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目前针对核桃测产的方法大多停留在利用传统的统计学模型上，其准确率几乎无法保证。因此，以青皮核桃为例，建立无人机航拍视角下的核桃图像数据集，首次将coordinate attention(CA)机制嵌入YOLOv8模型中，利用改进后的YOLOv8-CA模型算法对青皮核桃进行目标检测。实验结果表明，改进后的新模型YOLOv8-CA与原始YOLOv8和YOLOv5相比，在mAP值上分别提高了0.004和0.051,在Recall值上分别提高了0.019和0.089。
Abstract：Current methods of walnut yield measurement mainly relied on traditional statistical models, and the accuracy could hardly be guaranteed. Therefore, taking green walnuts as an example, an image dataset of walnuts from the perspective of drone aerial photography was established. The coordinate attention(CA) was innovatively applied to the YOLOv8 model for the first time. The improved YOLOv8-CA model algorithm was used for object detection of green walnuts. The experimental results showed that the newly improved model(YOLOv8-CA), improved the mAP value by 0.004 and 0.051, and the Recall value by 0.019 and 0.089 compared with the original YOLOv8 and YOLOv5, respectively.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023256
中图分类号:S664.1;TP391.41
引用信息:
[1]钟天泽,云利军,杨璇玺,等.基于改进YOLOv8的无人机视角下青皮核桃目标检测[J].郑州大学学报(理学版),2025,57(05):24-30.DOI:10.13705/j.issn.1671-6841.2023256.
基金信息:
云南省教育厅科学研究基金项目(2023Y0533); 云南省基础研究计划重点项目(202401AS070034)
2024-05-21
2024-05-21
2024-05-21