Fast detection of wind turbine blade damage using Cascade Mask R-DSCNN-aided drone inspection analysis

In this paper, a fast wind turbine defect detection model is proposed with a Cascade Mask region Convolutional Neural network (Cascade Mask R-CNN). Instead of standard convolution in the backbone network of Cascade Mask R-CNN, a depthwise separable convolution is used to minimize the computation cost. Moreover, image augmentation and transfer learning techniques are also involved to enhance the performance of the proposed model. The detection and instance segmentation performance of the proposed model is compared with existing techniques in terms of mean intersection over union (MIoU), mean average precision (MAP) and classifier accuracy. The experimental results show that the proposed WTB defect detection and classification model shows better performance with 82.42% MAP, 87.49% MIoU and 97.8% classifier accuracy.

Automated summary

A fast wind turbine defect detection model is proposed using a Cascade Mask region Convolutional Neural network (Cascade Mask R-CNN) in this paper. A depthwise separable convolution is used to minimize the computation cost in the backbone network of Cascade Mask R-CNN instead of standard convolution. In addition, image augmentation and transfer learning techniques are used to enhance the model's performance. The proposed WTB defect detection and classification model shows better performance with 82.42% MAP, 87.49% MIoU and 97.8% classifier accuracy, as compared to existing techniques.