Abstract: In order to effectively detect the damage state of wind turbine blades, this paper proposes a wind turbine blade damage detection and recognition technology based on the CenterNet target detection algorithm. The DLA-60 feature extraction network is selected as the backbone network of the CenterNet algorithm, and the attention - guided data enhancement mechanism is introduced in the DLA-60 network to improve the accuracy of the detection algorithm. Experiments show that the optimized wind turbine blade damage detection and recognition model has a detection accuracy of 88%, which is 2.6% higher than the original algorithm, and the detection time is basically the same as the original network, which has great practicability and accuracy.