A CycleGAN small sample library amplification method for faulty Insulator detection.

In deep learning training, insulator detection requires a large number of faulty insulators. It is actually difficult to obtain a large amount of faulty insulator data. Generative Adversarial Network (GAN) provides a feasible solution for augmenting training samples. This paper supplements the defective insulator samples in the structure of the Cycle-consistent GAN (CycleGAN), optimizes the model by changing the loss function, and inputs the image synthesized by the forward generator to the reverse generator, thus maintaining the overall outline of the sample while adding the difference. In the SSD (Single Shot Detector) target detection experiment, the method of using the improved CycleGAN model to expand the dataset was compared with other GAN models. The results show that the method of using the improved CycleGAN to expand the dataset significantly improves the recognition rate of insulator drop detection compared with other expansion methods. [ABSTRACT FROM AUTHOR]