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安吉白茶的新鲜叶子的识别:S-YOLOv10-ASI算法融合了非对称特征的PYRA-MID网络
Chunhua Yang1,2, Wenxia Yuan2, Qiang Zhao1
1College of Mechanical and Electrical Engineering, Wuhan Donghu College, Wuhan, China.
PloS one
|July 2, 2025
概括
通过改进远距离和小目标识别,S-YOLOv10-ASI算法增强了机器人茶叶收获. 这种新的方法显著提高了准确性,并为自动茶叶采摘机器人奠定了基础.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于小目标和远距离检测,机器人准确识别和收获茶叶仍然具有挑战性.
- 现有的物体检测模型经常与复杂的农业环境和不同茶叶植物结构相扎.
研究的目的:
- 开发一个先进的算法,S-YOLOv10-ASI,用于精确的茶叶识别和收获.
- 提高机器人系统在农业应用中的能力,特别是用于茶叶采摘.
主要方法:
- 将切片辅助超级推理技术与YOLOv10网络集成.
- 使用空间到深度卷积和渐进特征金字塔网络修改YOLOv10网络.
- 优化损失函数的计算,使用交叉点在欧盟 (IoU).
主要成果:
- 与YOLOv10相比,S-YOLOv10-ASI表现出显著的改善,边界框回归损失减少了30%以上 (培训),分类损失下降了60%以上 (验证).
- 精度,回忆和mAP分别增加了7.1%,6.69%和6.78%.
- 不同茶叶芽阶段的AP值 (单个芽,一个芽/一片叶子,一个芽/两片叶子) 提高了6.10%至8.28%.
结论:
- 该S-YOLOv10-ASI算法有效地解决了长距离检测的挑战,低分辨率的小目标茶.
- 改进后的模型实现了高精度和回忆,这对于开发安吉白茶采摘机器人至关重要.
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