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新疆大学机械工程学院
Published:2021
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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.
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.
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.
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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)
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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)
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