您好，欢迎访问浙江省农业科学院 机构知识库！
浙江省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny Model

文献类型： 外文期刊

作者： Chen, Zhenlei 1 ; Qian, Mengbo 1 ; Zhang, Xiaobin 1 ; Zhu, Jianxi 2 ;

作者机构： 1.Zhejiang A&F Univ, Coll Opt Mech & Elect Engn, Hangzhou 311300, Peoples R China

2.Zhejiang Acad Agr Machinery, Jinhua 321051, Peoples R China

关键词： Chinese bayberry detection; YOLOv7-tiny; attention mechanism; partial convolution; target recognition

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.

  • 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：浙江省农业科学院

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