计算机科学
云计算
深度学习
人工智能
前沿
服务器
GSM演进的增强数据速率
边缘计算
实时计算
计算机视觉
模拟
航空航天工程
计算机网络
工程类
操作系统
作者
Yajuan Liu,Zhen Wang,Xiao-Lun Wu,Fang Fang,Ali Syed Saqlain
出处
期刊:IEEE Internet Computing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:27 (1): 43-51
被引量:2
标识
DOI:10.1109/mic.2022.3175935
摘要
Blade health is directly related to the safety and efficiency of wind turbine (WT) operation. In this article, a cloud-edge-end collaborative detection method for WT blade surface damage is proposed based on lightweight deep learning network. The blade images are obtained by unmanned aerial vehicle. The YOLOv3 is optimized on the cloud server, including backbone network replacement, filter pruning, and knowledge distillation. After model training, the lightweight deep learning model YOLOv3-Mobilenet-PK is obtained and deployed on edge device to detect the surface damage of the WT blades, then the detection results can be viewed through the portable mobile device. The results show that the mean average precision (mAP) of the detection method proposed in this article is over 90%, the detection speed is about two times that of the YOLOv3-DarkNet53. This method has the advantages of fast detection speed, high accuracy, and less occupation of bandwidth.
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