基于相位和高光谱的番茄果实多模态融合检测方法  

Multimodal Fusion Detection Method of Tomato Fruit Based on Phase and Hyperspectral

作　　者：戴海宸 韦鑫宇 徐一新 陈元平 徐媛媛[1] 季颖[1] DAI Haichen;WEI Xinyu;XU Yixin;CHEN Yuanping;XU Yuanyuan;JI Ying(College of Physics and Electronic Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学物理与电子工程学院,镇江212013

出　　处：《光子学报》2024年第7期258-272,共15页Acta Photonica Sinica

基　　金：国家自然科学基金(No.11874184);江苏大学农业装备学部项目(No.NZXB20200215);江苏大学大学生科研课题立项资助项目(No.22A416)。

摘　　要：针对传统高光谱成像技术在农产品品质无损检测中信息表征不全、光谱反射率分布受形貌影响的问题,基于结构光成像原理和深度学习技术,提出一种易操作、速度快的样本三维形貌自动化重建算法及其与光谱分布数据匹配融合的方法,搭建了相应的检测装置。对被测物的三维表面形貌,基于单目相机条纹成像原理,通过语义分割网络模型输出的像素语义信息来映射表面高度信息;对被测物物理形貌信息与生化成分信息的匹配融合,基于标准参考物体的线特征拟合对两异源图像进行配准和评估;利用所搭建的检测装置对番茄果实进行了试验。对直径4~8 cm的样本,所训练的网络模型可在0.75 s内预测出其三维高度分布,直径和最大高度误差均在4%以内;使用边缘提取算法、曲线拟合算法、线特征融合方法实现了三维映射与高光谱图像的实时配准融合。本文研究可为克服单一类型图像评估指标不足提供参考,为农产品无损可视化检测提供更丰富的评价数据。Tomatoes are highly nutritious and popular worldwide as both a vegetable and a fruit.With the improvement of consumers′requirements for food quality and the access standards of tomato products in various countries,tomato quality control has received more and more attention.Traditional manual sorting operations are time-consuming,laborious and inefficient,so it is necessary to develop fast and accurate inspection technology.With the development of machine vision and neural network technologies,automated agricultural product defect detection methods have been widely studied and applied,providing new ideas and methods for agricultural product quality inspection.However,tomatoes and other agricultural products are affected by genetic factors and growth environment,and their external three�dimensional geometric forms and internal physiological information are complex and different,and a single type of detection method can only detect specific defects,so more dimensional detection methods are needed to detect the quality of tomatoes.In view of the needs of automatic detection of tomato quality and the shortcomings of traditional hyperspectral imaging technology in the non-destructive testing of agricultural products,such as incomplete information characterization and spectral reflectance distribution affected by morphology,this work adopts the idea of fusion detection of structured light imaging and hyperspectral imaging,and designs and builds a detection device that can realize the non-destructive diagnosis of the three-dimensional morphology of the appearance of the sample and the internal physiological information in the same imaging room.Based on the deep learning technology to obtain the three-dimensional topography of the sample from the projection fringes,a data fusion algorithm with simple operation and low computational cost was designed to register the heterologous images collected by different sensors,and the multi-dimensional information of the appearance and internal physiological state of the sample w

关 键 词：高光谱成像 形貌重建 深度学习 图像融合 多维信息表征 

分 类 号：Q631[生物学—生物物理学]