农机化研究2024，Vol.46Issue(8)：80-84,5.
适应遮挡条件下奶油生菜的实例分割方法研究
Research on Instance Segmentation Method of Butter Lettuce Under Adaptive Occlusion Conditions
摘要
Abstract
Using machine vision technology to measure the phenotypic parameters of lettuce is of great significance to ex-plore the growth law of lettuce.The construction of lettuce individual identification and outer contour segmentation algo-rithms is an important prerequisite for accurate measurement of phenotypic parameters,but when lettuce is cultivated to harvest In the top view,the leaves of the lettuce individuals overlap and block each other,which greatly hinders the indi-vidual identification and outer contour segmentation of lettuce.In response to the above problems,this paper improves the Mask R-CNN neural network model,the mask branch adopts the class-agnostic mode,and the original convolution backbone is replaced by ResNeXt50 combined with FPN,which realizes the individual recognition and outer contour seg-mentation of butter lettuce under occlusion conditions.In order to verify and analyze the segmentation accuracy of the im-proved model,this paper uses the average accuracy AP75 and the average detection time as the evaluation indicators,and sets up comparative experiments with the original Mask R-CNN,DeepMask,and MNC segmentation models on dif-ferent degrees of occlusion test sets.The results show that the average accuracy of the improved model reaches 98.7%,which is about 4%higher than the original model,and it can still maintain good segmentation accuracy on the heavily oc-cluded test set.This study can provide an algorithm reference for the identification and segmentation of plant leaves under shading conditions,and also provide technical support for the extraction of phenotypic parameters of butter lettuce.关键词
奶油生菜/轮廓分割/遮挡/Mask R-CNN/深度学习/图像处理Key words
butter lettuce/outer contour segmentation/occlusion/Mask R-CNN/deep learning/image procession分类
农业科技引用本文复制引用
韩江枫,杨意,郑鸿燊,刘厚诚,琚俊,辜松..适应遮挡条件下奶油生菜的实例分割方法研究[J].农机化研究,2024,46(8):80-84,5.基金项目
国家重点研发计划项目(2021YFD2000703) （2021YFD2000703）
广东省现代农业产业共性关键技术研发创新团队建设项目(2022KJ131) （2022KJ131）
