您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

CabbageNet: Deep Learning for High-Precision Cabbage Segmentation in Complex Settings for Autonomous Harvesting Robotics

文献类型： 外文期刊

作者： Tian, Yongqiang 1 ; Cao, Xinyu 1 ; Zhang, Taihong 1 ; Wu, Huarui 4 ; Zhao, Chunjiang 4 ; Zhao, Yunjie 1 ;

作者机构： 1.Xinjiang Agr Univ, Sch Comp & Informat Engn, Urumqi 830052, Peoples R China

2.Minist Educ, Engn Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China

3.Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100125, Peoples R China

5.Minist Agr & Rural Affairs, Key Lab Digital Village Technol, Beijing 100125, Peoples R China

关键词： cabbage; deep learning; instance segmentation; automatic harvesting; intelligent agriculture

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 24 卷 24 期

页码：

收录情况： SCI

摘要： Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation. This study proposes an improved YOLOv8n-seg network to address these challenges effectively. Key improvements include modifying the baseline model's final C2f module and integrating deformable attention with dynamic sampling points to enhance segmentation performance. Additionally, an ADown module minimizes detail loss from excessive downsampling by using depthwise separable convolutions to reduce parameter count and computational load. To improve the detection of small cabbage heads, a Small Object Enhance Pyramid based on the PAFPN architecture is introduced, significantly boosting performance for small targets. The experimental results show that the proposed model achieves a Mask Precision of 92.2%, Mask Recall of 87.2%, and Mask mAP50 of 95.1%, while maintaining a compact model size of only 6.46 MB. These metrics indicate superior accuracy and efficiency over mainstream instance segmentation models, facilitating real-time, precise cabbage harvesting in complex environments.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台