 2 confirmed the efficacy of the relevant improvements proposed in this work. Experiment 3 compares the recognition of wind turbine blade damage by commonly used lightweight networks and shows that the ED Net model proposed has a better performance with an accuracy range of 99.12% to 99.23% and a recall of 99.23%