农机化研究2025，Vol.47Issue(4)：186-191,6.DOI:10.13427/j.issn.1003-188X.2025.04.027
深度学习方法在红花采摘机器人中的应用
Application of Deep Learning Method in Safflower Picking Robot
摘要
Abstract
In order to realize rapid and accurate recognition of flesh safflower in complex agricultural environment,a new method based on improved YOLOv5s was proposed.Based on YOLOv5s,a GPU-adapted lightweight Ghost module is in-tegrated to obtain a baseline model with lower complexity and faster network reasoning speed.CBAM attention mechanism is embedded into the baseline model to improve the performance of small objects in high frequency features.A Focal-EIoU loss function based on border width and height difference was established to improve the recognition rate of safflower under different occlusion conditions.Finally,experiments on a parallel safflower picking robot are carried out to verify the feasibility and reliability of the improved algorithm.The experimental results show that the mAP value of the improved Yolov5s model is improved by 1.94 percentage points compared with the original model.The parameters of the model and the detection speed of a single image are 3.52 MB and 0.06 s/amplitude respectively,the recognition success rate of robot vision system for picking safflower can reach 89.92%.关键词
红花/采摘机器人/深度学习/YOLOv5s/识别成功率Key words
safflower/picking robot/deep learning/YOLOv5s/recognition success of rate分类
农业科技引用本文复制引用
陈金荣,许燕,周建平,王小荣,崔超..深度学习方法在红花采摘机器人中的应用[J].农机化研究,2025,47(4):186-191,6.基金项目
新疆维吾尔自治区创新团队项目(2022D14002) （2022D14002）
新疆农机研发制造推广应用一体化项目(YTHSD2022-05) （YTHSD2022-05）
