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.

翻译：风力发电机组的安全性是其稳定运行的前提。然而，鸟类碰撞对风力发电机及其叶片的安全运行构成直接威胁，且每年有数百万只鸟类因风机撞击而死亡。为保护生态环境、保障海上风机安全运行，并解决当前目标检测算法在夜间等低光照环境下检测能力不足的问题，本文提出一种通过将CBAM注意力机制和RetinexNet网络集成到YOLOv5中来提升网络性能的方法。首先，将训练集图像输入集成CBAM注意力模块的YOLOv5网络进行训练，并存储最优权重模型。随后，利用Decom-Net和Enhance-Net对低光照图像进行增强和去噪处理，并在最优权重模型上测试其精度。此外，采用k-means++聚类算法优化锚框选择方法，解决了初始质心不稳定的问题，获得了更优的聚类效果。实验结果表明，该模型在鸟类检测任务中的准确率可达87.40%，相比基线提升了21.25%。该模型能够实时检测风机附近的鸟类，并在夜间、雨天及抖动条件下展现出强稳定性，验证了其可保障风电机组安全稳定运行的能力。