基于改进Faster R-CNN的桃树缺磷症检测研究
Research on peach phosphorus deficiency detection based on improved Faster R-CNN
胡彦军 1张烨 2张平川 3张彩虹 3陈昭 3陈旭3
作者信息
- 1. 河南科技大学应用工程学院,河南三门峡,472099;新乡学院机电工程学院,河南新乡,453003;河南科技学院计算机科学与技术学院,河南新乡,453003
- 2. 新乡学院机电工程学院,河南新乡,453003
- 3. 河南科技学院计算机科学与技术学院,河南新乡,453003
- 折叠
摘要
桃树缺磷症(Peach Phosphorus Deficiency,PPD)初期症状不明显、不同阶段症状差异大,而现有的基于计算机视觉的桃树病害识别模型,识别准确率不高、对不同品种识别泛化性差,为此,提出改进Faster R-CNN(Faster Region based Convolutional Neural Network)模型.首先,使用 RS(Rank & Sort)-Loss 函数代替 区域建议网络(Region Proposal Network,RPN)中的交叉熵函数;其次,使用Soft-NMS(Non-Maximum Suppression)算法代替原有的NMS算法;最后,使用ResNeXt101网络替换原来的特征提取网络,提高对PPD识别的准确率和泛化性,并在自建PPD数据集上进行检测试验.试验结果表明:改进后的Faster R-CNN网络在自建PPD数据集上对PPD的各类别平均检测准确率达92.28％、召回率达92.31％、识别准确率达92.28％,满足实际应用要求.
Abstract
The initial symptoms of peach phosphorus deficiency(PPD)are not obvious,and the symptoms differ greatly in different stages.However,the existing peach disease identification model based on computer vision has low identification accuracy and poor generalization of different varieties.Therefore,the Faster R-CNN(Faster Region based Convolutional Neural Network)model is improved.Firstly,the RS(Rank & Sort)-Loss function is used to replace the cross-entropy function in the Region Proposal Network(RPN).Secondly,the Soft-NMS(Non-Maximum Suppression)algorithm is used to replace the original NMS algorithm.Finally,ResNeXt101 network is used to replace the original feature extraction network to improve the accuracy and generalization of PPD recognition,and the detection test is carried out on the self-built PPD data set.The experimental results show that the improved Faster R-CNN network has an average detection accuracy of 92.28％,a recall rate of 92.31％and a recognition accuracy of 92.28％on the self-built PPD data set,which meets the practical application requirements.
关键词
桃树缺磷症/改进/Faster/R-CNN/RPN/Soft-NMS/ResNeXt101Key words
peach phosphorus deficiency/improved Faster R-CNN/RPN/Soft-NMS/ResNeXt101引用本文复制引用
基金项目
河南省科技厅科技攻关计划(212102310553)
河南省科技厅科技攻关计划(222102210116)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据