基于YOLOv5s的深度学习在自然场景苹果花朵检测中的应用  

Application of deep learning using YOLOv5s to apple flower detection in natural scenes

作　　者：尚钰莹 张倩如 宋怀波[1,2,3] Shang Yuying;Zhang Qianru;Song Huaibo(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Key Lab of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligence Service,Yangling 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]农业农村部农业物联网重点实验室,杨凌712100 [3]陕西省农业信息感知与智能服务重点实验室,杨凌712100

出　　处：《农业工程学报》2022年第9期222-229,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(2019YFD1002401);国家自然科学基金项目(31701326);国家高技术研究发展计划(863计划)项目(2013AA10230402)。

摘　　要：疏花是苹果栽培的重要管理措施,机械疏花是目前最具有发展潜力的疏花方式,花朵的高效检测是疏花机器人高效作业的重要保障。该研究基于机器视觉与深度学习技术,提出了一种基于YOLOv5s深度学习的苹果花朵检测方法,在对田间拍摄得到的苹果花朵图像标注后,将其送入微调的YOLOv5s目标检测网络进行苹果花朵的检测。经测试,模型的精确率为87.70%,召回率为0.94,均值平均精度(mean Average Precision,mAP)为97.20%,模型大小为14.09 MB,检测速度为60.17帧/s,与YOLOv4、SSD和Faster-RCNN模型相比,召回率分别提高了0.07、0.15、0.07,m AP分别提高了8.15、9.75和9.68个百分点,模型大小减小了94.23%、84.54%、86.97%,检测速度提升了126.71%、32.30%、311.28%。同时,该研究对不同天气、颜色和光照情况下的苹果花朵进行检测,结果表明,该模型对晴天、多云、阴天、小雨天气下苹果花朵的检测精确率分别为86.20%、87.00%、87.90%、86.80%,召回率分别为0.93、0.94、0.94、0.94,m AP分别为97.50%、97.30%、96.80%、97.60%。该模型检测白色、粉色、玫红色和红色花朵的精确率分别为84.70%、91.70%、89.40%、86.90%,召回率分别为0.93、0.94、0.93、0.93,m AP分别为96.40%、97.70%、96.50%、97.90%。该模型检测顺光和逆光条件下苹果花朵的精确率分别为88.20%、86.40%,召回率分别为0.94、0.93,m AP分别为97.40%、97.10%。结果表明YOLOv5s可以准确快速地实现苹果花朵的检测,模型具有较高的鲁棒性,且模型较小,更有利于模型的迁移应用,可为疏花器械的发展提供一定的技术支持。Flower thinning is one of the most important management measures in apple cultivation.Mechanical thinning has been the most promising way for thinning flowers in recent years.Accurate and rapid detection of flowers can be critical to the highly efficient operation of flower thinning robots.In this study,an apple flower detection was proposed using machine vision and YOLOv5s deep learning.3005 apple flower images were collected,including 1611 apple images on sunny days,512 on cloudy,519 on overcast sky days,and 363 on light rainy days.Two lighting conditions were considered,where 1830 apple images under the front lighting and 1175 apple images under the backlight.Two occlusion situations were selected,where 1602 apple images with occlusion,and 1403 apple images without occlusion.The apple flower images were taken to annotate in the field,and then sent to the fine-tuned YOLOv5s target detection network for the detection of the apple flower.300iterations of training were implemented after the test.The better performance was achieved,where the precision of the model was 87.70%,the recall was 0.94,the mean average precision was 97.20%,the model size was 14.09 MB,and the detection speed was 60.17 f/s.Specifically,the recall increased by 7,15,and 7 percental points,respectively,compared with the YOLOv4,SSD,and Faster-RCNN models,while the mAP increased by 8.15,9.75,and 9.68 percental points,respectively,the model size decreased by 94.23%,84.54%,and 86.97%,respectively,as well as the detection speed increased by 126.71%,32.30%,and 311.28%,respectively.At the same time,the study detected apple flowers in different weather,colors and light conditions.The results showed that the precision values of the model to detect the white,pink,rose and red flowers were 84.70%,91.70%,89.40%,and 86.90%,respectively,while the recall were 0.93,0.94,0.93,and 0.93,respectively,as well as the mean average precision were 96.40%,97.70%,96.50%,and 97.90%,respectively.The precision values of the model for detecting apple blossoms under sunny,clo

关 键 词：机器视觉 苹果花朵 检测 YOLOv5s 自然场景 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]