【摘要】 针对传统风机桨叶检测算法在复杂环境存在误检及检测精度低的问题，提出一种融合多尺度特征与注意力机制的风机桨叶缺陷检测方法。首先使用改进的骨干网络L-ResNet50进行特征提取，保留更多有效信息；然后对不同尺度特征层嵌入注意力机制模块，增强重点语义信息；最后对提取出的深层特征与浅层特征进行多尺度特征融合，进一步提高模型准确率。通过对无人机航拍采集的风机桨叶图像进行缺陷检测实验，结果表明，所提方法在复杂环境下的风机桨叶缺陷检测中平均准确率较原Faster R-CNN模型提高8.2%。更多还原

【Abstract】 To address the problems of false detection and low detection accuracy of traditional wind turbine paddle detection algorithms in complex environments, a wind turbine paddle defect detection method integrating multi-scale features and attention mechanism is proposed. Firstly, the improved backbone network L-ResNet50 is used for feature extraction to retain more effective information. Then the attention mechanism module is embedded for different scale feature layers to enhance the focused semantic information. Finally, the extracted deep features and shallow features are fused with multi-scale features to further improve the model accuracy. Through the defect detection experiments on the wind turbine paddle images captured by UAV aerial photography, the results show that the average accuracy of the proposed method in the detection of wind turbine paddle defects in complex environments is improved by 8.2% compared with the original Faster R-CNN model.更多还原