A noise self-training insulator-defect detection method based on the improved two-stage detection net with data generation

Abstract

Traditional methods for detecting damage of electric power line insulators are often limited to improve the accuracy of detection because of poor datasets. To enhance the performance of insulator detectors, in this paper, we propose a semi-supervised object detection method by combining a two-stage proposal-connection detection net (TPD-Net) with an enhanced network structure generative adversarial network (En-GAN), termed the noise self-training insulator-defect detection network (NS-IDNet). Firstly, the En-GAN approach is utilized to synthesize a large number of class-balanced samples as unlabeled data, controlled by a coefficient. Secondly, according to the TPD-Net method, a teacher model is trained to employ the labeled data, and then this teacher model is used to predict the sample labels for the unlabeled data. Finally, noise self-training is conducted. The student model and teacher model are repeatedly trained until convergence, while noises are introduced into the above models and samples. Diagnostic results on the test set from the original dataset reveal that the proposed NS-IDNet outperforms the traditional supervised model. Additionally, comparative experiments demonstrate the diagnostic accuracy of the proposed NS-IDNet is superior to traditional semi-supervised models.