Identification of Icing Thickness of Transmission Line Based on Strongly Generalized Convolutional Neural Network

Due to various problems such as weather or light changing, icing images have the characteristics of low resolution and multi-morphological. How to find the image-based recognition method with strong generalization performance has become the research hotspots. In this paper, an IBP-CNN method based on strong generalized convolutional neural network is proposed to identify the icing thickness of the transmission line. Firstly, the number of each layer filter is determined by GAP mechanism. Secondly, the optimal model layer is obtained by bidirectional greedy algorithm. Finally, the parameters of each layer are trained by back propagation algorithm. Compared with mechanical model monitoring method and image edge detection method, the experimental results show that the proposed method can maintain high accuracy and low computational complexity under different resolution and different position angles, it has strong natural scene generalization ability and can be put into practice in the future.