通过分析TOF(time of flight)成像信息,将场景反射光强度与深度信息采用图像表示,应用各向异性扩散与冲击滤波耦合的滤波模型,滤除深度图中的噪声,采用阈值分割、区域生长、区域最小外接圆等算法将柑橘从背景中分割出,并以区域距离极差...通过分析TOF(time of flight)成像信息,将场景反射光强度与深度信息采用图像表示,应用各向异性扩散与冲击滤波耦合的滤波模型,滤除深度图中的噪声,采用阈值分割、区域生长、区域最小外接圆等算法将柑橘从背景中分割出,并以区域距离极差、距离方差和识别柑橘半径值为约束条件,对识别的柑橘区域进一步筛选,再结合三维数据库信息提取柑橘的基本特征参数,最后实现树上柑橘的实时识别与定位.自然生长状态下柑橘的识别正确率达86.7%,误判率为0,深度误差小于12 mm,实际半径误差小于13 mm,图像实时采集与处理耗时小于100 ms.展开更多
Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categori...Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.展开更多
文摘目的为解决水果内部品质信息的快速无损检测,自主研制了一台基于可见/近红外光谱技术的便携式分析仪,通过试验验证其可行性及所建模型的鲁棒性。方法以红富士苹果为检测对象,采集透射光谱曲线,与化学指标可溶性固形物含量(soluble solid content,SSC)分别建立基于平均光谱、基于各采样光谱的偏最小二乘(partial least squares,PLS)回归模型,比较预测精度并对非同批次样本进行预测。结果试验表明该分析仪对苹果SSC具有较高的测量精度,特别是基于各采样光谱的PLS模型,对同批次样本预测相关系数(Rp)达到0.924,预测均方根误差低至0.429%Brix,预测精密度(平均偏差)低至0.136%Brix,对非同批次样本SSC表现出较强的鲁棒性能,预测均方根误差为0.531%Brix。结论通过此项研究,表明该便携分析仪可用于水果内部品质信息的定量分析,并建议采用基于各采样光谱建立的回归模型用于外来样本的预测。
基金supported by the Natural Science Foundation of Zhejiang Province(No.LY21C200001)National Natural Science Foundation of China(No.31571920)+1 种基金Wenzhou Science and Technology Project(No.N20160004)Wenzhou Basic Public Welfare Project(No.N20190017)。
文摘Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.
