基于改进YOLOv4的苹果采摘机器人树枝障碍物深度识别
Depth recognition of branch obstacles of apple picking robot based on improved YOLOv4
黄哲 1唐仕喜 2沈冠东 3高心悦 4王仕廉2
作者信息
- 1. 南京工业大学机械与动力工程学院,南京 211816;盐城师范学院,美术与设计学院,江苏 盐城 224000
- 2. 盐城师范学院,信息工程学院,江苏 盐城 224000
- 3. 盐城师范学院,美术与设计学院,江苏 盐城 224000
- 4. 南京工业大学机械与动力工程学院,南京 211816
- 折叠
摘要
为识别特征不明显的树枝,尤其是机械手进行苹果采摘时遮挡住苹果位置的树枝,提出了一种结合语义分割和YOLOv4来获取树枝语义骨架,以及识别出树枝位置框的方法.采用语义分割划分树枝矩形包络的方法,剔除影响树枝识别效果的小树枝和分支,再用labelImg和labelme工具对数据集进行标注;对训练的网络模型添加了3层最大池化层,并在回归损失方面对YOLOv4的CIOU回归损失函数进行了改进,提出了针对复杂特征、适范围提高预测准确率的置信度相关函数BIOU.结果表明,改进的YOLOv4网络模型训练遮挡苹果位置树枝的F1和AP分别比原始网络训练全部树枝高出20.00个百分点和23.36个百分点,获得训练效果更好的数据集和树枝识别网络.
Abstract
In computer vision,it was difficult to train and recognize objects with unclear features,and improve the detection in many fields.In order to identify the branches with less obvious characteristics,mainly the branches that covered the apple position when the manipulator picked the apple,a method to obtain the branch semantic skeleton and identify the branch position box by combining se-mantic segmentation and YOLOv4 algorithm was proposed.Before using the data set for training,the method of semantic segmentation to divide the rectangular envelope of branches,eliminate the small branches and branches that affected the effect of branch recogni-tion,and then label the data set with labelimg and labelme tools was used;Three layers of maximum pooling layer were added to the trained network model,and the CIOU of YOLOv4 was improved in terms of regression loss.A confidence correlation function BIOU was proposed to improve the prediction accuracy according to the complex characteristics and suitable range.The final experiment showed that the F1 and AP of the tree branches with occluded apple positions trained by the improved YOLOv4 network model were 20.00 precentage and 23.36 precentage higher than those of all the trees trained by the original network.
关键词
树枝识别/YOLOv4/语义分割/数据集训练/BIOU边框回归损失函数Key words
branch recognition/YOLOv4/semantic segmentation/dataset training/BIOU frame regression loss function引用本文复制引用
基金项目
江苏省重点研发计划(产业前瞻与关键核心技术)重点项目(BE2021016)
出版年
2024
NETL
NSTL
万方数据