Abstract
Unmanned aerial vehicle (UAV) based inspection of wind turbine surface conditions is a research hot spot in the wind energy industry. To tackle the fundamental problems in UAV-based inspection, this letter proposes an improved U-Net model for segmenting wind turbines from UAV-taken images. In the proposed segmentation model, ResNet is employed as the backbone for feature extraction while two types of attention mechanism, ECA-Net and PSA-Net, are used to capture important details in images. Therefore, multiscale features are effectively fused and cross-channel attention is obtained. The feasibility and efficiency of the proposed method are validated based on UAV-taken images collected from four commercial wind farms. Meanwhile, the proposed method is benchmarked with the generic U-Net, UNet++, Res-UNet, DenseUNet, and DeepLabv3. The segmentation results show the proposed model outperforms other methods, and, thus, the proposed model is applicable for real UAV-based inspection tasks.
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