An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease

过度拟合 卷积神经网络 人工智能 计算机科学 一般化 模式识别(心理学) 机制(生物学) 人工神经网络 网络模型 集合(抽象数据类型) 试验装置 钥匙(锁) 计算机视觉 数学 哲学 数学分析 认识论 计算机安全 程序设计语言
作者
Jiangtao Qi,Xiangnan Liu,Kai Liu,Farong Xu,Guo Hui,Xinliang Tian,Mao Li,Zhiyuan Bao,Yang Li
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:194: 106780-106780 被引量:247
标识
DOI:10.1016/j.compag.2022.106780
摘要

• A deep learning model based on attention mechanism is proposed for tomato virus disease recognition. • The recognition accuracy is improved while maintaining the same detection speed. • It provides technical support for other researches related to plant disease recognition. Traditional target detection methods cannot effectively screen key features, which leads to overfitting and produces a model with a weak generalization ability. In this paper, an improved SE-YOLOv5 network model is proposed for the recognition of tomato virus diseases. Images of tomato diseases in greenhouses were collected using a mobile phone, and the collected images were expanded. A squeeze-and-excitation (SE) module was added to a YOLOv5 model to realize the extraction of key features, using a human visual attention mechanism for reference. The trained network model was evaluated on the test set of tomato virus diseases. The accuracy was 91.07%, which was 7.12%, 17.85% and 8.91% higher than that of the Faster regions with convolutional neural network features (R-CNN) model, single-shot multiBox detector (SSD) model and YOLOv5 model, respectively. Meanwhile, the mean average precision (mAP @0.5 ) was 94.10%, which was 1.23%, 16.77% and 1.78% higher than that of the Faster R-CNN model, SSD model and YOLOv5 model. The proposed SE-YOLOv5 model can effectively detect regions of tomato virus disease, which provides disease identification and control theoretical research and technical support.
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