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Image anomaly detection for IoT equipment based on deep learning
تشخیص ناهنجاری تصویر برای تجهیزات اینترنت اشیا بر اساس یادگیری عمیق-2019 Intelligent power grid systems is the trend of power development, since traditional methods of manually
monitoring power equipment have been unable to meet the requirements of power systems. When an
abnormal situation occurs in the operating environment, most monitoring devices cannot be quickly
and accurately identified, which may have serious consequences. Aiming at the above problems, in this
paper, we propose an anomaly detection algorithm for the monitoring environment of power IoT equipment
operating environment based on deep learning from the perspective of personnel identification and
fire smoke detection. The multi-stream CNN-based remote monitoring image personnel detection
method and the deep convolutional neural network-based fire smoke detection method have achieved
good results in personnel identification and fire smoke detection in the power equipment operating environment
monitoring image, respectively. This provides a reference for monitoring image anomaly
detection. Keywords: Operating environment monitoring | Image anomaly detection | Deep learning |
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