This paper aims to solve the problem of accurate detection of various types of defects in the complex internal cavity structure of wind turbine blades. An improved SSD (single shot multibox detector) algorithm is thus proposed and three aspects of improvement are made: 1) in terms of network framework, the base network of SSD is changed from VGG-16 to ResNet101 to optimize the input features for the regression and classification tasks of predicting bounding boxes; 2) an FCSE attention module is added to make the model pay more attention to important features and improve its detection accuracy; 3) the loss function is improved by adding a hyperparameter to control the smooth region, making the model more robust. Through comparative experiments on a self-built wind turbine blade internal cavity dataset, the improved SSD model achieves an mAP value of 83.6%, which is 9.4 percentage points higher than that of the original SSD model, and has advantages over other mainstream models based on SSD framework in detection accuracy, while greatly reducing the model parameter quantity, lowering the model complexity and storage requirements, and achieving a detection speed of 31.6 f/s, meeting the detection speed needs in practical production.

turbine blades  /  single shot multibox detector  /  defect detection