  The safety of wind turbines is a prerequisite for the stable operation of
offshore wind farms. However, bird damage poses a direct threat to the safe
operation of wind turbines and wind turbine blades. In addition, millions of
birds are killed by wind turbines every year. In order to protect the
ecological environment and maintain the safe operation of offshore wind
turbines, and to address the problem of the low detection capability of current
target detection algorithms in low-light environments such as at night, this
paper proposes a method to improve the network performance by integrating the
CBAM attention mechanism and the RetinexNet network into YOLOv5. First, the
training set images are fed into the YOLOv5 network with integrated CBAM
attention module for training, and the optimal weight model is stored. Then,
low-light images are enhanced and denoised using Decom-Net and Enhance-Net, and
the accuracy is tested on the optimal weight model. In addition, the k-means++
clustering algorithm is used to optimise the anchor box selection method, which
solves the problem of unstable initial centroids and achieves better clustering
results. Experimental results show that the accuracy of this model in bird
detection tasks can reach 87.40%, an increase of 21.25%. The model can detect
birds near wind turbines in real time and shows strong stability in night,
rainy and shaky conditions, proving that the model can ensure the safe and
stable operation of wind turbines.
