食品与机械2026，Vol.42Issue(2)：126-132,7.DOI:10.13652/j.spjx.1003.5788.2025.80514
基于改进YOLOv11n模型的香蕉成熟度识别方法
Recognition for banana ripeness based on improved YOLOv11n
摘要
Abstract
[Objective]To improve the efficiency of recognition for banana ripeness.[Methods]A method for recognizing banana ripeness is developed based on improved YOLOv11n.A modified polarized self-attention mechanism is introduced into YOLOv11n to enhance the feature extraction capability of the backbone network across various banana distribution scenarios.The original upsampling is replaced with a module of content-aware reassembly of features,which enlarges the receptive field to more effectively aggregate contextual information.Scylla intersection over union(SIoU)is adopted as the new bounding box loss,which calculates the vector angle between ground truth and predicted boxes to better address the matching problem between them and reduce instances of missed and false detection.[Results]The improved method achieves increases of 1.4%and 3.0%in mean Average Precision 0.50(mAP0.50)and mean Average Precision 0.50～0.95(mAP0.50～0.95),respectively,with the recognition accuracy surpassing other existing methods.[Conclusion]The proposed method effectively enhances the accuracy and efficiency of recognition for banana ripeness,demonstrating high practical value.关键词
香蕉成熟度/YOLOv11n模型/极化自注意力/内容感知的特征重组/斯库拉交并比Key words
banana ripeness/YOLOv11n/polarized self-attention/content-aware reassembly of features/scylla intersection over union引用本文复制引用
胡俐蕊,王佳星,胡泽坤..基于改进YOLOv11n模型的香蕉成熟度识别方法[J].食品与机械,2026,42(2):126-132,7.基金项目
广西重点研发计划项目(编号:桂科AB25069378) （编号:桂科AB25069378）
钦州市科技计划项目(编号:202116602) （编号:202116602）
