SSViT-YOLOv11:用于咖啡果成熟度检测的融合轻量级YOLO和ViT
Yifan Liu1, Qiudong Yu1, Shuze Geng1
1College of Information Technology engineering, Tianjin University of Technology and Education, Tianjin, China.
Frontiers in plant science
|December 17, 2025
概括
一个新的AI模型,SSViT-YOLOv11,准确地检测咖啡果的成熟度,改善收获时间和咖啡豆质量. 这种自动化视觉检测系统为农民提供了高精度和速度.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 手动评估咖啡果的成熟度是主观和低效的.
- 自动视觉检测受到阻塞,照明和小目标的挑战.
研究的目的:
- 开发一个改进的咖啡果成熟度识别自动化系统.
- 为了提高视觉检测模型的准确性,速度和模型紧性.
主要方法:
- 拟议的SSViT-YOLOv11框架整合了单级视觉变压器 (SSViT) 和YOLOv11n.
- 集成的任意内核卷积 (AKConv) 和多尺度卷积注意力 (MSCA).
- 精细的多尺度特征融合,用于更好的上下文和小物体检测.
主要成果:
- 实现了81.1%的精度,77.4%的回忆率和84.54%的mAP@50.
- 在23 FPS运行,只有216万个参数.
- 在准确性,速度和模型大小方面表现出卓越的性能.
结论:
- SSViT-YOLOv11提供了准确性,速度和紧性的有效平衡.
- 该模型适用于协助农民评估咖啡果的成熟度.
- 自动视觉检测显示了农业应用的重大前景.
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