基于改进SSD的风机叶片缺陷检测

Defect detection of wind turbine blade based on improved SSD

针对传统算法检测精度低、耗时长的问题,本文提出一种基于改进SSD的风机叶片缺陷检测方法。首先,采用霍夫变换对处于倾斜状态的风机叶片进行校正,并裁剪为统一的大小形成完整的数据集。接着,通过两种残差网络(ResNet,ResNext)替代传统VGG作为SSD算法的Backbone进行特征提取。最后,通过预测网络实现缺陷类别和边界框定位的预测。实验结果显示,改进SSD算法较传统SSD算法在mAP值上提升了7%,同时,检测效率也大幅提升。

Aiming at the problems of low accuracy and time-consuming of traditional algorithm,this paper proposed a defect detection method of wind turbine(WT)blade based on improved SSD.Firstly,the inclined WT blades are corrected by Hough transform and trimmed to a uniform size to form a complete data set.Then,two kinds of residual networks(ResNet,ResNext)are used to replace the traditional VGG as the backbone of SSD algorithm for feature extraction.Finally,the prediction network is used to complete the prediction of defect category and boundary box location.The experimental results show that the improved SSD algorithm improves the value of mAP by 7%compared with the traditional SSD algorithm.At the same time,the detection efficiency has been greatly improved.