葡萄UL-YOLO:双向交叉尺度融合与圆,用于在果园中强大的葡萄检测
Xiuli Zhu1, Zhenghong Yu1, Chengwei Li1
1School of Robotics, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China.
Frontiers in plant science
|January 19, 2026
概括
这项研究介绍了Grapevine超轻量级YOLO (GrapeUL-YOLO),这是一个用于果园中准确实时葡萄检测的新模型. 它实现了自动收获的高精度和效率,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 农业机器人农业机器人
- 机器学习 机器学习
背景情况:
- 自动采摘葡萄需要在具有挑战性的果园条件下精确检测果实.
- 复杂的背景,可变的照明和水果封闭阻碍了当前的检测模型.
研究的目的:
- 开发一个强大的实时葡萄检测模型,用于果园的自动收获.
- 为了提高检测准确性和效率,尽管环境复杂.
主要方法:
- 基于YOLOv11.1的葡萄藤超轻量级YOLO (葡萄UL-YOLO) 的建议.
- 引入跨尺度剩余特征骨干 (CSRB) 以实现高效的多尺度特征提取.
- 开发了自适应双向融合网络 (ABFN),用于增强具有密集阻塞的特征融合.
- 设计了一个可适应形状的检测头,使用圆形框.
主要成果:
- 在Embrapa WGISD数据集上,GrapeUL-YOLO实现了0.912的mAP@0.5和0.576的mAP@0.5:0.95.
- 超过了包括CenterNet和YOLOv11在内的9个主流模型的表现.
- 模型具有5.11M参数,每个图像的平均检测时间为16.9ms.
结论:
- 葡萄UL-YOLO为自动葡萄检测提供了高精度和轻量级设计的平衡.
- 为果园环境中的实时应用提供高效的解决方案.
- 在处理复杂的果园场景进行收获方面表现出显著的改进.
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