A feedback cognition method of insulator state based on attention mechanism
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
To overcome the drawbacks of the existing insulator state recognition models, open-loop cognitive mode and insufficient generalization ability of loss function for detailed recognition deep network, in this paper a feedback cognition method of insulator state is proposed based on attention mechanism in imitation of human inspection mode, i.e. real-time evaluation of reliability of cognitive results and self-optimizing and regulation of the multi-scale image knowledge space. Firstly, for the pre-processed insulator image, a stacked convolutional neural network with adaptive scale architecture is designed, which enables the network input to be scaled from the overall image to the detailed area. Each scaled network shares the same architecture with different parameters to ensure the discriminative ability of different resolution inputs and generate a detailed attention area for the next scale. Secondly, for multiple scale features, stochastic configuration network (SCN) builds the classification criterion of the insulator states with universal approximation ability. Thirdly, an inter-class classification loss function and an intra-class ranking loss function are constructed to optimize the attention network, which generates a higher confidence score ranking than the previous prediction. Finally, learning from closed-loop control idea, the generalized error entropy performance index is defined to evaluate the reliability of the uncertain cognition results of insulator states in real time.The network scale level is dynamically regulated to realize the self-optimizing regulation of the feature space and the reconstruction of the classification criteria based on the constraint of the uncertain detection results, which enables insulator states to be re-recognized with feedback mechanism. Experimental results show that compared with other network architectures, the proposed method enhances the generalization ability and improves the cognition accuracy of the model.
