基于改进YOLOv5s算法的草莓病虫害检测研究
Research on Strawberry Disease and Pest Detection Based on Enhanced YOLOv5s Algorithm
马赛 1丁健 1张火强 1汪慧1
作者信息
- 1. 合肥大学先进制造工程学院,安徽合肥 230601
- 折叠
摘要
草莓的生长容易受到多种病虫害的影响,为了快速、准确地检测草莓植株在生长过程中所受病虫害的情况,通过采集草莓生长过程中的图像信息,应用YOLOv5s算法进行分析处理,将CBAM注意力机制集成到YOLOv5s模型中,用以增强模型的感受力和表达能力,通过选择SIOU损失函数替代GIOU损失函数进一步加快模型收敛.研究结果表明,经过改进的算法模型准确率达到了 95.2％,召回率提升至97.2％,平均精度均值提高至98.5％.一定程度上满足草莓病虫害的检测.
Abstract
Strawberry growth is susceptible to a variety of diseases and pests.In order to quickly and accu-rately detect the diseases and pests suffered by strawberry plants during the growth process,this paper collects image information during the growth process of strawberry,applies YOLOv5s algorithm for analysis and process-ing,and integrates CBAM attention mechanism into YOLOv5s model.In order to enhance the sensibility and ex-pressiveness of the model,SIOU loss function is selected to replace GIOU loss function to accelerate the conver-gence of the model.The results show that the accuracy of the improved algorithm model reaches 95.2％,the re-call rate increases to 97.2％,and the average accuracy increases to 98.5％.To some extent,it can satisfy the detection of strawberry pests and diseases.
关键词
病虫害检测/YOLOv5s/CBAM/SIOU损失函数Key words
pest and disease detection/YOLOv5s/CBAM/SIOU loss function引用本文复制引用
基金项目
安徽省大学生创新创业训练计划(2023)(S202311059261X)
出版年
2024
NETL
NSTL
万方数据