摘要
草莓作为高价值经济作物,其自动化采摘需要进行目标发现及熟度判断,传统草莓采摘分析方法主要使用色度和大小分析等简单图像处理方法,误报率高。提出二阶段检测网络YOLO-ResNeXt,并根据互联网图片及产地实拍创建Strawberry3000数据集,在此基础上,创新性采用变分自编码器(Variational Auto-Encoder,VAE)进行网络部分结构的快速搜索,该方案效率高且对简单结构搜索起到了较好的效果。经测试,该算法能够有效检测草莓目标并分析草莓熟度,在准确率及召回率等指标上对比通用计算机视觉算法有着很大提高,将有效促进高价值经济作物采摘工作的发展。
As a high-value economic crop,strawberry s automatic picking requires target detection and maturity judgment.Traditional strawberry picking analysis methods mainly use simple image processing methods such as color and size analysis,which has high false alarm rate.In this paper,a two-stage detection network YOLO-ResNeXt is proposed.The Strawberry3000 dataset was created according to the Internet images and the actual farmland photos.On this basis,this paper innovatively used the variational auto-encoder(VAE)to search the network structure quickly,which had high efficiency and good effect on the simple structure search.According to the test results,the algorithm can effectively detect strawberry target and analyze strawberry maturity.Compared with the traditional computer vision algorithm,the accuracy and recall rate are greatly improved,which will effectively promote the development of high-value economic crop picking.
作者
田宏伟
徐云龙
杨艳红
刘雪兰
任艳
Tian Hongwei;Xu Yunlong;Yang Yanhong;Liu Xuelan;Ren Yan(Applied Technology College of Soochow University,Suzhou 215325,Jiangsu,China;School of Agricultural Information,Jiangsu Agri-animal Husbandry Vocational College,Taizhou 225300,Jiangsu,China)
出处
《计算机应用与软件》
北大核心
2024年第10期149-154,共6页
Computer Applications and Software
基金
国家自然科学基金项目(61472262)。
关键词
计算机视觉
深度学习
目标检测
Computer vision
Deep learning
Object detection
