改进了基于YOLO v5s的土豆外部缺陷检测方法.
XiLong Li1, FeiYun Wang1, Yalin Guo1
1Chinese Academy of Agricultural Mechanization Sciences Croup Co., Ltd., Beijing, China.
Frontiers in plant science
|March 5, 2025
概括
这项研究使用改进的YOLO v5s模型增强了土豆缺陷检测,为自动分类系统实现了更高的准确性. 这种先进的模型为农业应用提供了更高效,更可靠的解决方案.
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:
- 手动的土豆缺陷分类是低效和有偏见的.
- 自动化系统需要高精度和速度,这给资源带来了挑战.
- 实时发现土豆缺陷对于农业效率至关重要.
研究的目的:
- 开发一个增强的YOLO v5s模型 (YOLO v5s-ours) 以实时检测土豆缺陷.
- 为了提高自动排序的检测精度和计算效率.
- 解决手动分类和现有的自动化系统的局限性.
主要方法:
- 在YOLO v5s.中集成了坐标注意力 (CA),自适应空间特征融合 (ASFF) 和心脏空间金字塔聚合 (ASPP) 模块.
- 开发一个专门的模型 (YOLO v5s-ours) 用于实时缺陷识别.
- 在六个缺陷类别中评估模型性能:健康,绿化,发芽,,机械损伤和腐烂.
主要成果:
- 我们的YOLO v5s-ours模型实现了82.0%的精度,86.6%的回忆,84.3%的F1-Score和85.1%的平均精度.
- 与基线模型相比,准确度显著提高24.6% (精度),10.5% (回忆),19.4% (F1-Score) 和13.7% (mAP).
- 保持了计算效率,率为30.7fps,尽管内存使用量略有增加.
结论:
- 增强的YOLO v5s-ours模型显著提高了实时土豆缺陷检测的准确性.
- 该模型为开发高效的自动化土豆分类系统提供了可行的解决方案.
- 这项研究通过克服传统分拣方法的局限性,推进了农业技术.
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