Segmentation of high-voltage transmission wires from remote sensing images using U-Net with sample generation

ABSTRACT

This paper proposes an approach under the framework of deep learning for the task of identification and extraction of high-voltage transmission wires in power grids based on high-resolution optical remote sensing images. First, the task is defined as a semantic segmentation problem. Based on the recently proposed Swin-Unet framework, an intuitive improvement named ‘Swin-Unet-M’ with a refined linear embedding is proposed, improving the wire segmentation performance. Second, an effective sample synthesis technique is developed to generate training images containing wires. The wire target is randomly merged in background images to generate a rich pseudo-wire segmentation training dataset. Consequently, the cost of manually pixel-wise wire labelling is largely reduced. Experimental results demonstrate that the proposed Swin-Unet-M model can obtain higher segmentation performance and effectively identify and extract the wires in real images.

KEYWORDS:

• Power grids
• transmission wire segmentation
• U-Net
• Swin Transformer
• training sample generation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://github.com/open-mmlab/mmsegmentation.

Additional information

Funding

The authors are grateful to Science and Technology Projects of China Southern Power Grid Co. Ltd. for the funding with grant number [GDKJXM20198382].