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

文献类型： 外文期刊

第一作者： Qi, Jiangtao

作者： Qi, Jiangtao;Liu, Xiangnan;Liu, Kai;Guo, Hui;Tian, Xinliang;Li, Mao;Bao, Zhiyuan;Li, Yang;Qi, Jiangtao;Liu, Xiangnan;Liu, Kai;Guo, Hui;Tian, Xinliang;Li, Mao;Bao, Zhiyuan;Li, Yang;Xu, Farong;Li, Yang

作者机构：

关键词： Tomato virus disease; Disease detection; SE-YOlOv5 model; Attention mechanism

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 194 卷

页码：

收录情况： SCI

摘要： 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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