基于FasterR-CNN和数据增强的棉田苗期杂草识别方法

李开敬; 许燕; 周建平; 樊湘鹏; 魏禹同

doi:10.13568/j.cnki.651094.651316.2020.06.03.0001

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基于FasterR-CNN和数据增强的棉田苗期杂草识别方法

信息科学与技术 | 更新时间：2026-01-21

- 基于FasterR-CNN和数据增强的棉田苗期杂草识别方法
- 基于FasterR-CNN和数据增强的棉田苗期杂草识别方法
- 新疆大学学报（自然科学版中英文） 2021年38卷第4期页码：450-456
- 作者机构：
  
  新疆大学机械工程学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金地区科学基金项目(51765063)
- DOI：10.13568/j.cnki.651094.651316.2020.06.03.0001
  中图分类号： TP391.41;S562;S451
- 纸质出版：2021
- 稿件说明：
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[1]李开敬,许燕,周建平,等.基于Faster R-CNN和数据增强的棉田苗期杂草识别方法[J].新疆大学学报(自然科学版)(中英文),2021,38(04):450-456.
[1]李开敬,许燕,周建平,等.基于Faster R-CNN和数据增强的棉田苗期杂草识别方法[J].新疆大学学报(自然科学版)(中英文),2021,38(04):450-456. DOI： 10.13568/j.cnki.651094.651316.2020.06.03.0001.

DOI：10.13568/j.cnki.651094.651316.2020.06.03.0001.

摘要

为解决棉花幼苗与多种类杂草交叉生长的识别率低、鲁棒性差等问题

以棉花幼苗和田间的七类常见杂草为研究对象

提出了一种基于Faster R-CNN和数据增强的棉田苗期杂草识别方法.采集不同背景下受光照影响的杂草图像4 694张

包括晴天、阴天和雨天.通过对样本图像的数据增强和特征提取网络ResNet-101的参数优化

训练出了一种可识别棉花幼苗与多种类杂草交叉生长的Faster R-CNN网络模型.在相同样本和特征网络下

将该模型与YOLO模型进行对比.结果表明:Faster R-CNN模型在棉田苗期的多种不同杂草识别中具有明显的优势

可实现各种交叉生长的杂草目标识别

平均识别率为92.01%

平均识别时间为0.261 s.

Abstract

In order to solve the problems of low recognition rate and poor robustness of cotton seedlings and various types of weeds

taking cotton seedlings and the seven common weeds in the field as the research object

a method based on Faster R-CNN and data enhancement is proposed. Method of identifying weeds in cotton field at seedling stage. 4 694 images of weeds affected by light under different backgrounds were collected

including sunny

cloudy and rainy days. Through the data enhancement of the sample image and the parameter optimization of the feature extraction network ResNet-101

a Faster R-CNN network model that can identify the cross-growth of cotton seedlings and various types of weeds is trained. Under the same sample and feature network

the model is compared with the YOLO model.The results show that the Faster R-CNN model has obvious advantages in the identification of many different weeds in the cotton field seedling stage

and can realize the identification of various cross-growing weeds

reaching an average recognition rate of 92.01% and an average recognition time of 0.261 s.

关键词

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references

WANG A,ZHANG W,WEI X,et al.A review on weed detection using ground-based machine vision and image processing techniques[J].Computers and Electronics in Agriculture,2019,158:226-240.

陈桂芬,李静,陈航,等.大数据时代人工智能技术在农业领域的研究进展[J].吉林农业大学学报,2018,40(4):502-510.CHEN G F,LI J,CHEN H,et al.Research progress of artificial intelligence technology in agriculture in the era of big data[J].Journal of Jilin Agricultural University,2018,40(4):502-510.(in Chinese)

袁亮,涂雪滢,巨刚,等.基于机器视觉的番茄实时分级系统设计[J].新疆大学学报(自然科学版),2017,34(1):11-16.YUAN L,TU X Y,JU G,et al.Design of tomato real time grading system based on machine vision[J].Journal of Xinjiang University(Natural Science Edition),2017,34(1):11-16.(in Chinese)

陈亚军,赵博,李树君,等.基于多特征的杂草逆向定位方法与试验[J].农业机械学报,2015,46(6):257-262.CHEN Y J,ZHAO B,LI S J,et al.Method and experiment of weed reverse location based on multi feature[J].Journal of Agricultural Machinery,2015,46(6):257-262.(in Chinese)

BRIVOT R,MARCHANT J.Segmentation of plants and weeds for a precision crop protection robot using infrared images[J].IEE Proceedings-Vision,Image and Signal Processing,1996,143(2):118.

李慧,祁力钧,张建华,等.基于PCA-SVM的棉花出苗期杂草类型识别[J].农业机械学报,2012,43(9):184-189+196.LI H,QI L J,ZHANG J H,et al.Identification of weed types in cotton seedling stage based on PCA-SVM[J].Journal of Aagricultural Machinery,2012,43(9):184-189+196.(in Chinese)

PIRON A,LEEMANS V,LEBEAU F,et al.Improving in-row weed detection in multispectral stereoscopic images[J].Computers and Electronics in Agriculture,2009,69(1):73-79.

HERRMANN I,SHAPIRA U,KINAST S,et al.Ground-level hyper spectral imagery for detecting weeds in wheat fields[J].Precision Agriculture,2013,14(6):637-659.

JAFARI A,MOHTASEBI SS,JAHROMI HE,et al.Weed detection in sugar beet fields using machine vision[J].International Journal of Agriculture&Biology,2006(8):602-605.

REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C].Las Vegas:Proceedings of the IEEE conference on computer vision and pattern recognition,2016.

孙哲,张春龙,葛鲁镇,等.基于Faster R-CNN的田间西兰花幼苗图像检测方法[J].农业机械学报.2019,50(7):216-221.SUN Z,ZHANG C L,GE L Z,et al.Field broccoli seedling image detection method based on fast rcnn[J].Journal of Agricultural Machinery,2019,50(7):216-221.(in Chinese)

周云成,许童羽,郑伟,等.基于深度卷积神经网络的番茄主要器官分类识别方法[J].农业工程学报.2017,33(15):219-226.ZHOU Y C,XU C Y,ZHENG W,et al.Classification and recognition of tomato main organs based on deep convo-lution neural network[J].Journal of Agricultural Engineering.2017,33(15):219-226.(in Chinese)

张庆英,葛动元,岳卫宏,等.摄像机标定精度研究中的图像及数据处理问题[J].新疆大学学报(自然科学版),2004,21(1):73-76.ZHANG Y Y,GE D Y,YUN W H,et al.Image and data processing in the study of camera calibration accuracy[J].Journal of Xinjiang University(Natural Science Edition),2004,21(1):73-76.(in Chinese)

余鹰,王乐为,张应龙.基于特征提取偏好与背景色相关性的数据增强算法[J].计算机应用,2019,39(11):3172-3177.YU Y,WANG L W,ZHANG Y L,Data enhancement algorithm based on feature extraction preference and backgrou-nd color correlation[J].Computer application,2019,39(11):3172-3177.(in Chinese)

李哲.基于GAN的不平衡数据增强算法及其应用研究[D].北京：北京交通大学,2019.LI Z.Research on GAN based unbalanced data enhancement algorithm and its application[D].Beijing:Beijing Jiaotong University,2019.(in Chinese)

RUSSAKOVSKY O,DENG J,SU H,et al.Image net large scale visual recognition challeng[J].International Journal of Computer Vision,2015,115(3):211-252.

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