农业机械学报2024，Vol.55Issue(11)：193-201,9.DOI:10.6041/j.issn.1000-1298.2024.11.021
基于高光谱成像和GAN-SA-UNet算法的烟叶叶脉分割方法研究
Tobacco Leaf Vein Segmentation Method Based on Hyperspectral Imaging and GAN-SA-UNet Algorithm
摘要
Abstract
As an important feature of plants,leaf veins contain physiological and genetic information.Aiming at the problems of blurred edge segmentation and low segmentation accuracy of small veins in complex leaf texture state,a GAN-SA-UNet vein segmentation algorithm was proposed with tobacco leaves as the research object.The spectral information of veins and leaves was obtained by hyperspectral imaging technology,and the principal component analysis(PCA)was used to reduce the dimension and obtain the composite map.On this basis,the spatial attention mechanismwas introduced to capture the key spatial features and improve the segmentation accuracy.At the same time,the adversarial network was introduced to optimize the generated results and improve the robustness of vein segmentation.The results showed that the interpretation rate of the first three principal components of PCA of leaf vein and leaf surface spectrum was 95.71％,and the spectral characteristics of the two after dimension reduction showed obvious separability.The first three principal components composite map could highlight the difference between leaf surface and leaf vein,and highlight the characteristics of leaf vein.The GAN-SA-UNet segmentation algorithm can capture the vein features in complex leaf texture images.The segmentation accuracy and intersection over union were 98.93％and 66.23％,respectively.Compared with the original model,they were increased by 0.18 percentage points and 4.21 percentage points,respectively.The inference time of single image was 4 ms.It showed strong generalization ability and efficient and accurate recognition ability in the verification test of different producing areas,parts,grades and types of tobacco leaves.关键词
烟叶叶脉分割/高光谱成像技术/U-Net/空间注意力机制/生成对抗网络Key words
tobacco leaf vein segmentation/hyperspectral imaging technology/U-Net/spatial attention mechanism/generative adversarial networks(GANs)分类
信息技术与安全科学引用本文复制引用
付主木,郝英杰,李嘉康,雷翔,堵劲松,徐大勇..基于高光谱成像和GAN-SA-UNet算法的烟叶叶脉分割方法研究[J].农业机械学报,2024,55(11):193-201,9.基金项目
中国烟草总公司重点研发项目(110202202010) （110202202010）
