## 4 Conclusions  
The WTB surface damage identification is an important part of wind turbine maintenance, and there are some relevant identification methods. However, the identification methods based on CNNs have the advantages of low cost, simple operation, and high recognition accuracy, and have therefore attracted the attention of many scholars. With continuous in-depth research, efficient and novel CNN-related structures continue to emerge. For example, the AC Block proposed in 2019 uses asymmetric convolution to obtain better feature extraction capabilities. The CBAM proposed in 2018 applies a novel channel and spatial combined attention mechanism, further improving the feature selection ability. Therefore, this article draws on the valuable ideas of the former two. Additionally, according to the actual task requirements, we make improvements to the problems of the number of parameters of the AC Block needing to be reduced and CBAM lacking multi-scale perception of the damaged space location. A lightweight CNN model of LACB_ECBAM Net with LAC Block and E_CBAM as the main highlights are proposed for the WTB surface damage identification. The article conducted three related experiments on LACB_ECBAM Net, and the conclusions are as follows:  
1. The LACB_ECBAM network is effective for the WTB surface damage identification task. On the dataset proposed in this article, containing 4729 images and 3 categories, the accuracy reached 99.94\(-\)99.96%, which can meet the practical engineering needs. 2. The LAC Block and E_CBAM used by LACB_ECBAM Net have outstanding generalization ability. On the present study task, AlexNet improved by LAC Block showed a 1.36% improvement in accuracy, 0.77% improvement in recall, 1.86% improvement in precision, and about 2.33 M reduction in the number of parameters. Comparatively, AlexNet improved by E_CBAM showed a 6.32% improvement in accuracy, 6.00% improvement in recall, and 6.12% improvement in precision. 3. LACB_ECBAM Net has excellent comprehensive performance in the task of WTB surface damage identification. It was superior to the other CNNs participating in the experiment, such as Xception, in terms of accuracy, recall, and the number of parameters. However, LACB_ECBAM Net also has shortcomings, such as precision needing to be improved and training time needing to be further reduced, which are problems to be solved in our follow-up work. Conceptualization, H.C.; methodology, H.C. and Q.S. ; validation, L.Z. ; formal analysis, L.Z. ; investigation, H.C.; resources, H.C.; data curation, H.C. and Q.S. ; 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. **Funding:** This research was funded by the National Natural Science Foundation of China under Grant 52005071 and the Applied Basic Research Program Project of Liaoning Province under Grant 2023 JHz/101300236. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** 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/KaKoYu007/Wind-turbine-blade)), accessed on 10 April 2023. **Conflicts of Interest:** The authors declare no conflict of interest. Figure A1: Structure diagram of AlexNet’s modifications. (**a**) Original AlexNet. (**b**) AlexNet + AC Block. (**c**) AlexNet + LAC Block. (**d**) AlexNet + CBAM. (**e**) AlexNet + E_CBAM. (**f**) AlexNet + LAC Block + E_CBAM. Figure A2: Main structure diagram of the six lightweight networks. (**a**) ResNeXt50 (32 \(\times\) 4d). (**b**) Xception. (**c**) MobileNet_V2. (**d**) ShuffleNet_V2.