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YOLO-LitchiVar:一种轻量级,高精度的检测模型,用于识别细粒度litchi品种

Bing Xu¹, Xianjun Wu¹, Xueping Su¹

¹School of Computer, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.

Frontiers in plant science

|February 16, 2026

概括

这项研究介绍了YOLO-LitchiVar,这是一种轻量级的人工智能模型,用于准确地分类果品种. 该模型提高了识别李子类型的效率和精度,为自动水果分类提供了实用解决方案.

科学领域:

计算机视觉计算机视觉
农业技术农业技术
机器学习机器学习

背景情况:

手动的利奇品种分类是低效的,主观的,缺乏标准化.
准确的识别litchi品种对于收获后管理和质量控制至关重要.

研究的目的:

开发一种轻量级,高精度的深度学习模型,用于细粒度litchi品种的分类.
提高自动化litchi识别系统的效率和准确性.

主要方法:

构建了一个12种常见的litchi品种的图像数据集.
提出了YOLO-LitchiVar,这是一个基于YOLOv12的模型,包含DSC3k2,C2PSA和ECA注意力模块.
优化模型复杂性,特征表示和区分性能.

主要成果:

YOLO-LitchiVar获得了94.4%的mAP50-95,超过了基线YOLOv12的0.8%.
与标准YOLOv12.12相比,模型参数减少了14.1%,计算成本减少了13.8%.
显著提高了具有挑战性的品种的召回率,并减少了类似类型之间的错误分类率.

结论:

YOLO-LitchiVar为智能litchi品种识别提供了一个高效和准确的解决方案.
该模型的轻量级设计使其适合在农业应用中移动部署.
这项研究推进了全球适用的自动化水果分类技术.

关键词:

轻量级的模型轻量级的模型这就是YOLOv12的原因.跨层次的功能融合融合.利奇的品种分类的分类.多个尺度的特征对齐调整.

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