A Res-UNet model for transmission segmentation based on attention mechanism

Cuicui Hang; Ting Yang; Wen Chen; Jun Gao; Jun Yan; Bo Wang

doi:10.1117/12.3024292

13 May 2024 A Res-UNet model for transmission segmentation based on attention mechanism

Cuicui Hang, Ting Yang, Wen Chen, Jun Gao, Jun Yan, Bo Wang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131599C (2024) https://doi.org/10.1117/12.3024292
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Image segmentation is widely used to intuitively observe transmission lines and substations to maintain transmission stability, but the small proportion of transmission lines and complex background information in high-altitude images undoubtedly increase the difficulty of segmentation. Therefore, this paper uses the transmission image segmentation data set, and constructs a new network structure with high precision and strong anti-jamming ability on the basis of comparing several mainstream semantic segmentation models. In order to enhance the expressive ability of network features and prevent the disappearance and degradation of gradients in network training, ResNet is used as the backbone of U-Net, and attention mechanism is introduced into the sampling part of the network to help the network better reconstruct the details and spatial information of the original image. The results show that the F1 score of the model in the test set is 91.43% and the score of mIoU is 81.3%. Compared with the existing models, mIoU has been improved by nearly 5%, reaching the level of practical application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cuicui Hang, Ting Yang, Wen Chen, Jun Gao, Jun Yan, and Bo Wang "A Res-UNet model for transmission segmentation based on attention mechanism", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131599C (13 May 2024); https://doi.org/10.1117/12.3024292

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KEYWORDS

Image segmentation

Education and training

Data transmission

Image transmission

Semantics

Statistical modeling

Image enhancement

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