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ITD-YOLO:一种改进的YOLO模型,用于检测高级绿茶中的杂质
Zezhong Ding1,2, Yanfang Li3, Bin Hu2
1Tea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Foods (Basel, Switzerland)
|May 14, 2025
概括
一个新的轻量级算法有效地检测和分类优质绿茶中的杂质,显著提高分类效率并减少劳动强度. 这种先进的方法通过自动杂质分类来提高茶叶的质量.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 食品科学 食品科学 食品科学
背景情况:
- 手动分类茶中的杂质是劳动密集型,低效和昂贵的.
- 现有的自动分类模型缺乏硬件性能和适合工业部署的适用性.
- 提高优质绿茶的质量需要有效和精确的杂质去除.
研究的目的:
- 开发一种轻量级的算法,用于检测和分类优质绿茶中的杂质.
- 提高分拣效率,减少茶叶加工中的劳动强度.
- 为茶叶中复杂的杂质分类提供技术基础.
主要方法:
- 为培训和评估,创建了一个包含四个杂质类别的定制数据集.
- 评估了各种YOLOv8模型,并选择YOLOv8n作为基础模型.
- 选择了Focaler_mpdiou损失函数,并将其应用于YOLOv8m作为教师模型.
- 采用模型修剪和知识蒸来创建一个轻量级但准确的模型.
主要成果:
- 优化的轻量级模型在精度 (P),回忆 (R) 和平均精度 (mAP) 中取得了改进.
- 实现了每秒 (FPS) 的显著增加,表明了更高的处理速度.
- 该模型实现了计算复杂度降低 (GFLOPs) 和参数减少,使其适合部署.
- 该模型在黑茶样本上显示了概括能力.
结论:
- 拟议的轻量级算法为茶叶中的杂质自动检测和分类提供了可行的解决方案.
- 开发的模型提高了分拣效率,减少了茶叶行业的手工劳动.
- 这项研究有助于推进茶叶加工中的自动化质量控制.
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