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%。
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.
作者
仝卫国
仪小龙
李冰
杨珂
Tong Weiguo;Yi Xiaolong;Li Bing;Yang Ke(Department of Automation,North China Electric Power University,Baoding 071003,China)
出处
《电子测量技术》
北大核心
2022年第24期166-172,共7页
Electronic Measurement Technology
基金
国家自然科学基金联合基金项目重点支持项目(U21A20486)资助
关键词
风机桨叶
注意力机制
多尺度特征融合
特征金字塔
缺陷检测
wind turbine blades
attention mechanism
multi-scale feature fusion
feature pyramid
defect detection
作者简介
仝卫国,副教授,主要研究方向为先进流量测量。E-mail:twg@163.com;仪小龙,硕士研究生,主要研究方向为缺陷检测与深度学习。E-mail:yxlxt@ncepu.edu.cn;通信作者:李冰,讲师,主要研究方向为数字图像处理与机器视觉。E-mail:52651520@ncepu.edu.cn;杨珂,硕士研究生,主要研究方向为目标检测与深度学习。E-mail:yangke@ncepu.edu.cn
