Image anomaly detection for IoT equipment based on deep learning

Abstract

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.