Loading [a11y]/accessibility-menu.js
Learning Insulators Segmentation from Synthetic Samples | IEEE Conference Publication | IEEE Xplore

Learning Insulators Segmentation from Synthetic Samples


Abstract:

Neural networks always require extensive training samples. However, in some special applications, i.e., insulators in high power grid, it is very hard and costly to colle...Show More

Abstract:

Neural networks always require extensive training samples. However, in some special applications, i.e., insulators in high power grid, it is very hard and costly to collect variety-rich samples. In this study, a synthetic method is proposed to generate segmentation training samples for the insulators. Instead of relying on full-fledged Computer Graphic, this study focuses on the training features of neural networks. Based on this synthetic approach, many kinds of insulators samples including positive, empty and fake ones can be constructed, and their quantities are particularly balanced by an equalization strategy. In order to validate these produced samples, three end-to-end segmentation networks are employed to adapt to the generators in an adversarial training framework. Meanwhile, an improved training strategy is utilized to speed up the convergence. Finally, extensive experiments are executed to further analyze the proposed synthetic method and demonstrate its effectiveness for insulators segmentation.
Date of Conference: 08-13 July 2018
Date Added to IEEE Xplore: 14 October 2018
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Rio de Janeiro, Brazil

Contact IEEE to Subscribe

References

References is not available for this document.