An encoder-decoder deep model for semantic segmentation of leaf vein patterns

Shijia Wang; Lianqing Wang

doi:10.1117/12.3011411

8 November 2023 An encoder-decoder deep model for semantic segmentation of leaf vein patterns

Shijia Wang, Lianqing Wang

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231I (2023) https://doi.org/10.1117/12.3011411
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Leaf vein segmentation is of great importance for increasing food production in botany and agriculture. To further improve the performance of vein image segmentation, we propose a new model CPB-DUNet based on the method of U-Net. That is able to find the detailed features of veins from the segmented vein image itself rather than from the original input image. CPB-DUNet is composed of two horizontal U-Net structures. The second U-Net corrects and refines the original U-Net segmentation results by iteration. CPB-DUNet still uses compact bilinear pooling between each pair of decoder modules to better extract features. And we achieved IoU scores of 81.97 and 53.59, respectively, in the only two publicly available labeled vein datasets, Leaf venation networks of Bornean trees and LVD2021, the former score the best in the literature.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shijia Wang and Lianqing Wang "An encoder-decoder deep model for semantic segmentation of leaf vein patterns", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231I (8 November 2023); https://doi.org/10.1117/12.3011411

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