天津科技大学学报2024，Vol.39Issue(6)：66-74,9.DOI:10.13364/j.issn.1672-6510.20230200
基于图像超分辨率预处理和二次迁移学习的水稻病害识别方法
Rice Disease Recognition Model Based on Image Super-Resolution Processing and Two-Step Transfer Learning
摘要
Abstract
Aiming at the problem that the existing deep learning model has low accuracy in the recognition of rice leaf dis-eases,in this article we propose a rice disease recognition method based on image super-resolution preprocessing and two-step transfer learning.With the use of super-resolution reconstruction technology,higher quality images can be obtained,thus improving the recognition rate.By using the two-step transfer learning technology,AW modules composed of Inception blocks is introduced to construct the network model AW-Net(Add width modules to the network model).This method in-creases the network width of the model,which can effectively reduce the intra-class distance,enlarge the inter-class distance,realize the effective extraction of the features of rice leaf disease regions and improve the recognition rate.The experimental results showed that the recognition accuracy of our proposed AW-Net model was significantly improved.关键词
图像超分辨率/二次迁移学习/VGG16/inception模块/图像分类Key words
image super-resolution/two-step transfer learning/VGG16/inception module/image classification分类
信息技术与安全科学引用本文复制引用
杨巨成,燕聪,贾庆祥,沈杰,刘建征..基于图像超分辨率预处理和二次迁移学习的水稻病害识别方法[J].天津科技大学学报,2024,39(6):66-74,9.基金项目
天津市科普项目(21KPHDRC00170) （21KPHDRC00170）
