智能化农业装备学报（中英文）2025，Vol.6Issue(3)：98-110,13.DOI:10.12398/j.issn.2096-7217.2025.03.010
基于改进YOLOv5s的云机器人视觉导航方法
Visual navigation method based on improved YOLOv5s and cloud robot
摘要
Abstract
In order to solve the autonomous navigation accuracy,real-time performance,computing power and other requirements of pigeon breeding robot,this paper transfers the computing power demand of the robot ontology to the cloud server,and proposes a visual navigation method for cloud robot based on improved YOLOv5s.Firstly,Ghost-Shuffle Conv(GS Conv)is used to replace the traditional convolutional layer of the backbone network and the neck network on the basis of YOLOv5s,and the redundant network layer of the backbone network is streamlined.Secondly,Efficient Channel Attention(ECA)is introduced into the Spatial Pyramid Pooling-Fast(SPPF)module,and the fusion of Ghost Bottleneck and Efficient Channel Attention(ECA)is used to replace the C3 module in the neck network,so as to reduce the number of parameters and calculation,realize the lightweight of the network,and improve the detection ability of small targets.The model training results show that compared with the original YOLOv5s model,the total number of parameters of the improved model is reduced by 75.57%,the model size is only 3.7 MB,the accuracy rate P,the mean accuracy mAP and the recall rate R are increased by 2.60,2.59 and 2.62 percentage points respectively,and the detection speed is 51 frames/s,that is,7.2 ms is reduced.The model is deployed to the cloud server,and the image transmission speed is effectively improved by compressing the image resolution and reducing the model parameters,and the computing power demand of the cloud robot itself is reduced.The visual navigation test results under different illumination and different speed conditions in the pigeon farm show that the average value of the maximum lateral deviation of the navigation algorithm of the improved model is 5.281 cm,the average value of the absolute lateral deviation is not more than 1.474 cm,the average value of the maximum heading deviation is 5.455 °,and the average value of the absolute heading deviation is not more than 1.897 °.It can be seen that the improved model proposed in this study is used for the visual navigation of the pigeon farming robot,which has the characteristics of high accuracy and fast speed,and can provide a technical reference for the intelligent production of the farm.关键词
肉鸽养殖/改进YOLOv5s/视觉导航/云机器人/室内导航/云计算Key words
meat pigeon breeding/improved YOLOv5s/visual navigation/cloud robot/indoor navigation/cloud computing分类
信息技术与安全科学引用本文复制引用
陈家政,付根平,黄伟锋,胡宏男,张世昂,朱立学..基于改进YOLOv5s的云机器人视觉导航方法[J].智能化农业装备学报（中英文）,2025,6(3):98-110,13.基金项目
广州市科技计划项目(2023B03J0862) （2023B03J0862）
岭南现代农业科学与技术广东省实验室科研项目(NZ2021038) （NZ2021038）
国家自然科学基金(32472015) Guangzhou Science and Technology Plan Project(2023B03J0862) （32472015）
Lingnan Modern Agricultural Science and Technology Guangdong Provincial Laboratory Research Project(NZ2021038) （NZ2021038）
National Natural Science Foundation of China General Project(32472015) （32472015）
