Insulator Segmentation for Power Line Inspection Based on Modified Conditional Generative Adversarial Network

There are a large number of insulators on the transmission line, and insulator damage will have a major impact on power supply security. Image-based segmentation of the insulators in the power transmission lines is a premise and also a critical task for power line inspection. In this paper, a modified conditional generative adversarial network for insulator pixel-level segmentation is proposed. The generator is reconstructed by encoder-decoder layers with asymmetric convolution kernel which can simplify the network complexity and extract more kinds of feature information. The discriminator is composed of a fully convolutional network based on patchGAN and learns the loss to train the generator. It is verified in experiments that the proposed method has better performances on mIoU and computational efficiency than Pix2pix, SegNet, and other state-of-the-art networks.

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