YOLO-SAM AgriScan:一个统一的框架,用于成熟的草检测和细分,使用Few-Shot和零射击学习
Partho Ghose1, Al Bashir1, Yibin Wang1
1Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究介绍了YOLO-SAM AgriScan,这是一个新的框架,用于高效的草细分. 它将少量射击学习与零射击细分相结合,减少农业应用程序的手动注释需求.
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:
- 传统的图像细分方法用于农业是缓慢的,需要大量的手动注释.
- 这种劳动密集型的过程限制了自动农业监测系统的可扩展性.
研究的目的:
- 开发一个高效和可扩展的框架,用于在工厂成熟的草细分.
- 克服农业计算机视觉任务中手动注释的局限性.
主要方法:
- 一个混合框架,YOLO-SAM AgriScan,整合了YOLOv11用于少数镜头对象检测和Segment Anything Model 2 (SAM2)用于零镜头细分.
- YOLOv11使用最小的注释样本进行了微调,而SAM2则在没有进一步监督的情况下生成了口罩.
- 该系统在定制和公共数据集上进行了评估,在完整数据和数据受限制的场景中进行了评估.
主要成果:
- 该框架实现了高性能,在定制数据集上,平均子得分为0.95和IOU为0.93.
- 在公开数据上保持了竞争性结果 (Dice: 0.95, IoU: 0.92).
- 在各种农业环境中证明了稳定性,通用性和实际相关性.
结论:
- 结合少数射击检测和零射击细分,为农业表型化提供了有效的注释光方法.
- YOLO-SAM AgriScan能够实现可扩展和高效的草细分,加速智能农业系统.
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