AS-YOLO:使用幽灵瓶和全球注意力机制来进行果茎细分的增强YOLO
Na Rae Baek1, Yeongwook Lee1, Dong-Hee Noh1
1IT Application Research Center, Jeonbuk Regional Branch, Korea Electronics Technologies Institute (KETI), Jeon-Ju 54853, Republic of Korea.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究介绍了AS-YOLO,这是一个先进的AI模型,用于在水果收获中检测果茎. 它通过实时准确细分茎来显著改进自动化系统.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 摘除茎是水果收获中的劳动密集型瓶,影响水果的质量和可销售性.
- 自动化水果收获需要准确和快速的水果和茎细分.
研究的目的:
- 开发一个改进的You Only Look Once (YOLO) 模型,命名为AppleStem (AS) -YOLO,用于精确的果茎细分.
- 为了提高实时应用程序的计算效率和特征提取.
主要方法:
- AS-YOLO模型整合了一个幽灵瓶,以减少参数和计算效率.
- 采用全球关注机制来增强特征提取能力.
- 该模型在定制构建和开放数据集上进行了评估.
主要成果:
- AS-YOLO实现了高精度,平均平均精度 (mAP) @50为0.956和mAP@50-95为0.782.
- 该模型显示实时推断速度为每秒129.8 (FPS).
- AS-YOLO的表现优于现有的最先进的细分模型.
结论:
- 该AS-YOLO模型显示了实时应用在自动水果收获中的巨大潜力.
- 这一进步通过解决一个关键的收获挑战,为更广泛的农业自动化领域做出了贡献.
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