基于改进YOLOv5的绿辣椒目标检测方法

doi:10.6048/j.issn.1001-4330.2024.12.018

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (12): 3032-3041.DOI: 10.6048/j.issn.1001-4330.2024.12.018

• 土壤肥料·贮藏保鲜加工·园艺特产 • 上一篇下一篇

基于改进YOLOv5的绿辣椒目标检测方法

王震鲁¹(), 白涛¹^,²(), 李东亚¹, 戴硕¹, 陈珍¹

1.新疆农业大学计算机与信息工程学院,乌鲁木齐 830052
2.智能农业教育部工程研究中心/新疆农业信息化工程技术研究中心,乌鲁木齐 830052

收稿日期:2024-05-11 出版日期:2024-12-20 发布日期:2025-01-16
通信作者: 白涛(1979-),男,新疆乌鲁木齐人,副教授,硕士生导师,研究方向为农业大数据、数据挖掘,(E-mail)bt@xjau.edu.cn
作者简介:王震鲁(2000-),男,山东济宁人,硕士研究生,研究方向为计算机视觉、物联网,(E-mail)2425387367@qq.com
基金资助:
科技部科技创新2030重大项目“群体智能自主作业智慧农场”(2022ZD0115800);新疆维吾尔自治区重大科技专项“农场智能平台关键技术研究”(2022A02011-4);中央引导地方科技发展专项“智慧农业创新平台建设”(ZYYD2022B12);新疆维吾尔自治区高校基本科研业务费科研项目“农业大数据交换共享与可视化平台”(XJEDU2022J009)

Green chili pepper target detection method based on improved YOLOv5

WANG Zhenlu¹(), BAI Tao¹^,²(), LI Dongya¹, DAI Shuo¹, CHEN Zhen¹

1. College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2. Engineering Research Center for Intelligent Agriculture of Ministry of Education/Xinjiang Research Center for Agricultural Information Technology, Urumqi 830052, China

Received:2024-05-11 Published:2024-12-20 Online:2025-01-16
Supported by:
S &T Innovation 2030 Major Project of Ministry of Science and Technology "Group Intelligent Independent Operation of Intelligent Farm"(2022ZD0115800);Major Scientific R & D Program Project of Xinjiang Uygur Autonomous Region "Research on Key Technologies of Farm Intelligent Platform"(2022A02011-4);Central Government Guiding the Local Science and Technology Development Special Fund Project(ZYYD2022B12);Basic Scientific Research Project for Universities in Xinjiang Uygur Autonomous Region "Agricultural Big Data Exchange, Sharing and Visualization Platform"(XJEDU2022J009)

摘要/Abstract

摘要：

【目的】 使用机器视觉对绿辣椒的精准识别是实现辣椒智能化采摘的重要前提,研究自然条件下辣椒遮挡情况、绿色辣椒及准确识别的方法,为辣椒智能化采摘机器人的精准识别提供技术支持。【方法】 提出一种基于改进YOLOv5辣椒目标检测模型,在YOLOv5主干网络加入CA(Coordinate Attention)注意力机制,以增强辣椒特征信息的提取,进一步增强对目标位置信息的提取;同时在特征融合网络中使用Bi-FPN结构,提高模型对遮挡辣椒的识别能力。【结果】 通过在自建辣椒数据集上进行训练,改进后的模型平均准确率达到91%,相比于研究其他所对比模型,改进模型的平均准确率更高。【结论】 基于改进YOLOv5的遮挡绿色辣椒的识别具有较高的准确性。

关键词: YOLOv5; CA注意力机制; Bi-FPN; 绿辣椒检测; 遮挡

Abstract:

【Objective】 Accurate recognition of green chili peppers using machine vision is an important prerequisite for realizing intelligent picking of chili peppers, so in view of the natural conditions of pepper occlusion, this study aims to accurately identify the problem.【Methods】 A chili pepper target detection model based on improved YOLOv5 was proposed, CA (Coordinate Attention) was added in YOLOv5 backbone network Attention mechanism in the YOLOv5 backbone network to enhance the extraction of chili pepper feature information and further enhance the extraction of target location information; meanwhile, a Bi-FPN structure was used in the feature fusion network to improve the model's ability to recognize occluded chili peppers.【Results】 By training on the self-constructed chili pepper dataset, the results showed that the improved model achieved an average accuracy of 91%, which was higher compared to the other models.【Conclusion】 The method proposed in this paper has high accuracy in recognizing occluded green chili peppers in natural environments, which can provide technical support for the accurate recognition of chili pepper intelligent picking robots.

Key words: YOLOv5; CA attention mechanism; Bi-FPN; green chili pepper detection; shading

中图分类号:

王震鲁, 白涛, 李东亚, 戴硕, 陈珍. 基于改进YOLOv5的绿辣椒目标检测方法[J]. 新疆农业科学, 2024, 61(12): 3032-3041.

WANG Zhenlu, BAI Tao, LI Dongya, DAI Shuo, CHEN Zhen. Green chili pepper target detection method based on improved YOLOv5[J]. Xinjiang Agricultural Sciences, 2024, 61(12): 3032-3041.

图/表 12

图1 辣椒数据集(部分)

Fig.1 Chili pepper dataset (partial)

图2 YOLOv5网络结构

Fig.2 YOLOv5 network structure diagram

图3 改进YOLOv5网络结构

Fig.3 Improvement of YOLOv5 network structure diagram

图4 坐标注意力模块图

Fig.4 Diagram of the coordinate attention module

图5 不同特征金字塔结构

Fig.5 Structure of different characteristic pyramids

表1 训练参数

Tab.1 Training parameters

参数项 Parameter items	数值 Numerical value
Epochs	150
Batch-size	64
Img-size	640
Learn_rate	0.01
优化器Optimizer	SGD

表2 不同模型对比结果

Tab.2 Comparison results of different models

模型 Model	平均精确率 Average accuracy (%)	参数量 Parameter quantity (M)	权重大小 Weight size (M)
Faster-RCNN	89.6	137.1	108.2
YOLOv5s	89.5	7.01	13.7
YOLOv7	90.4	37.1	71.3
YOLOv8s	90.3	11.1	21.5
Ours	91.0	7.1	14.0

图6 检测结果对比

Fig.6 Comparison of test results

表3 注意力模块对比

Tab.3 Comparison results of attention modules

模型 Model	平均精确率 Average accuracy (%)	精确率 Accuracy (%)	召回率 Recall (%)
YOLOv5s+GAM	89.2	87.3	87.7
YOLOv5s+EMA	89.6	87.8	87.7
YOLOv5s+CA	89.9	86.9	89.7

图7 不同注意力模块训练

Fig.7 Training chart for different attention modules

表4 消融试验对比

Tab.4 Comparative results of ablation experiments

模型 Model	平均精确率 Average accuracy (%)	召回率 Recall (%)	平均精确率 Average accuracy (%)
YOLOv5s	88.5	88.2	89.5
YOLOv5s+CA	86.9	89.7	89.9
YOLOv5s+BiFPN	87.2	88.8	90.7
Ours	89.4	87.5	91.0

图8 不同模块训练结果

Fig.8 Graph of training results of different modules

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基于改进YOLOv5的绿辣椒目标检测方法

Green chili pepper target detection method based on improved YOLOv5

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