## 4 Conclusions  
Damage identification is of great importance for the maintenance of the wind turbine blade. Observation under weather, light, angle, and other external interference conditions make it difficult to distinguish minute differences in the characteristics of damage to wind turbine blades. In this paper, an improved ED Net model based on the CNN model is proposed to address the issues in the current damage identification methods of the wind turbine blade. It balances cost, complexity, and accuracy by introducing an improved EAC block based on asymmetric convolution and an improved DPCI_SE block based on the SE block.  
EAC Block highlights the main feature information obtained by convolution by increasing the weight of the convolution kernel backbone position in the convolution process and further improves the nonlinearity of the convolution module by adding a convolution layer with a convolution kernel of 1 before the convolution branch. DPCI_SE Block primarily enriches the spatial information of features by adding parallel structures of average pooling and maximum pooling before the SE block so that the SE block can enhance the effective feature information and suppress the ineffective or weak feature information to a greater extent.  
In this paper, three sets of experiments are conducted. Experiment 1 confirmed the efficacy of the ED Net model for identifying damaged wind turbine blades. Experiment 2 confirmed the efficacy of the relevant enhancements proposed in this work. In addition, based on the analysis of experimental data, the enhancement efficiency of EAC Block is insufficient and requires further development. Experiment 3 compares the damage identification of wind turbine blades by commonly used lightweight networks and demonstrates that the ED Net model proposed in this paper provides superior overall performance. Experiments results show that the ED Net model has an accuracy range of 99.12% to 99.23% and a recall of 99.23%, which can be applied to actual work scenarios. Future research will investigate the light-weighting of ED Net using techniques such as deep separable convolution.  
Conceptualization, H.C.; methodology, H.C.; validation, L.Z.; formal analysis, L.Z.; investigation, H.C.; resources, H.C.; data curation, H.C.; writing--original draft preparation, L.Z. and H.C.; writing--review and editing, L.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 52005071 and the Liaoning Provincial Educational Department Project under Grant JDL2020004.  
Not applicable.  
The datasets analyzed during the current study are available from the corresponding author upon reasonable request. The data that support the findings of this study are openly available in \"Wind-turbine-blade\" at [[https://github.com/KaKoYu007/Wind-turbine-blade](https://github.com/KaKoYu007/Wind-turbine-blade)]([https://github.com/KaKoYu007/Wind-turbine-blade](https://github.com/[PERSON]/Wind-turbine-blade)), accessed on 10 April 2022.  
The authors declare no conflict of interest.