摘要
实现苹果自动检测对推动苹果生产管理自动化、智能化有着重要意义。为了解决当前苹果检测方法准确率低、鲁棒性差等问题,文章提出了一种基于改进YOLOv8算法的苹果检测模型,通过引入GAM注意力机制提升模型的检测精度。在苹果检测数据集上进行验证,基于改进的YOLOv8算法和原始YOLOv8算法相比,mAP 0.5提高了1.7%,mAP 0.5∶0.95提高了2.2%。实验结果表明,文章提出的改进YOLOv8算法更能满足实际情况中对苹果检测的要求。
The implementation of automatic apple detection is of great significance for promoting the automation and intelligence of apple production management.In order to solve the problems of low accuracy and poor robustness of current apple detection methods,the article proposes an apple detection model based on the improved YOLOv8 algorithm,which improves to the detection accuracy of the model by introducing the GAM attention mechanism.Verified on the apple detection dataset,based on the improved YOLOv8 algorithm and the original YOLOv8 algorithm,mAP 0.5 improved by 1.7%and mAP 0.5∶0.95 improved by 2.2%.The experimental results show that the improved YOLOv8 algorithm proposed in this paper can better meet the requirements of apple detection in practical situations.
作者
杜宝侠
唐友
辛鹏
杨牧
Du Baoxia;Tang You;Xin Peng;Yang Mu(Jilin Institute of Chemical Technology,Jilin 132022,China;Jilin Agricultural Science and Technology University,Jilin 132101,China;FAW-Tokico Shock Absorber Co.,Ltd.,Changchun 130001,China)
出处
《无线互联科技》
2023年第13期119-122,共4页
Wireless Internet Technology
基金
吉林省科技发展计划项目,项目名称:基于数据挖掘技术的全基因组选择方法研发及云计算平台体系构建,项目编号:YDZJ202201ZYTS692。
