基于Mask R-CNN的复杂环境下辣椒识别方法研究
Research on pepper recognition method in complex environment based on Mask R-CNN
付晓鸽 1李涵 1左治江 1杜铮2
作者信息
- 1. 江汉大学精细爆破国家重点实验室,武汉市,430056;爆破工程湖北省重点实验室,武汉市,430056
- 2. 武汉市农业科学院农业机械化研究所,武汉市,430207
- 折叠
摘要
针对辣椒采摘机器人在真实场景中辣椒簇状、粘连和光照不均导致无法精准采摘辣椒的问题,提出一种基于Mask R-CNN实例分割网络模型的辣椒识别方法.以真实场景下的辣椒为研究对象,采集自然生长的辣椒图像4 496张,对其中的4 000张进行数据标注作为数据集,通过设置不同的学习率、训练周期和模型网络层对数据集进行训练.试验结果表明,Mask R-CNN网络模型对真实场景下辣椒的识别和分割效果较好,平均准确率达到 90.34%,平均速度达到0.82 s/幅,为智能辣椒采摘机器人的辣椒分割识别和定位提供有力的技术支撑.
Abstract
In order to solve the problem that pepper picking robots can not pick pepper accurately in real scenes due to pepper clusters,adhesion and uneven lighting,a pepper recognition method based on Mask R-CNN instance segmentation network model is proposed.With pepper in the real scene as the research object,4 496 images of naturally growing pepper were collected,and 4 000 of them were labeled as data sets.The data sets were trained by setting different learning rates,training cycles and model network layers.The experimental results show that the Mask R-CNN network model has a good effect on pepper recognition and segmentation in the real scene,with an average accuracy of 90.34%and an average speed of 0.82 s/frame,providing a strong technical support for pepper segmentation recognition and location of intelligent pepper picking robot.
关键词
辣椒识别/实例分割/Mask/R-CNN/神经网络/采摘机器人Key words
pepper recognition/instance segmentation/Mask R-CNN/neural networks/picking robot引用本文复制引用
基金项目
湖北省教育厅百校联百县—高校服务乡村振兴科技支撑行动计划(BXLBX0369)
武汉市知识创新专项曙光计划项目(2022010801020378)
出版年
2024
国家科技期刊平台
NSTL
万方数据