论文标题
使用基于注意的深度学习模型鉴定太阳能光伏面板和风力涡轮机叶片上的表面缺陷
Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model
论文作者
论文摘要
全球可再生能源的产生迅速增加，这主要是由于安装了大型可再生能源电厂。但是，由于环境因素可能导致资产寿命的发电，故障和降解，监测这些大型植物中的可再生能源资产仍然具有挑战性。因此，可再生能源资产上表面缺陷的检测对于维持这些植物的性能和效率至关重要。本文提出了一个创新的检测框架，以实现可再生能源资产的经济表面监控系统。定期捕获资产的高分辨率图像，并检查以识别太阳能电池板和风力涡轮机叶片上的表面或结构性损害。 {Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects.} The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97\% for both wind and solar plant assets.从结果来看，我们提出的模型证明了其监测和检测可再生能源资产中损害的潜力，以有效且可靠的可再生电厂运行。
The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. {Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects.} The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97\% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.
