Fig-YOLO:一个改进的基于YOLOv11的Fig检测算法,用于复杂的环境
Zhihao Liang1, Ruoyu Di1, Fei Tan1
1College of Information Science and Technology, Shihezi University, Shihezi 832003, China.
Foods (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了Fig-YOLO,这是一个先进的AI算法,用于在具有挑战性的果园条件下精确检测无花果. 它通过克服小目标和遮等问题,显著提高了智能收获的准确性.
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:
- 精确的无花果检测对于智能收获至关重要,但受到小目标,遮蔽和类似背景的阻碍.
- 现有的方法难以应对现实世界果园环境的复杂性.
研究的目的:
- 开发一个改进的基于YOLOv11n的算法,命名为Fig-YOLO,用于增强的无花果检测.
- 解决包括小物体大小,遮蔽和背景相似性在内的关键挑战.
主要方法:
- 在骨干中引入空间频率选择性卷积 (SFSConv),用于联合空间和频率特征建模.
- 纳入了增强的双部门关注机制 (EBAM),以改善关键地区的代表性.
- 使用多分支动态采样卷积 (MFCV) 模块捕获不同尺寸和特征融合的无花果.
主要成果:
- Fig-YOLO实现了高性能指标:89.2%的精度,78.4%的回忆率和87.3%的mAP@0.5.5.
- 与基线YOLOv11n算法相比,显示了显著的改进.
- 在各种条件下保持稳定的性能 (果实大小,遮蔽,照明,数据源).
结论:
- Fig-YOLO有效地克服了用于智能收获的无花果检测的主要障碍.
- 提出的建筑创新提高了复杂环境中的稳定性和准确性.
- 这种算法为自动化果园监测和收获系统提供了强有力的支持.
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