分割
计算机科学
人工智能
公制(单位)
模式识别(心理学)
卷积神经网络
图像分割
工程类
运营管理
作者
Weizheng Zhang,Yuefeng Wang,Godwin Shen,Canlin Li,Meng Li,Y.Q. Guo
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 103102-103114
被引量:2
标识
DOI:10.1109/access.2023.3316364
摘要
High-precision segmentation of tobacco leaves is a prerequisite for analysis of phenotypic information.Challenges such as mutual occlusion and fuzzy edges make leaf segmentation difficult.This paper proposes an improved algorithm based on the Mask Region-based Convolutional Neural Networks (MASK RCNN) model and an instance segmentation method based on the SAM model to address these challenges.First, the MASK RCNN model is enhanced by incorporating a feature fusion layer and a hybrid attention mechanism, which improves the segmentation performance.The improved MASK RCNN model achieves an Avg.MIoU metric of approximately 85.10%, which is an improvement of 11.10% over the original algorithm.It also achieves an Avg.MPA metric of about 84.94%, indicating an improvement of 10.84%.Second, the Segment Anything Model (SAM) model is presented for the first time for tobacco leaf segmentation, providing empirical support for its application in the tobacco field.The SAM model demonstrates accurate segmentation of tobacco leaf images at different growth stages, demonstrating its good generality.In conclusion, the proposed methods effectively address the challenges in tobacco leaf segmentation, resulting in improved accuracy and performance.These techniques provide significant technical support for tobacco leaf phenotype research.
科研通智能强力驱动
Strongly Powered by AbleSci AI