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Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone

机译：无人机拍摄的风力涡轮机刀片图像紧凑的异常检测

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Yinan Wang; Ryota Yoshihashi; Rei Kawakami; Shaodi You; Tohru Harano; Masahiko Ito; Katsura Komagome; Makoto Iida; Takeshi Naemura;
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1. Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model [J] . Touati Redha, Mignotte Max, Dahmane Mohamed Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2020,第期

机译：异构图像中无监督变化检测的异常特征学习：深稀疏的残余模型
2. Automatic image detection of multi-type surface defects on wind turbine blades based on cascade deep learning network [J] . Mao Yulin, Wang Shuangxin, Yu Dingli, Intelligent data analysis . 2021,第2期

机译：基于级联深度学习网络的风力涡轮机叶片多型表面缺陷自动图像检测
3. A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection [J] . Song Fu, Shisheng Zhong, Lin Lin, Engineering Applications of Artificial Intelligence . 2021,第May期

机译：用于燃气轮机无监督异常检测的重新优化的深自动编码器
4. Deep Unsupervised Workload Sequence Anomaly Detection with Fusion of Spatial and Temporal Features in the Cloud [C] . Mengqing Wang, Zhihua Zhang, Jialei Liu, IEEE International Conference on Cloud Computing . 2020

机译：深度无监督的工作量序列异常检测，云中的空间和时间特征融合
5. Volume CT Data Inspection and Deep Learning Based Anomaly Detection for Turbine Blade [D] . Wang, Kan. 2017

机译：基于体积CT数据检查和深度学习的涡轮叶片异常检测
6. Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade [O] . Shaohua Tian, Zhibo Yang, Xuefeng Chen, 2015

机译：基于FBG的风力机叶片静态应变响应损伤识别。
7. Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model [O] . Redha Touati, Max Mignotte, Mohamed Dahmane 2020

机译：异构图像中无监督变化检测的异常特征学习：深稀疏的残余模型
8. Unsupervised Spatial Feature and Change Detection in RS Imaging [R] . Mokken, R. J. 2000

机译：Rs成像中的无监督空间特征与变化检测

Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone

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