Target Recognition Framework and Learning Mode Based on Parallel Images

In the application of deep learning algorithms based on large-scale data sets, some problems, such as insufficient samples, imperfect sample quality, and high cost of building large data sets, emerge and restrict algorithm performance. In this paper, a target recognition framework and learning mode based on parallel images are proposed, and the application verification is carried out by taking the insulator target recognition in the transmission line as an example. This paper uses the artificial image generation technology to establish the insulator data set NCEPU-J, and then proposes the target recognition framework PITR and three learning modes, namely OriPITR, TrsPITR, and MutiPITR. The insulator strings with the piece number of 7, 11 and 14 are verified, and the recognition accuracy is significantly improved. The results show that the target recognition framework and learning mode based on parallel images are feasible and effective.