Safety State Idetification for Transmission Towers in Goafes Based on Improved Fuzzy Theory and Transfer learning

The existing identification methods of transmission tower safety state in goafs have some defects, such as large sample data scale, and the identification accuracy is significantly affected by some time-varying parameters. To solve this problem, this paper proposes an identification method based on improved fuzzy theory and transfer learning. Firstly, the image feature data of transmission tower in goafs is extracted according to the YOLOv3 algorithm which belongs to one-stage target detection algorithm, and the image feature data is preprocessed by fuzzy algorithm. Secondly, a deep convolution neural network framework is proposed for security state recognition, and a multi-stage migration learning algorithm is used to complete network training. Finally, the results show that compared with the traditional machine learning algorithm without migration learning training, the proposed algorithm improves the accuracy and precision by more than 4.5% and 3.8%, respectively, which verifies the effectiveness and superiority of the proposed algorithm.