[1]化春键,黄宇峰,蒋毅,等.基于改进YOLOv5s模型的田间食用玫瑰花检测方法[J].江苏农业学报,2024,(08):1464-1472.[doi:doi:10.3969/j.issn.1000-4440.2024.08.011]
　HUA Chunjian,HUANG Yufeng,JIANG Yi,et al.Detection method of edible roses in field based on improved YOLOv5s model[J].,2024,(08):1464-1472.[doi:doi:10.3969/j.issn.1000-4440.2024.08.011]
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基于改进YOLOv5s模型的田间食用玫瑰花检测方法()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年08期
页码:
1464-1472
栏目:
农业信息工程
出版日期:
2024-08-30

文章信息/Info

Title:
Detection method of edible roses in field based on improved YOLOv5s model
作者:
化春键1; 2; 黄宇峰1; 2; 蒋毅1; 2; 俞建峰1; 2; 陈莹3
(1.江南大学机械工程学院,江苏无锡214122；2.江苏省食品先进制造装备技术重点实验室,江苏无锡214122；3.江南大学物联网工程学院,江苏无锡214122)
Author(s):
HUA Chunjian1; 2; HUANG Yufeng1; 2; JIANG Yi1; 2; YU Jianfeng1; 2; CHEN Ying3
(1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China；2.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, China；3.School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)
关键词:
目标检测; YOLOv5s; 特征融合; 注意力机制; 食用玫瑰花
Keywords:
object detection; YOLOv5s; feature fusion; attention mechanism; edible roses
分类号:
TP391.4
DOI:
doi:10.3969/j.issn.1000-4440.2024.08.011
文献标志码:
A
摘要:
为了在田间环境下准确检测食用玫瑰花及其成熟度,实现花期玫瑰花的自动化采摘,针对田间光照、遮挡等因素造成识别精度较差的问题,提出了一种基于YOLOv5s的改进模型,对花蕾期、采摘期、败花期食用玫瑰花的生长状态进行检测。首先,为了增强多尺度特征融合能力,对特征融合结构进行改进。其次,采用多分支结构训练提高精度,在颈部网络C3模块进行改进。最后,为了提升特征信息的提取能力,在颈部网络中添加融合注意力模块,使模型关注检测目标,减少玫瑰花的误检及漏检现象。改进后的模型检测总体类别平均精度较原始模型提升了3.6个百分点,达到90.4%,对3个花期玫瑰花的检测精度均有提升。本研究结果为非结构环境下的不同花期食用玫瑰花检测提供了更加准确的方法。
Abstract:
In order to accurately detect edible roses and their maturity in the field and realize the automatic picking of flowering roses, an improved model based on YOLOv5s was proposed to solve the problem of poor recognition accuracy caused by factors such as light and occlusion in the field. The growth state of edible roses at bud, picking and abortive flowering stages was detected. Firstly, in order to enhance the ability of multi-scale feature fusion, the feature fusion structure was improved. Secondly, multi-branch structure training was used to improve the accuracy, and the neck network C3 module was improved. Finally, in order to improve the ability of feature information extraction, a fusion attention module was added to the neck network to make the model focus on the detection target and reduce the false detection and missed detection of roses. The mean average precision of the improved model was 3.6 percentage points higher than that of the original model, reaching 90.4%, and the detection accuracy of roses in three flowering periods was improved. The results of this study provide a more accurate method for detecting edible roses at different flowering stages in unstructured environment.

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备注/Memo

备注/Memo:
收稿日期：2023-10-09基金项目：国家自然科学基金项目(62173160)作者简介：化春键(1975-),男,北京人,博士,副教授,主要从事机器视觉、图像处理、深度学习等研究。(E-mail)277795559@qq.com
更新日期/Last Update: 2024-09-18