农机化研究2025，Vol.47Issue(7)：18-24,34,8.DOI:10.13427/j.issn.1003-188X.2025.07.003
温室复杂环境下茄子及果梗快速检测方法
Rapid Detection of Eggplant and Fruit Stem in Complex Greenhouse Environment
摘要
Abstract
In order to meet the fast and accurate detection of eggplant and fruit stems under different illumination and shading environments of greenhouse by eggplant picking robot,this study proposed a lightweight YOLOv5s eggplant detec-tion model.Firstly,MobileNetV3 was used to replace the YOLOv5s feature extraction network,and then the lightweight CBAM attention mechanism was embedded into the MobileNetV3 backbone network to enhance the feature extraction capa-bility and reduce the number of parameters of the model while ensuring with good accuracy.The final experiment deter-mined the use of WIoU instead of CIoU as the boundary regression loss function.The improved YOLOv5s algorithm model was tested based on the homemade eggplant dataset under different illumination.The test results showed that the improved model reduced the amount of parameters by 49.7%and the computation by 61%compared with the original model,and the average detection accuracy of eggplant and fruit stems under different illumination conditions using this model was 95.2%,which was 1.2%better compared with the original model.The detection speed was 55.6 frames/s under GPU and 10.4 frames/s under CPU,which showed that the improved algorithm can meet the requirements of eggplant picking robot for real-time detection of eggplant picking.关键词
茄子/果梗/YOLOv5s/轻量化/光照变化Key words
eggplant/fruit stem/YOLOv5s/lightweight/illumination variation分类
农业工程引用本文复制引用
魏喜安,白龙,闫涛,郭嘉褀..温室复杂环境下茄子及果梗快速检测方法[J].农机化研究,2025,47(7):18-24,34,8.基金项目
国家自然科学基金项目(11802035) （11802035）
北京市科技计划一般项目(KM201911232022) （KM201911232022）
北京信息科技大学"勤信英才"项目(5112111110) （5112111110）
