基于改进SSD的风机叶片表面缺陷检测方法

针对无人机对风机叶片表面缺陷检测中出现的多尺度目标问题，本文提出一种基于改进SSD的风机叶片缺陷检测方法。以具有多尺度结构框架的目标检测模型SSD为基础，引入残差网络ResNet50作为其特征提取网络，用以获取更深层次的细节特征信息，从而提升缺陷检测模型的整体效果。在建立的风机叶片表面缺陷图像数据集下进行模型验证，结果表明，该方法的平均精确度mAP@.5为84.29%，与YOLOv3和RetainNet相比，对各类型缺陷的平均精确度分别提高了2.92%和8.69%，同时较传统SSD算法平均精确度提升了2.21%。

Defect Detection of Wind Turbine Blade Based on Improved SSD

Aiming at the multi-scale target problem of detecting wind turbine blade surface defects by unmanned aerial vehicle (UAV), this paper proposes an improved SSD-based defect detection method for wind turbine blades. Based on the target detection model SSD with the multi-scale structural framework, the residual network ResNet50 is introduced as its feature extraction network to obtain deeper detailed feature information, thus improving the overall effect of the defect detection model. Model validation is performed under the established wind turbine blade surface defect image data set, and the results show that the mean average precision (mAP@.5) is 84.29%. Compared with YOLOv3 and RetainNet, it improves the average accuracy of each defect type by 2.92% and 8.69%, respectively. Moreover, it also improves the average accuracy by 2.21% compared with the traditional SSD algorithm.