Abstract
Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.
Highlights
In order to verify the impact of two-stage generation on model performance, we conducted experiments on the number of stages of InsulatorGAN introduced on the InsuGenSet dataset
Until the training set size is reduced to 70%, InsulatorGAN’s scores on various indicators are similar to SelectionGAN, which shows that InsulatorGAN has strong robustness and can still learn key feature information even on a small-scale dataset
InsulatorGAN, which can generate insulator-detection images based on aerial images taken by drones
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. CGAN uses aligned images to enable the framework to learn the relevant mapping from input to output In this way, the original image and ground truth can be used to generate an insulator-detection image. To solve the above problems, this paper proposes an insulator-detection imagegeneration model called InsulatorGAN based on an improved conditional generation confrontation network. Based on the parameter sharing mechanism, we proposetoause multi-scale discriminator different levels of abstraction to determine whether the input image is true or false; structure that enables the entire discriminator network to use feature information at. To solve the small scale of the public insulator dataset CPLID, we established a daconducted many comparative experiments between the and state-oftaset called InsuGenSet for insulator-detection image generation based on real imthe-art models on InsuGenSet, and the results demonstrated the effectiveness and ages.
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