期刊论文

Apple disease leaf detection based on multi-scale feature fusion network

中文

中国农机化学报

2095-5553

2023

44

08

184-190

10.13733/j.jcam.issn.2095-5553.2023.08.025

2021JJ60049:湖南省自然科学基金项目

本校为其他机构

准确地检测出苹果叶片的病害对提高苹果产量和质量具有重要意义。针对现有苹果叶片病害检测模型信息利用不充分，导致检测性能不佳的问题，提出一种基于多尺度特征融合网络的苹果病害叶片检测方法。首先，利用深度可分离卷积改进传统Vgg-16网络，并作为苹果叶片病害图片的全局特征提取器；其次，利用Swin Transformer网络作为局部特征提取器；然后，提出一种多尺度特征融合网络将局部和全局特征进行融合，构造多尺度特征；最后，将融合的多尺度特征作为全连接网络的输入，实现苹果病害叶片的检测。实验结果表明，所提出方法可以实现93.98%的准确率、94.11%的精准率、93.93%的召回率和94.62%的F1...

Accurate detection of apple leaf diseases is of great significance for improving apple production and quality.Aiming at the problem that existing apple leaf disease detection models cannot make full use of information for given images,resulting in poor detection perform...MORE Accurate detection of apple leaf diseases is of great significance for improving apple production and quality.Aiming at the problem that existing apple leaf disease detection models cannot make full use of information for given images,resulting in poor detection performance,an apple disease leaf detection based on multi-...